International Journal Of Nonlinear Analysis And Applications
Volume:13 Issue: 1, Winter-Spring 2022

In this paper, utilizing the technique of Petryshyn’s fixed point theorem in Banach algebra, we analyze the existence of solution for functional integral equations, which includes as special cases many functional integral equations that arise in various branches of non-linear analysis and its application. Finally, we introduce the numerical method formed by modified homotopy perturbation
approach to resolving the problem with acceptable accuracy.

A model for transmission dynamics of cholera infection between human host and environment is developed. We incorporate the proportion of infectious individuals who do not comply with treatment into the human population. Stability analysis, as well as simulation of the model, is done. The results from the stability analysis show that the disease-free equilibrium solution is locally asymptotically stable if R0 < 1 while the endemic equilibrium solution is globally asymptotically stable when R0 > 1. The technical tool used for our analysis is the theory of competitive systems, compound matrices and stability of periodic orbits. Finally, we investigate, numerically, the influence of seasonal variation on the control of cholera.

This work is devoted to studying the existence of solutions to systems of $p$-Laplacian type. We prove the existence of at least one weak solution, under some assumptions, by applying Galerkin's approximation and the theory of Young measures.

This manuscript is devoted to establishing Hyers–Ulam stability for a class of non-linear impulsive coupled sequential fractional differential equations with multi point boundary conditions on a closed interval [0,T] with Caputo fractional derivative having non-instantaneous impulses.
Sufficient conditions are introduced that guarantee the existence of a unique solution to the proposed problem. Furthermore, Hyers–Ulam stability of the proposed model is also presented and an example is provided to authenticate the theoretical results.

‎It is generalized the subgradient extragradient algorithm from linear spaces to nonlinear cases‎. ‎This algorithm introduces a method for solving equilibrium problems on Hadamard manifolds‎. ‎The global convergence of the algorithm is presented for pseudo-monotone and Lipschitz-type continuous bifunctions‎.

The main goal of this paper is investigating some types of topological groupoid and their action which denoted by LM- groupoid and M- groupoid. Some properties of these groupoid are written as proposition. We concentrated to research the relation of M- groupoid and LM- groupoid.

The aim of this research paper is to present and study a new class of generalized closed sets in double fuzzy topological spaces, by using the fuzzy - αm</sup> closed set, that was previously presented. This new class is called the fuzzy αm</sup> - g∗</sup>fuzzy closed set. The relationship of the new concept with previous concepts is studied and the characteristics of this concept is investigated through important theorems that determine the position of this set in relation to sets that have been studied or that will be presented later. Also, generalizations of the functions are presented according to the concept, their properties are studied, and some necessary examples that show the properties of this concept and its relationships.

Let $R$ be a $Gamma$-ring and $partial$ be an $RGamma$-module. A proper $RGamma$-submodule. $T$ of an $RGamma$-module $partial$ is called Z-prime $RGamma$-submodule if for each $tin partial, gamma in Gamma$ and $f in partial^{ast}=Hom_{R_{Gamma}}(partial,R),f(t)gamma t in T$ implies that either $t in T$ or $f(t)in [T: _{R_{Gamma}} partial]$. The purpose of this paper is to introduce interesting theorems and properties of Z- prime $RGamma$-submodule of $RGamma$-module and the relation of Z-prime $RGamma$-submodule, which represents of generalization Z-prime R-submodule of R-module.

In this paper, the concepts of almost maximal submodules are fuzzified and studied. Also, three new concepts are introduced, which are: e-simple submodules, Ee-simple modules and e-maximal submodules. Then these concepts are fuzzified and studied.

In this article, we investigated the dynamic behavior of a discrete-time population model with the harvest. We give numerical simulation and chaos control by using the linear feedback control method.

This paper proposes a new method by hybrid Simplex Downhill Algorithm with Moment Method (SMOM) to estimate the parameters of Log-Logistic distribution based on Survival functions. Simulation is used to compare the suggested methods with two classical methods (Maximum Likelihood Estimator and with Moment Method). The results demonstrate that SMOM was efficient than the maximum likelihood Estimator and Moment method based on Mean Square Error (MSE).

In this paper, the Kantorovich method for the numerical solution of nonlinear emph{Uryshon} equations with a smooth kernel is considered. The approximating operator is chosen to be either the orthogonal projection or an interpolatory projection using a Legendre polynomial basis. The order of convergence of the proposed method and those of superconvergence of the iterated versions are established. We show that these orders of convergence are valid in the corresponding discrete methods obtained by replacing the integration by a quadrature rule. Numerical examples are given to illustrate the theoretical estimates.

For solving differential equations, a variety of numerical methods are available, accuracy, performance, and application are all different. In this article, we proposed new numerical techniques for solving the generalized regularized long wave equation(GRLWE) that are based on types M and M-1 of B-splines-least-square method (BSLSM) and weight function of B-splines respectively, which were proposed previously for solving integro-differential equations [2] where $Min {N}$.  We investigated linear stability using a Fourier method.

Let $P(z) =displaystyle prod_{v=1}^n (z-z_v),$ be a monic polynomial of degree $n$, then, $G_gamma[P(z)] = displaystyle sum_{k=1}^n gamma_k prod_{{v=1},{v neq k}}^n (z-z_v),$ where $gamma= (gamma_1,gamma_2,dots,gamma_n)$ is a n-tuple of positive real numbers with $sum_{k=1}^n gamma_k = n$, be its generalized derivative. The classical Gauss-Lucas Theorem on the location of critical points have been extended to the class of generalized derivativecite{g}. In this paper, we extend the Specht Theorem and the results proved by A.Aziz cite{1} on the location of critical points to the class of generalized derivative .

The notion that infectious disease transmission and dissemination are governed by rules that may be expressed mathematically is not new. In fact, the nonlinear dynamics of infectious illness transmission were only fully recognized in the twentieth century. However, with the Coronavirus outbreak, there is a lot of discussion and study regarding the origin of the epidemic and how it spreads before all vulnerable people are infected, as well as ideas about how the disease virulence changes during the epidemic. In this paper, we provide some critical mathematical models which are SIR and SIS and their differences in approach for the interpretation and transmission of viruses and other epidemics as well as formulate the optimal control problem with vaccinations.

The paper deals with two different aspects of wavelet frames. First, we obtain a necessary condition on irregular wavelet frames on local fields of positive characteristic and in the second aspect, we present some results on the perturbation of wavelet frames, when we disturb the mother function of a wavelet frame or dilation parameter. All the results have been carried without the compactness of support neither on generating function nor on its Fourier transform.

Discovering objects and knowing their number has been discussed in many works. Face detection technology is important for the visual scene, Deep learning theory using computer technology to discover the face, which is a wide field in marketing, traffic and security system control systems, in addition to photography. Facial recognition algorithms or face detection Include steps for the facial image to extract features to match them with a database. The face has a biometric feature. The facial feature consists of prominent and easily identifiable information that is responsible for distinguishing the objects that distinguish the face, the distance between the eyes, the shape of the nose, and The mouth for the device to perform a training group and record the data. Matlab program helps to dispense with training because MATLAB provides the instruction (CascadeObjectDetector) for facial recognition and the Viola-Jones algorithm with the result of separating the detection results in the form of subsets. In this work, a new algorithm is created for a group of elements in the unit picture And run a face detection code to highlight the background to store the information of each image in a specified folder and the face detection techniques by proposing a new algorithm to detect a face from among a group of faces, distinguish it and make it a file of its own, all of that using the Matlab program to train the neural network for face recognition.

This study deals with a bi-objective flexible flow shop problem (BOFFSP) with transportation times and preventive maintenance (PM) on transporters via considering limited buffers. The PM actions on transporters are a missing part of the literature of the flexible flow shop problem (FFSP) which before the breakdown occurs, each transporter at each stage is stopped and the PM action is performed on it. The capacity of each intermediate buffer is limited and each job has to wait in the intermediate buffers. By including all these features in the proposed BOFFSP, not only processing times affect the objective functions, but also, the transportation times of jobs, the waiting time of jobs in the intermediate buffers, and availability of transporters in the system are considered in the model and make it a sample of a real-world FFSP. The presented BOFFSP has simultaneously minimized the total completion time and the unavailability of the system. As the problem is NP-hard, a non-dominated sorting genetic algorithm II (NSGA-II) and a multi-objective particle swarm optimization (MOPSO) is proposed to solve the model for large size problems. The experimental results show that the proposed MOPSO relatively outperforms the presented NSGA-II in terms of five different metrics considered to compare their performance. Afterwards, two one-way ANOVA tests are performed. It can be observed MOPSO achieves relatively better results than NSGA-II. Finally, sensitivity analysis is conducted to investigate the sensitivity of the objective functions to the number of jobs and their transportation time at each stage.

Let $r(z)= f(z)/w(z)$ where $f(z)$ be a polynomial of degree at most $n$ and $w(z)= prod_{j=1}^{n}(z-a_{j})$, $|a_j|> 1$ for $1leq j leq n.$ If the rational function $r(z)neq 0$ in $|z|< k$, then for $k =1$, it is known that $$left|r(Rz)right|leq left(frac{left|B(Rz)right|+1}{2}right) underset{|z|=1}sup|r(z)|,,, for ,,,|z|=1$$ where $ B(z)= prod_{j=1}^{n}left{(1-bar{a_{j}}z)/(z-a_{j})right}$. In this paper, we consider the case $k geq 1$ and obtain certain results concerning the growth of the maximum modulus of the rational functions with prescribed poles and restricted zeros in the Chebyshev norm on the unit circle in the complex plane.

Here our aim is to prove the Hermite-Hadamard and Fejér inequalities for p-convex functions via Caputo fractional derivatives. We also establish some useful identities in order to find further Hadamard’s and Fejér type inequalities which are generalizations of the results given in the literature cited here.

In this manuscript, we introduce and study the existence of a solution of a system of generalized nonlinear variational-like inclusion problems in 2-uniformly smooth Banach spaces by using $H(.,.)$-$eta$-proximal mapping. The method used in this paper can be considered as an extension of methods for studying the existence of solutions of various classes of variational inclusions considered and studied by many authors in 2-uniformly smooth Banach spaces. Some important results, theorems and the existence of solution of the proposed system of generalized nonlinear variational-like inclusion problems have been derived.

In this paper, we have established some trapezoid type inequalities for generalized fractional integral. The results presented here would provide some fractional inequalities and Riemann-Liouville type fractional operators.

‎In recent years the binary quadratic program has grown in‎ ‎combinatorial optimization‎. ‎Quadratic programming can‎ ‎be formulated as a semidefinite programming problem‎. ‎In this paper‎, ‎we consider the general form of‎ ‎binary semidefinite programming problems (BSDP)‎.‎ ‎We show the optimal solutions of the BSDP belong to the efficient set of a semidefinite multiobjective programming problem (SDMOP)‎. ‎Although‎ ‎finding all efficient points for multiobjective is not an easy problem‎, ‎but‎‎solving a continuous problem would be easier than a discrete variable problem‎. ‎In this paper‎, ‎we solve an SDMOP‎, ‎as an auxiliary‎, ‎instead of BSDP‎. We show the performance of our method by generating and solving random problems.

As the prevalence of Wireless Sensor Networks (WSNs) grows in the many mission-critical applications such as military and civil domains, the need for network security has become a critical concern. The inherently vulnerable characteristics of WSNs appoint them susceptible to various types of attacks. A particularly harmful attack against sensor and ad hoc networks is known as the Sybil attack, where a node illegitimately claims multiple identities. Sybil attacks can severely deteriorate the network performance and compromise the security by disrupting many networking protocols. This paper presents a lightweight Intrusion detection system (IDS) based on received signal strength indicator (RSSI) readings of messages to protect WSNs against Sybil attack. Our idea in the proposed method is based on the local calculation (within each node and without the need for communications) the RSSI ratio from the suspected nodes to the Sybil attack. The obtained results demonstrate that Proposed System achieves high detection accuracy, low false alarm rate and low energy consumption appointing it a promising IDS candidate for wireless sensor networks.

To sustain in the global competitive market, the manufacturing organizations are adopting tools like Total Productive Maintenance (TPM), Lean Manufacturing (LM), etc. Implementation of these tools was assessed by an effectiveness index called Overall Equipment Effectiveness (OEE) Throughput Effectiveness (TE). Overall Manufacturing Line Effectiveness (OMLE) used as the performance evaluation index for the integrated tool {[}Total Productive Lean Manufacturing --TPLM{]} implementation performance has been assessed. OMLE is a robust metric of manufacturing performance that incorporates the measures like Line Availability (LA), Line Production Quality Performance (LPQP) of the product line. OMLE offers a means of controlling the whole production process by analyzing results from the totality of events with or without inventory between the processes in the product line. In the present paper, the attempt made towards identifying the bottleneck parameter (Losses in the processes --category A, the Cycle time in the processes-category B, Inventory between the process -- category C) in the bottleneck processes of the n number of the processes product line through programming (software in C). Also, the top three parameters of the processes in the n process product line have been obtained easily towards executing improvements by the engineers and managers. Analyze it with the change of inventory and zero inventory between the five, seven, and nine different processes product line processes. The suggested improvement activities (obtained from the OMLE evaluation and optimization program) are validated in south India's real case study organization and improved the bottleneck processes and losses in the product line.

Audio-visual detection and recognition system is thought to become the most promising methods for many applications includes surveillance, speech recognition, eavesdropping devices, intelligence operations, etc. In the recent field of human recognition, the majority of the research be- coming performed presently is focused on the reidentification of various body images taken by several cameras or its focuses on recognized audio-only. However, in some cases these traditional methods can- not be useful when used alone such as in indoor surveillance systems, that are installed close to the ceiling and capture images right from above in a downwards direction and in some cases people don't look straight the cameras or it cannot be added in some area such as W.C. or sleeping room. Thus, its commonly difficult to identify any movement or breakthrough process, on the other hand when need to pursue suspect when enter a building or party to identify his location and/or listen to his speech only and isolate it from other voices or noises, the other. Hence, the use of the hybrid combination technique is very effective. In this work, we proposed a multimodal human recognition approach that utilizes both the face and audio and is based upon a deep convolutional neural network (CNN). Mainly, to solve the challenge of not capturing part of the body, final results of recognizing via separate CNNs of VGG Face16 and ResNet50 are joined together depending on the score-level combination by Weighted Sum rule to enhance recognition performance. The results show that the proposed system success to recognise each person from his voice and/or his face captured. In addition, the system can separate the person voice and isolate it from noisy environment and determine the existence of desired person.

In this paper, we use the homoclinic orbit approach without using small perturbations to prove the existence of soliton solutions of the discrete nonlinear Schrödinger equations with long-range interaction by employing the properties of the symmetries of reversible planar maps. Moreover, the long-range interaction by a potential proportional to $1/l^{1+alpha} $ with fractional $alpha < 1 $ and $l $ as natural number.

This paper focuses on the solvability and approximate analytic solution of composed linear descriptor operator with constant coefficient, using functional (Variational) approach. This approach is based on finding a suitable functional form whose critical points are the solution of the proposed problem and the solution of a proposed problem is a critical point of the obtained variational functional defined on suitable reflexive Hilbert space since the existence of this approach is based on the symmetry and positivity of the composed linear descriptor operator on Hilbert space. The necessary mathematical requirements are derived and proved. A step-by-step computational algorithm is proposed. Illustration and computation with the giving exact solution are also proposed.

Concrete is a material that the mechanical properties of which change over time. This change is due to chemical reactions within the concrete known as hydration. One of these properties is the modulus of elasticity and longitudinal wave velocity, which has a direct relation with the concrete age and its setting. Just after the materials mixing, the concrete setting is fast, and over time its rate decreases. Here, a series of cubic concrete specimens are prepared and changing in longitudinal wave velocity and modulus of elasticity in different ages is monitored during the process of curing and a relationship has been presented. Materials specifications impact and concrete mixing ratios, including the water to cement ratio and fine to coarse aggregates ratio is studied. Ultrasonic wave velocity has been increased faster at early ages of specimens where the concrete setting process is fast and in last days, rates of increasing the longitudinal wave velocity decreases. An increase in the water to cement ratio leads to increases in the longitudinal wave velocity over time. The empirical equations have been formulated as logarithmic curves. These empirical equations have been developed and a model with more efficiency and precision has been presented. These empirical equations can be used in the analytical and numerical analysis of structures. These models can be used to determine the loading time of concrete structures and to predicting their other physical and mechanical properties, such as strength and stiffness.

This work is concerned with the periodic solution of a doubly degenerate Allen-Cahn equation with nonlocal terms associated with Neumann boundary conditions. Firstly, we define a new associated auxiliary problem. Secondly, the topological degree theorem is applied to prove the existence of a limit point to the auxiliary problem, where this limit point represents a nontrivial nonnegative time-periodic solution of the main studied problem. It is observed that the topological degree theorem technique plays an important role in proving the desired results. Furthermore, this technique can be applied to other similar equations with homogeneous Dirichlet or Neumann boundary conditions.

In this paper, we study the stability of Mann iteration procedure in two directions, namely one due to Harder and the second one due to Rus with respect to a map $T:Kto K$ where $K$ is a nonempty closed convex subset of a normed linear space $X$ and there exist $deltain(0,1)$ and $Lgeq 0$ such that $||Tx-Ty||leqdelta||x-y||+L||x-Tx||$ for $x,yin K$. Also, we show that the Mann iteration procedure is stable in the sense of Rus may not imply that it is stable in the sense of Harder for weak contraction maps. Further, we compare and study the equivalence of these two stabilities and provide examples to illustrate our results.

In this paper, the uniform stability for the solution of integro-differential inequalities, with nonlinear control inputs and delay functions, is investigated by using some inequality estimator conditions. Moreover, we apply the obtained results on the solutions of some proposed classes of integro-differential inequalities with nonlinear control input functions as problem formulations examples. The results show that the stability technique used in this work is efficient and robust and it can be applied to a general class and various types of integro-differential inequalities.

Let $mathcal{P}_n$ be the class of all complex polynomials of degree at most $n.$ Recently Rather et. al.[ On the zeros of certain composite polynomials and an operator preserving inequalities, Ramanujan J., 54(2021)  605–612. url{https://doi.org/10.1007/s11139-020-00261-2}] introduced an operator $N : mathcal{P}_nrightarrow mathcal{P}_n$
defined by $N[P](z):=sum_{j=0}^{k}lambda_jleft(frac{nz}{2}right)^jfrac{P^{(j)}(z)}{j!}, ~ k leq n$ where $lambda_jinmathbb{C}$, $j=0,1,2,ldots,k$ are such that all the zeros of $phi(z) = sum_{j=0}^{k} binom{n}{j}lambda_j z^j$ lie in the half plane $|z| leq left| z - frac{n}{2}right|$ and established certain sharp Bernstein-type polynomial inequalities. In this paper, we prove some more general results concerning the operator $N : mathcal{P}_n rightarrow mathcal{P}_n$ preserving inequalities between polynomials. Our results not only contain several well known results as special cases but also yield certain new interesting results as special cases.

In this paper, we consider the nonlinear $r(x)-$Laplacian Lam'{e} equation $$ u_{tt}-Delta_{e}u-divbig(|nabla u|^{r(x)-2}nabla ubig)+|u_{t}|^{m(x)-2}u_{t}=|u|^{p(x)-2}u $$ in a smoothly bounded domain $Omegasubseteq R^{n}, ngeq1$, where $r(.), m(.)$ and $p(.)$ are continuous and measurable functions. Under suitable conditions on variable exponents and initial data, the blow-up of solutions is proved with negative initial energy as well as positive.

In this research, we analyze the existence of solution for some nonlinear functional integral equations using the techniques of measures of noncompactness and the Petryshyn's fixed point theorem in Banach space. The results obtained in this paper cover many existence results obtained by numerous authors under some weaker conditions. We also give an example satisfying the conditions of our main theorem but not satisfying the conditions described by other authors.

In this paper we apply the Chebyshev polynomials method for the numerical solution of a class of variable-order fractional integro-differential equations with initial conditions. Moreover, a class of variable-order fractional integro-differential equations with fractional derivative of Caputo-Prabhakar sense is considered. The main aim of the Chebyshev polynomials method is to derive four kinds of operational matrices of Chebyshev polynomials. With such operational matrices, an equation is transformed into the products of several dependent matrices, which can also be viewed as the system of linear equations after dispersing the variables. Finally, numerical examples have been presented to demonstrate the accuracy of the proposed method, and the results have  been compared with the exact solution.

Lassa fever is a zoonotic acute viral illness caused by Lassa virus. Since there is no vaccine yet to protect against contracting the virus, it continues to spread in West Africa. In this paper, a mathematical model of lassa transmission that considers two classes of rats: house rat and bush rat, is proposed. Theoretically, global stability of the model disease-free and endemic equilibria are established by constructing a global Lyapunov function. Sensitivity indices of the basic reproduction number are derived using the normalised forward approach to evaluate the effectiveness of control measures. The disease-free equilibrium is globally asymptotically stable when the basic reproduction
number R0 < 1 and the unique endemic equilibrium is globally asymptotically stable when R0 > 1. Results from sensitivity analysis reveals that rat biting rate for infectious house rats RFH and infectious bush rats R_FB, transmission probability per contact with infectious house and bush rats (R_FH and R_FB), human recruitment rate and transmission probability per contact with infectious human hosts are highly significant in determining the severity of lassa infection. On the other hand, natural death rate of rats, natural death rate of human hosts, recovery and hospitalization rates of human hosts are critical for lassa transmission reduction. Plans that target the contact rate between house and bush rats (i.e use of indoor residual spray, fumigation of environment with pesticide) and
those that target recovery rate of human hosts (i.e treatment of infectious human hosts) are recommended to control the disease.

In this study, a vector Autoregressive model was used to analysis the relationship between two time series as well as forecasting. Two financial time series have been used, which are a series of global monthly oil price and global monthly gold price in dollars for a period from January 2015 to Jun 2019. It has 54 monthly values, where the data has been transferred to get the Stationarity, Diekey Fuller test for the Stationarity was conducted. The best three order for model was determined through a standard Akaike information AIC, it is VAR(7) , VAR(8) and VAR(10) respectively. The comparison was made between selected orders by AIC based on the accuracy measure and mean square error (MSE). It turns out that less MSE value of the VAR(10) model. Some tests were conducted like Lagrange-multiplier, Portmanteau, Jarque - Bera to residuals for the selected model, with forecasting for the VAR(10) model for the period from Jun 2019 to Jun 2021 , It is 24 monthly value. It turns out that less MSE for forecasting value for oil price series is to VAR(7) model and less MSE for forecasting value for gold price series is VAR(10) model. The results have been computed through the Stata program.

The maximum likelihood estimator employs in this paper to shrinkage estimation procedure for an estimate the system reliability $R$ in the stress-strength model, when the stress and strength are independent and non-identically random variables and they follows the odd Fr`{e}chet inverse exponential distribution (OFIED). Comparisons among the proposed estimators were presented depend on simulation established on mean squared error (MSE) criteria.

In this paper, we investigate existence and uniqueness of solutions of nonlinear Volterra-Fredholm impulsive integrodifferential equations. Utilizing theory of Picard operators we examine data dependence of solutions on initial conditions and on nonlinear functions involved in integrodifferential equations. Further, we extend the integral inequality for piece-wise continuous functions to mixed case and apply it to investigate the dependence of solution on initial data through $epsilon$-approximate solutions. It is seen that the uniqueness and dependency results got by means of integral inequity requires less restrictions on the functions involved in the equations than that required through Picard operators theory.

Due to the nature of activities and processes, the petrochemical industry causes the production of industrial effluents, emissions and wastes that have adverse effects on the environment. The purpose of this study is to investigate the effect of petrochemical economic activities on environmental factors. In this paper, in order to minimize the costs and the amount of pollution caused by the emission of harmful gases, the closed-loop green supply chain model has been used, in which direct and reverse logistics networks have been considered. As a result, a fuzzy mathematical programming model has been developed for when the data are not definitively known. After the demand parameters and the amount of pollution are considered fuzzy, the maximum and bisector mean methods of the area are considered as methods (diffusion) of comparison and ranking of fuzzy definite numbers, and by adding Limitations of these two methods, the model was developed. To solve the model with real data, a plant from the petrochemical industry was selected and the data were prepared for a solution with very good estimates. Finally, the colonial competition algorithm was used to solve it. According to the model, its applicability was shown to reduce the number of environmental pollutants along with the reduction of transportation and waste, and the model for the closed-loop supply chain, which simultaneously considers two direct and inverse logistics networks. It is appropriate.

Using the fixed point method, we prove the Hyers-Ulam stability and the superstability of $n$-Jordan $*$-derivations in Fr'echet locally $C^*$-algebras for the following generalized Jensen-type functional equation
$$ fleft(frac{ a+b}{2} right) + fleft( frac{a-b}{2} right) =f(a).$$

‎In the present paper‎, ‎we try to investigate the Noether symmetries and Lie point symmetries of the‎ ‎Vaidya-Bonner geodesics‎. ‎Classification of one--dimensional subalgebras of Lie‎ ‎point symmetries are considered‎. ‎In fact‎, ‎the collection of pairwise non-conjugate one--dimensional‎ ‎subalgebras that is called the optimal system of subalgebras is determined‎. ‎Moreover‎, ‎as illustrative‎ ‎examples‎, ‎the symmetry analysis is implemented on two special cases of the system‎.

The aim of this paper is to establish coupled coincidence and coupled common fixed point theorems involving a pair of weakly compatible mappings satisfying rational type contractive condition in the setting of dislocated quasi b-metric spaces. The presented result improves and generalizes several well-known comparable results in the existing literature. We also provided an example in support of our main result.

Cyber-Physical Systems are one of the emerging technologies which involve the integration of cyber system physical and control systems. This Cyber-physical System automates the industrial process like manufacturing, monitoring and control. Since the system involves three different cyber, physical and control optimization domains, such systems are complex in nature and cannot be done with a traditional optimization mechanism. Machine learning and deep learning are efficient mechanisms to model the behavior of such complex systems for design and optimization. In this work, the application of machine learning mechanisms in the cyber-physical system for various purposes like security, re-organization, and scheduling. This systematic review will give more insight into the latest application and mechanism of machine learning and deep learning for the cyber-physical system.</span>

Parkinson's Disease (PD) is a neurodegenerative disorder that affects predominantly neurons in the brain. The main purpose of this paper is to define a way in detecting the PD in its early stages. This has been achieved through the use of recorded speech, a biomarker in the natural environment in its original state.  In this paper, the Mel-Frequency Cepstral Coefficients (MFCC)  method is utilized to extract features from the recorded speech. The principal component analysis (PCA) and Genetic algorithm (GA) are then applied for feature extraction/selection. Once the features are selected, multiple classifiers are then applied for classification. Performance metrics such as accuracy, specificity, and sensitivity are measured. The result shows that Support Vector Machine (SVM) along with the GA has shown optimal performance.

EEG (Electroencephalogram) is brain waves measure. It is available test allowed to discover the brain functions over time. The brain troubles are evaluated by EEG. It is used to locate the activity in the brain during a seizure and to consider the patients who suffer from brain functionality problems. These troubles include tumors, coma, confusion and long-term difficulties (such as weakness associated with a stroke). The acquisition of EEG signals requires contact and liveliness and these signals are changes under stress that make so potentially unnecessary if it is acquired under menace. In this paper, an innovative and robust solution for this problem is introduced. To this end, the manner depends on models of various data compression models of information-theoretic plus the metrics symmetry related to Kolmogorov complexity. The proposed procedure compares two EEG segments and clusters the data into three groups: a corresponding record for each participant, a distinct person for each group, and self-participant. The technique was used to determine the database participant based on EEG signals. Using a distance measuring approach suggested in this scheme, a 1-NN classifier was constructed. Nearly every person in the underlying database could be accurately identified by the classifier with $96%$ accuracy

The aim of this paper is to use (U,V)-Lucas polynomials to introduce and study a new family of holomorphic and bi-univalent functions defined in the open unit disk which involve q-derivative operator. We investigate upper bounds for the Taylor-Maclaurin coefficients |d2| and |d3| and Fekete- Szego ̈ problem for functions belongs to this new family. Some interesting consequences of the results established here are indicated.

Most time series are characterized in practice that they consist of two components, linear and non-linear, and when making predictions, the single models are not sufficient to model these series. Recently several linear, non-linear and hybrid models have been proposed for prediction, In this research, a new hybrid model was proposed based on the combination of the linear model Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) with the non-linear model fuzzy time series model (FTS). The proposed hybrid model analyzes the linear component of the specified time series using the ARFIMA model, calculates the estimated values, and then calculates the residuals for this model by subtracting the estimated values from the original time series. The nonlinear component is analyzed using the (FTS) model for the computed residuals, which inherently contain the nonlinear patterns of the time series. The final values for the prediction by applying the} {hybrid model (ARFIMA-FTS) are obtained by combining the predictions of the (ARFIMA) model of the original series with the predictions of the model (FTS) for the residual series. The new hybrid model was used to predict those infected with the Covid-19 virus in Iraq for the period from 24/2/2020 to 11/8/2021. The proposed model was more efficient in the prediction process than the single (ARFIMA) model using a number of comparison criteria, including (RMSE), (MAPE) and (MAE). The final results showed that the proposed model has the ability to predict time series that contain linear and nonlinear components

The broad increase make use of digital cameras, by hand wound imaging has turn out to be common practice in experimental place. There is in malice of still a condition for a reasonable device for accurate wound curing consideration between dimensional facility and tissue categorization in a exacting simple to exploit technique We achieved the major unit of this plan by computing a 3-D model for wound dimensions using un calibrated revelation techniques. We highlight at this point on tissue classification from color and eminence region descriptors computed after unverified segmentation. As a result of perception distortions, unconstrained lighting provisions and viewpoints, wound assessments modify commonly in the middle of patient review. The majority significant separation of this article is to overcome this trouble by means of a multi inspection approach for tissue classification, relying on a 3-D model onto which tissue labels are mapped and categorization result merged. The investigational categorization tests communicate that improved repeatability and robustness are obtained and that metric assessment is attain through appropriate region and degree dimensions and wound chart origin.In this manuscript we proposed wound image segmentation, tissue classification in grouping with the Random Forest (RF). These methodology are helpful for classifying the rate of injured tissue in a segmented element and improved accuracy.

The aim of this paper is to establish new fixed point theorems for single-valued and multivalued maps which satisfy α-ψ-contraction conditions in the complete fuzzy metric space. In this paper, we extend the results of Hussain et al. and Samet et al. Some comparative examples are also given which demonstrate the superiority of our results from the exiting results in the literature.

In this article, a modern analytical technique called Homotopy Analysis Method (HAM) has been applied to several partial differential equations to obtain their sequential solution. The performance of the method was analysed on partial differential equations and found to be computationally efficient. The homotopy analysis method (HAM) has been found to be effective. The numerical results have been improved by proposing a new technique that combines the numerical method with the Harris Hawks Optimization algorithm. The current numerical results are compared to the exact solutions and it's found to be in very good results.

The Covid-19 pandemic has first been identified in China at the end of 2019 and later has spread worldwide. In Malaysia, Covid-19 cases have increased drastically in March 2020 that caused the Government to implement a Movement Control Order (MCO) starting 18 March 2020 to curb the outbreak which has affected all sectors in the country including higher education institutions. The objective of this paper is to present the impact of MCO implementation on the electricity consumption on the university buildings particularly at Universiti Malaysia Perlis. The methodology processes involve comparing the energy usage in 2019 as baseline data with usage in 2020 as the assessed data. These data then been analyzed for every 3 months or quarterly for reporting. Based on the results, MCO implementation has shown positive impact to the energy conservation where 3,023 MWh of electrical energy has been reduced in 2020 which is 25.72% less than the same period in 2019. This condition has been extremely beneficial to the university’s operations where RM 1.369 million of electricity bill has been saved. In the aspect of environmental sustainability, the amount of energy reduction is equivalent to more than 2,000 tonnes of CO2 avoidance at rated value 0.694 tCO2 per MWh. As a result from energy reduction, the BEI performance of the buildings has improved from 3-Star (moderately efficient) to 4-Star (efficient).

This paper is to define a new iterative scheme under a special sequence of asymptotically nonexpansive mapping with a special sequence. We prove some convergence, existence in {CAT(0)} space.

Power quality problems in the distribution system have tremendously escalated due to numerous consumptions of various types of loads especially nonlinear loads. These problems have affected the utility grid and the consumers in the distribution system resulting in equipment breakdown, overheating in utility system equipment, and other problems related to electronics devices that are being used by the utility system and the consumers. The electronics devices are quite sensitive and can be malfunctioned even with small disruptions that occured in the supply power system. The power quality problem involved with nonlinear loads can be mitigated by using Distribution Static Synchronous Compensator (DSTATCOM). This paper proposed a method for power quality improvement by using a Frequency Domain-based Voltage Reference Configuration (VRC) control algorithm for the Distribution Static Synchronous Compensator (DSTATCOM) in a three-phase distribution system. The performance of the proposed control algorithm is simulated in the MATLAB environment using Simulink. It is verified that the proposed control algorithm can reduce the THD of the distorted grid current at the Point of Common Coupling (PCC) below 5% according to the IEEE Standard 519:2014 under nonlinear loads and unbalanced loads conditions.</span>

Cayley- Purser Algorithm is a public key algorithm invited by Sarah Flannery in 1998. The algorithm of Cayley-Purser is much faster than some public key methods like RSA but the problem of it is that it can be easily broken especially if some of the private key information is known. The solution to this problem is to modify this algorithm to be more secure than before so that it gives its utilizers the confidence of using it in encrypting important and sensitive information. In this paper, a modification to this algorithm based on using general linear groups over Galois field $GF(p^n)$, which is represented by $GL_m(GF(p^n))$ where $n$ and $m$ are positive integers and $p$ is prime, instead of $GL_2(Z_n)$ which is General linear set of inverted matrices $2 times 2$ whose entries are integers modulo $n$. This $GL_m(GF(p^n))$ ensures that the secret key of this algorithm would be very hard to be obtained. Therefore, this new modification can make the Cayley-Purser Algorithm more immune to any future attacks.

Researchers have traditionally focused more on the quantity of company information disclosures, but in recent years, the quality of the information disclosed is more important than their quantity. On the other hand, the primary purpose of financial reporting is to provide useful information to stakeholders in relation to the conditions of the company to help investors' economic decisions, which is a function of the quality of the information provided by companies. Therefore, in the present study, by examining the opinions of experts and using the process of hierarchical analysis technique, the weight and importance of risk information disclosure criteria at three different levels (general, company specific and industry specific) were extracted in comparison, then by prioritizing them, Compatibility rate was calculated. Using statistical methods of two-way analysis of variance within the subject, it was concluded that there is no significant difference between the effect of risk information disclosure criteria on corporate investors' decision making.

The main goal of this paper is to obtain the estimated determination of the quantum dot potential for PbS and PbTe structures (in mV) which are compared to other values at different QD's diameters (in nm). In order to investigate this goal, the study relies on two methods of interpolation such as Neville and Spline methods, as well as it constructs mathematical models that help to find the estimate determination of quantum dot potential for PbS and PbTe structures compared to other values at different QD's diameters. The numerical results were very close to the real results. Finally, we estimated the determinations outside the fields and labs of the measured areas.

In this paper, we have introduced a new class of sets in topological space called $B^{ic}$-open set and we have introduced and study the properties of $B^{ic}$-continuous function and topological properties.

Deep fakes is the process of changing the information of the image or video with different techniques and methods that start with humor and fun and sometimes reach economic, political and social goals such as counterfeiting, financial fraud or impersonation. The data for this field is still increasing at a very high rate. And therefore. The process of combating and exploring them is a very difficult task. In this paper, we conducted a review of previous studies and what researchers dealt with on the subject of deep fakes. Explain the concepts of deepfakes. Counterfeiting methods and techniques and patterns through the techniques and algorithms used in counterfeiting. Some deepfake detection algorithms. 

The aim of the present study was to compare the productive power of foreign adjusted bankruptcy by providing a proposed model of bankruptcy in Iran. This research is applied in terms of purpose and descriptive and correlational in terms of nature which through that bankruptcy models are studied according to the Shumway's hazard model (2001), and in the form of a logit model and accounting are evaluated based on Pourheydari's and Koopayee's model (2010). Accordingly, a sample including1287 years, the company from 2006 to 2018 has been selected from Tehran Stock Exchange. For statistical hypothesis testing, receiver operating characteristics (ROC) was applied. Receiver operating characteristics is applicable in software like SAS, SPSS, and STATA. The results showed that Shumway's model (2001) provides a more accurate bankruptcy prediction of companies and this is the sign of superiority of the hazard model over classic models.

It’s easy to exchange files, folders, and emails in a private network with the aid of a private cloud (PriCld). However, the most common attack with this sort of system is the guessing of the password. The unencrypted connection makes it potential for a man-in-the-middle attack (MaMiAtk) to occur at times. Scanners used to discover vulnerabilities in the system and exploiting them are also a major issue. Therefore, a system that can withstand assaults such as MaMiAtk, Denial of Service Attack (DoS attack), and password guessing is required. In the presented study, a log-based analysis approach has been recommended to guard the system against assaults such as DoS attack, password guessing, and automated scanners. We also use encrypted channels to prevent attacks such as MaMiAtk.

Electroencephalography (EEG) signals are commonly used to identify and diagnose brain disorders. Each EEG normal waveform consists of the following waveforms: Gamma(γ) wave, Beta (β) wave, Alpha (α), Theta (θ), and Delta (δ). The term Neurological Diseases ” NurDis ” is used to describe a variety of conditions that affect the nervous Epilepsy, neuro infections (bacterial and viral), brain tumors, cerebrovascular diseases, Alzheimer’s disease, and various dementias are all examples of neurological disorders. Encephalitis is one of the illnesses that affects the brain. The EEG signals used in this paper were from the CHB-MIT Scalp EEG database. The discrete wavelet transform (DWT) was utilized to extract characteristics from the filtered EEG data. Finally, classifiers such as K Nearest Neighbor (KNN) and Support vector machine (SVM) were used to categorize the EEG signals into normal and pathological signal classes using all of the computed characteristics. In order to categorize the signal in a normal and anomalous group, the KNN and SVM classifiers are employed. For both classifiers, performance assessments (accuracy, sensitivity and specificity) are determined. KNN classifier accuracy is 71.88%, whereas SVM classifier accuracy is 81.23%. The sensitivity of KNN and SVM are 80.14% and 77.31%, respectively. The KNN classification specificity is 69.62% and the SVM classification specificity is 98%. Both classifiers performance is evaluated using the confusion matrix.

‎This article deals with the nonlinear parabolic equation with piecewise continuous arguments (EPCA)‎. ‎This study‎, ‎therefore‎, ‎with the aid of the $theta$‎ ‎-methods,‎ ‎aims at presenting a numerical solution scheme for solving such types of equations which has applications in certain ecological studies‎. ‎Moreover‎, ‎the convergence and stability of our proposed numerical method are investigated‎.‎Finally‎, ‎to support and confirm our theoretical results‎, ‎some numerical examples are also presented‎.

In this paper, we introduce a new self-adaptive hybrid algorithm of inertial form for solving Split Feasibility Problem (SFP) which also solve a Monotone Inclusion Problem (MIP) and a Fixed Point Problem (FPP) in $p$-uniformly convex and uniformly smooth Banach spaces. Motivated by the self-adaptive technique, we incorporate the inertial technique to accelerate the convergence of the proposed method. Under standard and mild assumption of monotonicity of the SFP associated mapping, we establish the strong convergence of the sequence generated by our algorithm which does not require a prior knowledge of the norm of the bounded linear operator. Some numerical examples are presented to illustrate the performance of our method as well as comparing it with some related methods in the literature.

The line graph of the graph $Gamma$ denoted by $L(Gamma)$ is a graph with a vertex set consists of the sets of edges of $Gamma$ and two vertices are adjacent in $L(Gamma)$ if they are incident in $Gamma$. In this article, we discuss and determine the effect of operations on the line graphs of simple graphs.

Bubble sizes are generated by micro-bubble generators (MBGs) in the water for their effect on the percentage of dissolved oxygen in the water and we find this in aquaculture where oxygen is important to marine life and in many applications. And since these bubbles range in size from 20 to 50, we need to highlight the shape of the bubble and distinguish it, so two Sobel algorithms were used and the Canny method was improved and compared between them, where the edge detection algorithm is sensitive to noise, and therefore, it is easy to lose weak edge information when filtering noise, and it appears ts fixed parameters are weak adaptability. In response to these problems, this paper proposed an improved algorithm based on the Canny algorithm. This algorithm introduced the concept of gravitational field strength to replace the image gradient and obtained the gravitational field strength factor. Two methods for choosing the adaptive threshold based on the average image gradation The size and standard deviation of two types of model images (one containing less edge information, the other containing rich edge information) were subtracted, respectively. The improved Canny algorithm is simple and easy to achieve. Experimental results show that the algorithm can retain more useful information and is more robust in the face of noise.

In the present paper, we introduce new types of convergence of a sequence in left dislocated and right dislocated metric spaces. Also, we generalize the Banach contraction principle in these newly defined generalized metric spaces.

Wavelets are now perceived as a ground-breaking new mathematical apparatus in signal and picture handling, time arrangement analysis, geophysics, guess hypothesis, and numerous various zones. Wavelet analysis has been an effective way to deal with issues related to analysing a signal in both time and frequency areas. Analysing a non-stationary signal becomes difficult for the various transform methods. Wavelet transforms techniques like Fourier Transform (FT), short-time Fourier transforms (STFT), and wavelet transform (WT) methods and their application are also discussed in detail. Wavelet analysis can be applied to one-dimensional signals like audio signals as well as two-dimensional signals like images. However wavelet processing has wide applications, some of the applications and methods are discussed in the paper.

Airports are considered a civilized interface that reflects the economic strength of the country and its development, and that Basra International Airport is the second largest airport in Iraq in terms of area, and one of the most important services it provides is to reduce the waiting time at the boarding pass stations and the security check station and passport stamping for foreign trips and to calculate the airport's ability to leave travellers from Basra airport. The simulation was used to find out the ability of Basra Airport to accommodate the number of passengers at peak times of the sequential queuing networks model.

We have established the solvability of fractional integral equations with both $(k,s)$-Riemann-Liouville and Erd'{e}lyi-Kober fractional integrals using a new generalized version of the Darbo's theorem using Mizogochi-Takahashi mappings and justify the validity of our results with the help of suitable examples.

In this paper, Pareto distribution was studied using a standard Bayes estimator. A Pareto distribution of two parameters is investigated to find the approximation of (M.S.E) of the shape parameter by depending on Tyler series of two variables to propose a model mathematically.

Let $R$ be a commutative ring with identity, and (U_{R}) be an $R$-module, with (E = End(U_{R})). In this work we consider a generalization of class small essential submodules namely E-small essential submodules. Where the submodule $Q$ of (U_{R}) is said E-small essential if $Q$ (cap W = 0) , when W is a small submodule of (U_{R}), implies that (N_{S}left( W right) = 0), where (N_{S}left( W right) = left{ psi in E | Impsi subseteq W right}). The intersection ({overline{B}}_{R}(U)) of each submodule of (U_{R}) contained in (Soc(U_{R})). The ({overline{B}}_{R}(U)) is unique largest E-small essential submodule of (U_{R}), if (U_{R}) is cyclic. Also in this paper we study ({overline{B}}_{R}(U)) and ({overline{W}}_{E}left( U right)). The condition when ({overline{B}}_{R}(U)) is E-small essential, and (text{Tot}left( U,U right) = {overline{W}}_{E}left( U right) = J(E)) are given.

In this work, an environmental and epidemiological model has been formulated and analyzed, where it was assumed that there is a disease in the predator society of an incurable type and does not give the predator immunity that leads to the death of the affected predator in the end. On the other hand, it has been assumed that a healthy predator is only capable of predation according to a functional response ~Holling type -- IV, Also, two types of factors are considered behavior against predation and the group's defence to formulate our proposed model. In addition, immigration was taken into consideration for prey society, mathematically and biologically acceptable equilibrium points for this model were found, as well, these points were studied analytically and numerically to know the effect especially, the emigration and behavior against predation to keep both the two types.

In this paper, the dynamical behaviour of an {epidemiological system} has been investigated. A stage-structured prey-predator model includes harvest and refuge for only prey, the disease of type (SIS) is just in the immature of the prey and the disease is spread by contact and by external source has been studied. The transmission of infectious diseases in the prey populations has been described by the linear type. While Lotka-Volterra functional response is used to describe the predation process of the whole prey population. This model has been represented by a set of nonlinear differential equations. The solution's existence, uniqueness and boundedness have been studied. "The local and global stability conditions of all the equilibrium points" have been confirmed. As a final point, numerical simulation has been used to study the global dynamics of the model.

With the repeated values, that the factorial experiments will be in three nested factors. And, the third factor is presented by experimental units (subjects). The repeated values or the experimental unit treatments definitely can be taken. These treatments can be dealt with as a fourth factor. Actually, these kinds of experiments have been analyzed in factorial ways, which are presented by the F test. That can be taken place in the condition of variance analysis to the repeated values experiments and in case there is no condition fitting in, we may use non-factorial ways which are presented by shifting into ranks. Therefore, the aim of this research is to make an analyzed study for this kind of factorial ways or non-factorial. This kind of experiment can be applied to Thalassemia in Thi-Qar province.

A mathematical model for the control of the banana weevil  Cosmopolites Sordidus </em> (Germar) by predatory ant species  is formulated and analyzed. The model incorporates predator switching to a non-dynamic alternative food source, optimal foraging theory and self regulation in both the banana weevil and predatory-ant species! Using Lyapunov's first method, the local stability of the equilibria is established. Furthermore, conditions for the existence of the interior equilibrium are derived and its global stability  established by the Bendixson--Dulac criterion with   periodic orbits  ruled out by  the Poincare--Bendixson theorem. It is determined that intrinsic growth rates and carrying capacities rather than handling time and nutritional value have significant impact on the banana weevils-- predatory ant interaction. Numerical simulations   confirm the theoretical results.

The Security is requested to relocate paramount information across the networks. To protect the confidential data from hacking, this paper describes a process to increase the security of the RSA algorithm by creating additional layer protection for it using Hermite polynomials which will be represented as a square matrix, its calculation is not complicated. In the RSA process, we need to choose very large numbers that lead to complex operations which require a long computation time, while in proposed encryption due to Hermite key we don't need that because two layers of encryption give robust safeness to the ciphertext from dangers of hackers due to the hard of breakable.

In this study, we used a powerful method, named as Sumudu-Elzaki transform method (SETM) together with Adomian polynomials (APs), which can be used to solve non-linear partial differential equations. We will give the essential clarification of this method by expanding some numerical examples to exhibit the viability and the effortlessness of this technique which can be used to solve other non-linear problems.

A study has proven the benefits of mathematics classes learning mathematics at university. However, there is still a lack of evidence regarding its benefits in mathematics teacher education programs. This study aims to test the flipped class in a mathematics teacher education program at a private university in Indonesia. The data source comes from thirty-one students of the mathematics education program in this study. Various data methods were used, including observation, journals, and tests. Then the data were analyzed quantitatively and qualitatively. The findings showed that a reverse classroom encourages students to learn independently, with students working together with peers and increasing learning awareness. However, some of the challenges presented in flipped classroom applications include technical issues, record editing skills, and longer time consumed. The recommendations offered to refer to the findings.

Critical thinking is a skill needed in the 4.0 era of education so to improve critical thinking requires the right method so that learning can develop students' critical thinking skills. The purpose of this study was to determine the effect of guided discovery learning models on students' critical thinking skills on the material of the immune system. This type of research is quasi-experimental and post-test only control group design. Samples were taken by cluster random sampling technique. A total of 69 students were made respondents divided into 2 classes, namely class XI IPA 2 (n = 35) and XI IPA 3 (n = 34). The research instrument in the form of multiple choice questions of critical thinking skills was used as a data collection tool. Data were analyzed using normality test, homogeneity test, and t test The results of this study indicate that there are significant differences between the experimental class and the control class with tcount (2,031)$mathrm{>}$ ttable (1,668). Conclusion It can be concluded that the use of guided discovery learning models has an effect on students' critical thinking skills with the use of syntax involving students to actively think at a high level to hone their critical thinking skills.

In this paper, we define and investigate the mappings of several variables which are quartic in each variable. We show that such mappings can be unified as an equation, say the multi-quartic functional equation. We also establish the Hyers-Ulam stability of a such functional equation by a fixed point theorem in non-Archimedean normed spaces. Moreover, we generalize some known stability and hyperstability results.

In this paper, we establish some inequalities for rational functions with prescribed poles having t-fold zeros at the origin. The estimates obtained generalise as well as refine some known results for rational functions and in turn, produce extensions of some polynomial inequalities earlier proved by Turan, Jain etc.

Friction stir welding (FSW) produce a strong metallurgical joint with the application of severe deformations and frictional heating in the metals and alloys by using a non-consumable rotating tool consisting of pin and tool shoulder. During welding, the plastic deformations of base metals vary for the varying mechanical properties such as tensile strength (TS), impact strength (IS), and hardness (HV) which in turn varies the welding conditions and parameters. Therefore, selection of optimal weld parameters plays an important role in enhancing the quality of weld joint. In this article, friction stir butt welds made of 6061 and 7075 Al alloys are performed with various welding parameters such as rotational speed of tool, angle of tilt, and axial force using tool has taper pin profile. Experiments are carried out on twenty-seven joints that are made on 6061 and 7075 Al Alloy plates of 6.50 mm thick of same nature and tested for its tensile, impact and HV properties.

The purpose of this article, is to establish the existence of solution of infinite systems of fractional differential equations in space of tempered sequence mβ(ϕ) by using techniques associated with Hausdorff measures of noncompactness. Finally, we provide an example to highlight and establish the importance of our main result.

Flow is considered in the moving frame of reference with constant velocity along the wave. The developed mathematical model is presented by a set of partial differential equations. A numerical algorithm based on finite element method is implemented to evaluate the numerical solution of the governing partial differential equations in the stream-vorticity formulation.This paper is about the study of Numerical analysis of the peristaltic conveyance of a casson fluid in a skewed tube under the consideration of low Reynolds and long wavelength .The problem is discussed on the inclination angle and yield stress of a fluid are examined for different qualitative and quantitative effects on pressure and also the trapping bolus creation analyzed by changing various parameters, the equation of flux analyzed in a wave frame moving at wave speed. Expressions are derived for the frictional force, change in volume flow rate, rise and drop in pressure. The impact of frictional force on various parameters on the pumping characteristics and pressure flow curves discussed through graphs

Mobile Ad-Hoc Network (MANET) is a structure less and emerging technology in recent years. Generally, this structure forms a network with nodes with inherent characteristics, including resource heterogeneity, node reliability, etc. In this manuscript, we proposed a Multi-Agent-Based Zone Routing (MAZR) protocol for enhancing the performance of MANET. Our proposed MAZR is works based on the principle of packet forwarding through intermediate and zone leaders. It consists of multiple agents, which include static and dynamic mobile agents. The proposed implementation is done as follows: Initially discovering the zone leader’s .The discovered zone leaders are connected to the communication nodes. The communication nodes and zone leaders are associated for building the network backbones for achieving multicast routing .To the multicast, zone members are connected .The zone managements, backbone and highly mobile nodes are initiated. The proposed MAZR protocol comprises five types of agents: Path agent, Network control agent, Multicast control agent, Network launch agent, and Multicast control agents. The Path agent, Network control agent, and Multicast control agent are static, and Network launch agents and Multicast control agents are mobile. The future protocol's performance is determined using the experimental work based on the evaluation metrics like delay, power consumption, and network lifetime. The obtained results prove the future MAZR is far improved than the Zone-based Hierarchical Link Protocol and Zone Routing Protocol in all aspects and ensures flexibility with versatile multicast service.

In this paper, the methods called Newton’s interpolation and Aitken’s methods were developed and examined. We use Newton’s interpolation and Aitken’s methods to find the exact and analytic results for three different types of nonlinear ordinary differential equations (NLODEs) of first and second order through illustrative examples. By using the new method, we successfully handle some class of nonlinear ordinary differential equations of first and second order in a simple and elegant way compared to Newton’s and Lagrange methods in previous studies. One can conclude that Newton’s interpolation and Aitken’s methods are easy to yield and implement actual precise results.

The face expression recognition is used in countless application areas for example security, computer vision and medical science etc. The facial expressions are used to communicate in a non-verbal way (i.e., using eye contact, facial expressions etc.). Emotions play an important role in facial expression recognition which helps to identify what an individual is feeling. Various AI research in the field of facial recognition system is being carried since a decade. Many of the machine learning algorithms are also being used to identify the facial expression which helps them to train and test using the facial expression to get a correct output of the given expression. This paper presents a new facial expression recognition system, local directional feature structure (LDFS). LDFS uses different features of the face (i.e., eyebrows, nose, mouth, eyes). The face is detected and aligned using the edge detection. The task of the edge detection is to detect the face, face alignment and position variations of the face. The edge detection extracts the specific features for the identification of the emotions. Two types of datasets have been used for the qualitative and quantitative experiments on the face expression mainly the CK+ and Jaffe dataset. This approach for our model shows an improvement when compared to the existing system.

In this work, a general implicit block method (GIBM) with two points for solving general fifth-order initial value problems (IVPs) has been derived. GIBM is proposed by adopting the basis functions of Hermite interpolating polynomials. GIBM is presented to be suitable with the numerical solutions of fifth-order IVPs. Hence, the derivation of GIBM has been introduced. Numerical implementations compared with the existing numerical GRKM method are used to prove the accuracy and efficiency of the proposed GIBM method. The impressive numerical results of the test problems using the proposed GIBM method agree well with the approximated solutions of them using the existing GRKM method.

There are many results for analytic functions in the open unit disk U concerning subordination. Two subclasses of analytic functions in U are introduced using subordinations in U. The object of the present paper is to discuss some properties of functions belonging to these two subclasses.

The aim of this paper is introduced some examples of a G-bi-shadowing actions on the metric G-space, by study a sufficient conditions of actions to be G-bi-shadowing. We show the G-λ-Contraction actions, G-(λ,L)\textbf{-}Contraction action, and G-Hardy-Rogers contraction action are G-bi-shadowing by proved some theorems.

This paper delves into some significant performance measures (PMs) of a bulk arrival queueing system with constant batch size b, according to arrival rates and service rates being fuzzy parameters. The bulk arrival queuing system deals with observation arrival into the queuing system as a constant group size before allowing individual customers entering to the service. This leads to obtaining a new tool with the aid of generating function methods. The corresponding traditional bulk queueing system model is more convenient under an uncertain environment. The α-cut approach is applied with the conventional Zadeh's extension principle (ZEP) to transform the triangular membership functions (Mem. Fs) fuzzy queues into a family of conventional bulk queues. This new model focus on mixed-integer non-linear programming (MINLP) tenders a mathematical computational approach is known as (0 -1) variables. To measures the efficiency of the method, the efficient solution strategy plays a crucial role in the adequate application of these techniques. Furthermore, different stages of the α-cut intervals were analyzed and the final part of the article gives a numerical solution of the proposed model to achieve practical issues.

Mathematics has always been of great importance in various sciences, especially computer science. The mechanism used to embed various types of information in a host medical images to safeguard the privacy of the patient including the patient's name, doctor's digital signature is called watermarking. There are a lot of improved watermark algorithms, however, this information is susceptible to attack when the data are transferred over universal internet channels. This paper proposed a robust watermark algorithm that uses a Lifting Wavelet Transform (LWT) and two times of the Hessenberg Matrix Decomposition Method (HMDM) to embed a watermark in a chosen channel of the host image after performing the transform. The experimental results demonstrate that the improvement appears (higher robustness against JPEG compression attack) and good imperceptibility against some attacks, to evaluate the fineness of the original with watermarked images and the extracted watermark respectively.

In this paper, we propose a modern technique to derive fractional moment by using Caputo definition of fractional derivative. Such technique represents a modification of fractional moments on a certain type of function which is known by the power law distribution functions. The results have been obtained show that the obtained fractional moments within such functions have closed form.

In this study, we present a proposal aimed at estimating the finite population's mean of the main variable by stratification rank set sample StRSS through the modification made to generalized ratio-cum-product type exponential estimator. The relative bias PRB, Mean Squared Error Mse and percentage relative efficiencies PRE of the proposed modified estimator is obtained to the first degree of approximation. Conditions under which the proposed estimator is more efficient than the usual unbiased estimator, ratio, product type estimators, and some other estimators are obtained. Finally, the estimators' abilities are evaluated through the use of simulations, as showed that the proposed modified estimator is more efficient as compared to several other estimators.

This article sheds light on the single phrase, logical thinking, which came to be understood in so many diverse ways. To assist explain the many distinct meanings, how they arose, and how they are connected, we trace the emergence and evolution of logical thinking in mathematics. This article is also, to some extent, a description of a movement that arose outside of philosophy's mainstream, and whose beginnings lay in a desire to make logic practical and an essential part of learners' lives.

In this paper, we introduce the concept of proximally closed (or δ− closed) in the proximity spaces and study some of its properties.

In this paper, a new sparse method called (MAVE-SiER) is proposed, to introduce MAVE-SiER, we combined the effective sufficient dimension reduction method MAVE with the sparse method Signal extraction approach to multivariate regression (SiER). MAVE-SiER has the benefit of expanding the Signal extraction method to multivariate regression (SiER) to nonlinear and multi-dimensional regression. MAVE-SiER also allows MAVE to deal with problems which the predictors are highly correlated. MAVE-SiER may estimate dimensions exhaustively while concurrently choosing useful variables. Simulation studies confirmed MAVE-SiER performance.

Let X(V,E) be a simple graph with n vertices and m edges without isolated vertices. Denote by B=(bij)m×m the edge adjacency matrix of X. Eigenvalues of the matrix B, μ1,μ2,⋯,μm, are the edge spectrum of the graph X. An important edge spectrum-based invariant is the graph energy, defined as Ee(X)=∑mi=1|μi|. Suppose B′ be an edge subset of E(X) (set of edges of X). For any e∈B′ the degree of the edge ei with respect to the subset B′ is defined as the number of edges in B′ that are adjacent to ei. We call it as ε-degree and is denoted by εi. Denote μ1(X) as the largest eigenvalue of the graph X and si as the sum of ε-degree of edges that are adjacent to ei. In this paper, we give lower bounds of μ1(X) and μD′1(X) in terms of ε-degree. Consequently, some existing bounds on the graph invariants Ee(X) are improved.

Both NOx and soot emissions are major concerns in compression ignition (CI) engine with diesel fuel and the enhancement of variety of fuel and air can increase the performance of combustion engine. There are several methods to enhance the variety of air-fuel within the cylinder. Altering the geometry of piston bowl is one of the methods to improve the air-fuel mixture. This article proposes the effect of piston bowl geometry on the direct injection diesel engine performance and emissions, where various profiles of piston bowl referred as hemispherical combustion chamber (HCC), toroidal combustion chamber (TCC) and shallow depth combustion chamber (SCC) are designed using computer aided design and drafting (CADD) tool and ANSYS workbench is adopted for analysis. In addition, Karanja oil mixed with base fluid diesel at different volume fractions like0.2%, 0.3% and 0.4% and are calculated for their combination properties. Further, theoretical calculations are considered to determine the properties of Nano fluids which are later used as inputs for the analysis. Finally, CFD analysis is employed on different geometries at different fluid volume fractions and thermal analysis is done for piston bowl geometries with different composite materials like carbon fiber and armide fiber.

The road is a path that supports to connect different places. It plays a crucial role in our day-today life. Improper maintenance, overloading, climate conditions, and some other elements create distress on the roads. The common distresses are Potholes, cracking, and rutting. Manually detecting the distresses means human inspection is a messy and long time-consuming process. In recent past accidents on road is on the increase due to improper maintenance of road. Efficient methods of detecting pavement damages using image processing, machine learning and deep learning techniques have been a trending research topic. Image processing algorithms mainly include edge detection, region growing methods, and threshold segmentation operations for processing the pavement images and extracting crack information from the images. Machine learning methods of pavement crack detection adapts neural networks, supervised and unsupervised learning algorithms with pavement crack image as input. With Deep learning techniques, it has been possible to detect pavement cracks with greater accuracy. In this paper, we review the deep learning methods of pavement crack detection and propose a novel method to detect pavement cracks using Deep Learning with transfer learning. We also analyzed the performance of the proposed model for different network architectures namely, Google net, Alexnet and Resent and inferred that Google net gives better performance in detecting pavement cracks.

The aim of the recognition in the human activity is to recognize the actions of the individuals using a set of observations and their environmental conditions. Since last two decades, the research on this Human Activity Recognition (HAR) has captured the attention of several computer science communities because of the strength to provide support to different applications and the connection to different fields of study such as, human-computer interaction, healthcare, monitoring, entertainment and education. There are many existing methods like deep learning which have been used to develop to recognize the different activities of the human, but couldn’t identify the sudden change of the activities in the human. This paper presents a method using the deep learning methods which can recognize the specific identities and identify a change from one activity to another for the applications of the healthcare. In this method, a deep convolutional neural network is built using which the features are extracted for the collection of the data from the sensors. After which the Gated Recurrent Unit (GRU) captures the long-tern dependency between the different actions which helps to improve the identification rate of the HAR. From the CNN and GRU, a model of wearable sensor can be proposed which can identify the changes of the activities and can accurately recognize these activities. Experiment have been conducted using open-source University of California (UCI) HAR dataset which composed of six different activity such as lying, standing, sitting, walking downstairs, walking upstairs and walking. The CNN-based model achieves a detection accuracy of 95.99% whereas the CNN-GRU model achieves a detection accuracy of 96.79% which is better than most existing HAR methods.

The education is very important for improving the values of students in the society. Different types of features like school related features, student related features, parent related features and teacher related features are influencing the success rate of students in their education. Identification of best features from the huge set of features for analyzing the success or failure of a student is one important challenge to the research community and academicians. The set of features information is collected for preparing the student dataset also one difficult task in the prediction of student academic performance. We collected a student dataset of different schools that contains 4965 student’s information. The dataset contains information of 45 features of different categories such as school related features, student related features, parent related features and teacher related features. All features are not useful for predicting the academic performance of a student. The Data mining methods are applied in various research domains including education to extract hidden information from datasets. The feature selection algorithms are used to determine the best informative features by eliminating the irrelevant and redundant features. In this work, Relief-F Budget Tree Random Forest feature selection algorithm is used to identify the relevant features in the collected school dataset. Five different machine learning models are used to predict the efficiency of feature selection algorithm. The decision tree model shows best accuracy for student academic performance prediction compared with other models. The experimental results display that the RFBTRF algorithm identifies the best informative features for enhancing the accuracy of student academic performance prediction and also reduces the over-fitting issues. The experiment started with individual features and then continued with combination of different categories of features. It was observed that the accuracy of student academic performance prediction is decreased when some categories of features are added to other categories of features.

The uncommitted bandwidth of the spectrum must be expeditiously employed by the mobile users, since nowadays the mobile users are rising step by step. The major aim of the channel reservation process is to minimize the probability of call dropping and an effective channel assignment approach can significantly minimize such probability of call dropping. There is several channel allocation and assignment approaches has been presented. In our paper, the Channel Allocation using Prediction Approach (CAPA) algorithm is presented. The CAPA algorithm appropriates the channel reservation for mobile users in the destination cell before the mobile user travels into that particular cell. The channels are pre-reserved while the mobile users are travelling inside some distance of the novel cell bound. Channel Adoption approach and Queuing approach are employed in our CAPA algorithm for apportioning the channel to predict permanent and temporary mobile users. In channel Adoption approach, free channels choose from the fundamental pool and optimally apportioned to permanent user. For temporary user, queuing approach is implemented and these approaches are employed to minimize the probability of call dropping and apply the available channel bandwidth efficiently. Hence, the Performance of CAPA algorithm is improved while compared to other existing channel reservation algorithms.

Financial theorists believe that low accruals firms have higher returns compared to those with high accruals calling this relationship an accruals anomaly. Since the introduction of this concept, various studies have examined the factors causing accruals anomalies and their impact on corporate revenues. The current paper aims at investigating the impact of accruals anomalies on corporate profitability as well as the role of the corporate life cycle. Thus, the data was collected using 109 companies listed on the Tehran Stock Exchange data during the period 2012 to 2018. Then, the relationships were examined using regression modelling with a panel approach. The results indicate that there is no significant impact of life cycle on the relationship between accrual anomalies and profitability.

The purposes of this article are to introduce and characterize the notions of (i,j)-ω-α-open sets in bitopological spaces. Besides, It introduces and studies the concepts of (i,j)-ω-α-continuous functions. Furthermore, (i,j)-ω-α-connected and (i,j)-ω-α-set-connected functions are defined in bitopological spaces and some of their properties are studied.

Here, the comparison between Sinc method in combination with double exponential transformations (DE) and approximation by means of differential transform method (DTM) for nonlinear Hammerstein integral equations is considered. Convergence analysis is presented. Detection of effectiveness from various aspects such as run time, different norms, condition number are highlighted and plotted graphically. Results of two schemes are practically well, but in manner of separable kernel, DTM solution is more accurate and so fast.

This paper includes description of fabrication and characterization of two Schottky diodes differ in substrate material (n-type and p-type black Silicon). Schottky diodes were composed of (Ag /B-Si/n-Si/Al and Ag/B-Si/p-Si/ Al) respectively. Etching was achieved both electrochemical and photo--electrochemical etching processes. Different etching times and etching current densities were applied. Ag for front contact and Al for back contact were deposited by thermal evaporation method. I-V characteristics were plotted for the diode in dark forward and backward biasing at room temperature. The ideality factor and barrier height values were obtained. The barrier height values was(0,33-0,36) eV and the saturation current values (6,86-7,05) for the diode samples were obtained from the current-voltage (I-V) curves , The ideality factor (\textit{n})\textbf{ } values was(27.47-35.61), Schottky diodes at the Ag/BS or the dual metal-semiconductor junctions (Ag/BS/c-Si and c- Si/Al), of a diode ideally exhibit Ohmic features).

This paper deals with the generalized H(.,.,.,.)-φ-η-cocoercive operator and use its application via resolvent equation approach to solve the variational-like inclusion involving infinite family of set-valued mappings in semi-inner product spaces. Applying the generalized resolvent operator tech- nique involving generalized H(.,.,.,.)-φ-η-cocoercive operator, an equivalence between the set-valued variational-like inclusion problem and fixed point problem is established. A relationship between the set-valued variational-like inclusion problem and resolvent equation is also established. Using this equivalent formulation an iterative algorithm is developed that approximate the unique solution of the resolvent equation.

In our modern world, education is essential for developing high moral values and excellence in indi- viduals. But the spread of Covid-19 widely affects the student’s education, the majority of students have continued their education via online learning platforms. The academic performance of students has been sluggish across the globe during this pandemic. This problem is solved using a multiclass Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) model which predicts the student learning rate and behavior. This research aims to classify the students’ performance into low, medium, and high grades in order to assist tutors in predicting the low-ranking students. The student data log is collected from the Kaggle student performance analysis dataset and pre-processed to remove the noise and non-redundance data. By analyzing the pre-processed data, the CNN ex- tracts feature that are based on student interest and subjective pattern sequences. Then extracted features are filtered by the Minimum Redundancy Maximum Relevance(mRMR) method. mRMR selects the best features and dilutes the least one which handles each feature separately. The feature weights are measured by Stochastic Gradient Descent (SGD) and updated for better feature learn- ing by CNN. At the last stage, the Multi-class LDA classifier evaluates the result into categorized classes. Based on the prediction, the tutors can easily find the low ranks of students who need a high preference for improving their academic performance. Experiments showed that the proposed model achieves greater accuracy (96.5%), precision (094), recall (092), F-score (095), and requires less computation time than existing methods.

n this paper we present a dynamic model of the heart’s pumping blood. To predict blood flow and pressure applied to the area of blood vessels (arteries - veins - capillaries). The fluid dynamics model is derived from the continuum equation and the Navier-Stokes equations. For an incompressible Newton flow through a network of cylindrical vessels. This paper combined a model of pressure applied to the walls of blood vessels with a (regular - turbulent) flow model of blood, and the viscoelastic deformation of the walls (arteries - veins - capillaries) was studied with different blood density and prediction of the effect of the thickness of the rubber wall on the flow and the resulting pressure on the blood vessels. The results of this study show that the viscous elastic wall of the blood vessels allows more physiological prediction of pressure and vascular deformation, and that blood flow with varying intensity is more in the aorta than in the rest of the vessels, and this is subject to wide dilation.

SARS-CoV-2 and the consequential COVID-19 virus is one of the major concerns of the 21st century. Pertaining to the novelty of the disease, it became necessary to discover the efficacy of deep learning techniques in the quick and consistent discovery of COVID-19 based on chest X-ray and CT scan image analysis. In this related work, Prognostic tool using regression was designed for patients with COVID-19 and recognizing prediction patterns to make available important prognostic information on mortality or severity in COVID-19 patients. And reliable convolutional neural network (CNN) architecture models (DenseNet, VGG16, ResNet, Inception Net)to institute whether it would work preeminent in terms of accuracy as well as efficiency with image datasets with Transfer Learning. CNN with Transfer Learning were functional to accomplish the involuntary recognition of COVID-19 from numerary chest X-ray and CT scan images. The experimental results emphasize that selected models, which is formerly broadly tuned through suitable parameters, executes in extensive levels of COVID-19 discovery against pneumonia or normal or lung opacity through the precision of up to 87% for X-Ray and 91% intended for CT scans.

Twitter is an information platform that can be used by any internet user. The opinions of the Twitter Netizens are still random or unclassified. The technique for classifying sentiment analysis requires an algorithm. One of the classification algorithms is Stochastic Gradient Descent (SGD). The more training data provided to the machine, the accuracy of the classification function model formed by the machine is also higher. But in making representations into numerical vectors, the dimensions of data become large due to the many features. Feature optimization needs to be done to the training data by reducing the dimensions of the training data while maintaining high model accuracy. The optimization feature used is the TF-IDF (term frequency-inverse document frequency) feature extraction. sentiment analysis using TF-IDF feature extraction and stochastic gradient descent algorithm can classify Indonesian text appropriately according to positive and negative sentiment. Classification Performance using TF-IDF feature extraction and stochastic gradient descent algorithm obtained an accuracy is 85.141%.

Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett's method, and Durbin's method), The non-parametric model is estimated by using kernel smoothing (Nadaraya Watson), K-Nearest Neighbor smoothing and Median smoothing. The Flower Pollination algorithms were employed and structured in building the ecological model and estimating the semi-parametric regression function with measurement errors in the explanatory and dependent variables, then compare the models to choose the best model used in the environmental scope measurement errors, where the comparison between the models is done using the mean square error (MSE). These methods were applied to real data on environmental pollution/ air pollution in the city of Baghdad, and the most important conclusions that we reached when using statistical methods in estimating parameters and choosing the best model, we found that the Median-Durbin model is the best as it has less MSE, but when using flower The pollination algorithm showed that the Median-Wald model is the best because it has the lowest MSE, and when we compare the statistical methods with the FPA in selecting semi-parametric models, we notice the superiority of the FP algorithm in all methods and for all models.

A time series has been adopted for the numbers of people infected with the Covid-19 pandemic in Iraq for a whole year, starting from the first infection recorded on February 18, 2020 until the end of February 2021, which was collected in the form of weekly observations and at a size of 53 observations. The study found the quality and suitability of the autoregressive moving average model from order (1,3) among a group of autoregressive moving average models. This model was built according to the diagnostic criteria. These criteria are the Akaike information criterion, Bayesian Information Criterion, and Hannan \& Quinn Criterion models. The study concluded that this model from order (1,3) is good and appropriate, and its predictions can be adopted in making decisions.

This paper is considered with Kumaraswamy distribution. Numerical, non-Bayes and Bayes methods of estimation were used to estimate the unknown shape parameter. The maximum likelihood is obtained as a non-Bayes estimator. As well as, Bayes estimators under a symmetric loss function (De-groot and NLINEX) by using four types of informative priors three double priors and one single prior. In addition, numerical estimators are obtained by using Newton's method and the false position method. Simulation research is conducted for the comparison of the effectiveness of the proposed estimators. Matlab 2015 will be used to obtain the numerical results.

The progressing of countries is measured by scientific research, Iraq made a good progressing in recent three years, whereas the Ministry of Higher Education and Scientific Research announced that Iraq ranked fifty-fourth among the publishing countries in Scopus. The study aim is to look after if COVID 19 help Iraq to get this rank? The information was collected by questionnaires to general people and a phone interview with chief- editors of Iraqi journals had been made. The results showed that the COVID 19 play an important role in helping authors to collect any information about paper publishing in high impact journals and this is due to the ease of access to information that was previously restricted with a specific time and place and became available online because of the COVID 19 lockdown. Despite the seriousness of COVID 19 but as we know that always there is a positive aspect of everything happening around us, COVID 19 helped us to know an important advantage of e-learning that we do not know before.

The extension of Aamri and El Moutawakil's property [1] to set-valued mappings arena is given. Also, some common fixed point theorems for strict contractions are established. these theorems extend results in [1,8].

The aim of this paper is to introduce some new modified types of arrow domination by adding some conditions on the arrow dominating set or on its complement set. Co-independent arrow domination, restrained arrow domination, connected arrow domination, and complementary tree arrow domination are the main types of domination introduced here. More properties and bounds are discussed and applied to some graphs.

In this work, we are interested in the study of the limit cycles of a perturbed differential system in R2, given as follows \left\{ \begin{array}{l} \dot{x}=y, \\ \dot{y}=-x-\varepsilon (1+\sin ^{m}(\theta ))\psi (x,y),% \end{array}% \right. where ε is small enough, m is a non-negative integer, tan(θ)=y/x, and ψ(x,y) is a real polynomial of degree n≥1. We use the averaging theory of first-order to provide an upper bound for the maximum number of limit cycles. In the end, we present some numerical examples to illustrate the theoretical results.

Differential equations can be used to examine patrials of higher rank with varying coefficients in various regions of the Cartesian coordinate plane. Meanwhile, the researchers and scientists have N. Rajabov, A.S. Star and F.A. Nasim Adeeb Haneen, and others. As a result, while the coefficients of partial differential equations differ from those of partial differential equations, this research examined the partial differential equation based on its rank (fourth rank). Conditions are established for the production of their coefficients within the context of that equation. In multiple different scenarios involving these coefficients, a single solution for that partial differential equation. These circumstances were summed up in five theories.

The current analysis employs the Riccati and modified simple equation methods to retrieve new optical solitons for highly dispersive nonlinear Schr\"{o}dinger-type equation (NLSE). With cubic-quintic-septic law (also known as a polynomial) of refractive index and perturbation terms having cubic nonlinearity, 1-optical solitons in the form of hyperbolic, periodic, and rational are derived. the two schemes offer an influential mathematical tool for solving NLSEs in various areas of applied sciences.

The Burger‒Huxley equation as a well-known nonlinear physical model is studied numerically in the present paper. In this respect, the nonstandard finite difference (NSFD) scheme in company with the Richtmyer’s (3, 1, 1) implicit formula is formally adopted to accomplish this goal. Moreover, the stability, convergence, and consistency analyses of nonstandard finite difference schemes are investigated systematically. Several case studies with comparisons are provided, confirming that the current numerical scheme is capable of resulting in highly accurate approximations.

Digital compression of images is a topic that has appeared in a lot of studies over the past decade to this day. As wavelet transform algorithms advance and procedures of quantization have helped to bypass current compression of image standards such as the JPEG algorithm. To get the highest effectiveness in compression of image transforms of wavelet need filters which gather a desirable character's number i.e., symmetry and orthogonally. Nevertheless, wave design capabilities are restricted due to their ability to have all of such desirable characters at the same time. The multi-wavelet technology removes a few of the restrictions of the wavelet play more than the options of design and thus able to gather all desired Characters of transforming. Wavelet and multi-wave filter banks are tested on a larger scale with images, providing more useful analysis. Multiple waves indicate energy-compression efficiency (a higher compression ratio usually indicates a higher mean square error, MSE, in the compressed image). Filter bank Characters such as orthogonal and compact support, symmetry, and phase response are important factors that also affect MSE and professed quality of the image. The current work analyzes the multi-wave Characters effect on the performance of compression of images. Four multi-wavelength Characters (GHM, CL, ORT4) were used in this thesis and the compression of image performance of grayscale images was compared with common scalar waves (D4). SPIHT quantification device in stress chart and use of PSNR and subjective quality measures to assess performance. The results in this paper point out those multi wave characteristics that are most important for the compression of images. Moreover, PSNR results and subjective quality show similar performance to the best scalar and multi-waves. The analysis also shows that a programmer based on multi-band conversion significantly improves the perceived image quality.

In this research we stated and proved the some escape criteria theorems of the one parameter family of the transcendental meromorphic-functions F={fk(z)=k csc(z):k∈C and\ z∈C}. Furthermore, we used non-standard iterations: Mann, Ishikawa and Noor iterations in the complex plane. This research can be considered as an extension of [1].

We formulate a new bond portfolio optimization model as a two-stage stochastic programming problem in which a decision maker can optimize the cost of bond portfolio selection while deciding which bonds to sell, which bonds to hold, and which bonds to buy from the market, as well as determine the quantity of additional cash in period t under different scenarios and varying assumptions, The model proved its efficiency by finding the optimal values and giving an investment plan that, it will reduce the cost of the portfolio.

The aim of this research paper is to introduce the concept of bi-Γ-algebra space (bi-gamma algebra space). The concept of bi-μ-measurable set in a bi-Γ-algebra space is defined. With this concept, some properties of bi-Γ-algebra space are proved. We then define various separation axioms for bi-Γ-algebra space such as M0,M1, M2, M3, and M4; then the relationships between them are studied. In addition, the concept of measurable function between two bi-measurable spaces is introduced and some results are discussed.

The present study aims to develop a credit portfolio optimization model in the banking industry using a multilayer perceptron artificial neural network with a metaheuristic particle swarm algorithm. Risk, having its own complexity, is a basic concept in financial markets. Since there is no clear picture of risk realization, financial markets are in need of risk control and management approaches. With regard to data collection, this is a descriptive study and regarding the nature and purpose of the research, it is a developmental-applied one. The statistical population of the research includes all facility files of the last 10 years and the financial statements of a commercial bank, selected by census method. The risk criteria used in the models include fuzzy Value-at-Risk (VaR), fuzzy conditional Value-at-Risk (CVAR), fuzzy average Value-at-Risk(AVaR), fuzzy lower absolute deviation(LAD), fuzzy Semi-Kurtosis, and fuzzy Semi-Entropy. The research models were implemented using a three-layer perceptron artificial neural network. MATLAB software was used to conduct the research. The results indicate that the performance of the fuzzy average Value-at-Risk model is better than other models in evaluating optimal portfolios due to the lower mean squared error rate in generating more revenue. Therefore, it is recommended that the above model be used to optimize the credit portfolio.

This work deals with Kumaraswamy distribution. Maximum likelihood, Bayes and expansion methods of estimation are used to estimate the reliability function. A symmetric Loss function (De-groot and NLINEX) are used to find the reliability function based on four types of informative prior three double priors and one single prior. In addition expansion methods (Bernstein polynomials and Power function) are applied to find reliability function numerically. Simulation research is conducted for the comparison of the effectiveness of the proposed estimators. Matlab (2015) will be used to obtain the numerical results.

Let L be a left module over a ring \ S with identity. In this paper, the concept of primary isolated submodules is introduced. We look for relations between this class of submodules and related modules. A number of facts and characterizations that concern is gained. The aim of this work is to introduce and study the primary isolated submodules as a generalization of isolated submodules. A submodule A of L is primary isolated if for each proper B of A, there is a primary submodule C of L, B⊆C but\ A⊈C. Some properties are gained and we look for any relationship between this type of modules and other related modules.

This study aimed to explore the decision making of a prospective mathematics teacher in the process of improving a Lower Order Thinking Skills (LOTS) problem to be a Higher Order Thinking Skills (HOTS) problem.~This study involves 51 prospective mathematics teachers taking part in improving HOTS problems. Two students were chosen based on their uniqueness and quality of HOTS problems produced and their fluency in communication.~Semi-structured based task interviews were conducted to both participants in exploring the decision-making process-based. Furthermore, the data were analyzed qualitatively. The results showed that S1 was able to produce three-question related to one another, take two questions assess the reasonableness, finally decide one problem consisting of two items. S2 was able to generate three separate ideas, clarify the three ideas, and assess the three ideas and finally decide on one HOTS problem. S1 and S2 are still lack in involving Pedagogical Content Knowledge in assessing ideas especially. These results have an impact on the importance of developing a teaching model that improves the Decision making Strategy Furthermore, it is necessary to explore the decision-making process of pre-service and in-service mathematics teachers in developing the HOTS problems.

Throughout this manuscript, we show time periodic solutions to a linear diffusion parabolic equation with Diriclet condition. Based on the topological degree theorem, we prove a time periodic solutions of the system such that we found the fixed point when the domain of the solution is sufficiently small.

Measles is a respiratory system infection caused by a Morbillivirus genus virus. The disease spreads directly or indirectly through respiration from the infected person's nose and mouth after contact with fluids. The vast population of infects in developing countries is yet at risk. Generally, the mathematical model of Measles virus propagation is nonlinear and therefore changeable to solve by traditional analytical and finite difference schemes by processing all properties of the model like boundedness, positivity feasibility. In this paper, an unconditionally convergent semi-analytical approach based on modern Evolutionary computational technique and Padé- Approximation (EPA) has been implemented for the treatment of non-linear Measles model. The convergence solution of EPA scheme on population: susceptible people, infective people, and recovered people have been studied and found to be significant. Eventually, EPA reduces contaminated levels very rapidly and no need to supply step size. A robust and durable solution has been established with the EPA in terms of the relationship between disease-free equilibrium in the population. When comparing the Non-Standard Finite Difference (NSFD) approach, the findings of EPA have shown themselves to be far superior.

Diabetic retinopathy (DR) is a serious retinal disease and is considered the leading cause of blindness and is strongly associated with people with diabetes. Ophthalmologists use optical coherence tomography (OCT) and retinal fundus imagery to assess the retinal thickness, structure, and also detecting edema, bleeding, and scarring. Deep learning models are used to analyze OCT or fundus images, extract unique features for each stage of DR, then identify images and determine the stage of the disease. Our research using retinal fundus imagery is used to identify diabetic retinopathy disease, among others, using the Convolutional Neural Network (CNN) method. The methodology stage in the study was a green channel, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological close, and background exclusion. Next, a segmentation process is carried out that aims to generate binary imagery using thresholding techniques. Then the binary image is used as training data conducted epoch as much as 30 times to obtain an optimal training model. After testing, the deep learning method with the CNN algorithm obtained 95.355\% accuracy in the identification of diabetic retinopathy disease based on fundus image in the retina.

In this paper we consider 1//∑nj=1(Ej+Tj+Cj+Uj+Vj) problem, the discussed problem is called a Multi objectives Function (MOF) problem, As objective is to find a sequence that minimizes the multiple objective functions, the sum earliness, the tardiness, the completion time, the number of late jobs and the late work. The NP-hard nature of the problem, hence the existence of a polynomial time method for finding an optimal solution is unlikely. This complexity result leads us to use an enumeration solution approach. In this paper we propose a branch and bound method to solve this problem. Also, we use fast local search methods yielding near optimal solution. We report on computation experience; the performances of exact and local search methods are tested on large class of test problems.

In this paper, VANET protocol is used to reduce traffic jams and accidents on the roads. The proposed algorithm is dependent on the wireless network between cars. The system is part of a wireless control node located at the intersection, which determines the optimal values of the phases of traffic lights. The protocol used in this research provides traffic fluency when compared to adaptive systems based on the use of cameras. It has also been developed as an integrated system validation simulator. The simulation framework consists of a realistic vehicle navigation model and a wireless network simulator. The proposed system designed as a work system that can be analyzed for peak hours and we got acceptable results. Average delay, fuel consumption and pollution are significantly reduced.

Science literacy is very important to be prepared for the younger generation in order to solve problems in life. STEM integrated guided inquiry is a strategy that can be done to improve scientific literacy in the learning process. We examine the effect of the integrated STEM guided inquiry model on students' scientific literacy abilities. A total of 66 middle school students participated in this study. Data were collected from one question instrument and then analyzed by normality test (chi square), homogeneity test for variance and hypotesis test (t-test). STEM integrated guided inquiry model has a significant effect with significance level 1\% (α= 0,99) is obtained tcount 7,9 > ttable 2,66. Based on the result obtained that STEM integrated guided inquiry model that can improve literacy science skills compared to conventional model

The aim of this study is analysis time series with using (Box and Jenkins ) method by identification , estimation, diagnosis, checking of model ,forecasting to find the beast forecasting model to the number of patient  with cardiac in Misan province by using the monthly data of the period (2005-2016)  by using SPSS version (26).The result of data analysis show that the  proper and suitable model is  Autoregression of order ARIMA (1,1,0) .According to this model the study forecast the numbers of patients with cardiac diseases the next years in monthly , so the forecasting values represented the scours time series data  that deal to the efficiency of the model.

The researchers faced challenges in the outlier detection process, mainly when deals with the high dimensional dataset; to handle this problem, we use The principal component analysis. Outlier detection or anomaly detection, with local density-based methods, compares the density of observation with the surrounding local density neighbors. We apply the outlier score as a measure of comparison. In this research, we choose different density estimation functions and calculated different distances. Weighted kernel density estimation with adaptive bandwidth for multivariate kernel density estimation(Gaussian) considered the KNN and RNN. KNN is considered too for the Epanenchnikov kernel density estimation. Lastly, we estimate the LOF as a base method in detecting outliers. Extensive experiments on a synthetic dataset have shown that RKDOS and EPA are more efficient than LOF using the precision evaluation criterion.

The K-Nearest Neighbor (KNN) method is often used by researchers for the classification process because it has a relatively great level of accuracy, however it also has a weakness which is sensitive of the noises. This research is aims to introduce an object recognition (identification) system of fingers leaves by classified using the KNN method. To resolves the weaknesses of the KNN method, the researcher has used the Local Binary Pattern (LBP) method to extract features of the leaves. For the comparison in feature extraction, the researcher has used the Gray Level Co-Occurrence Matrix (GLCM) method. The data that were used on this research are papaya leaves and chaya leaves (with the labels such as good and damage forms). In this research, an experimental design has been carried out that was differentiated by according to the comparison (of ratio) between training data and testing data (NI/Np), there were 90 training data and 45 testing data, where the feature extraction method used the 10 of features. Experimentally, it was shown that by using the ratio NI/Np = 67\%:33\%, the performance or system performance for classifying the images of fingers leaves by using the LBP extraction method showed that training data was obtained the results close to 95% and testing data was obtained the results close to 76%, while by using the GLCM extraction showed that training data was obtained the results close to 83\% and testing data was obtained the results close to 58\%.

Our research includes studying the case 1//F(∑Ui,∑Ti,Tmax) minimized the cost of a three-criteria objective function on a single machine for scheduling n jobs. and divided this into several partial problems and found simple algorithms to find the solutions to these partial problems and compare them with the optimal solutions. This research focused on one of these partial problems to find minimize a function of sum cost of (∑Ui) sum number of late job and (∑Ti) sum Tardiness and (Tmax) the Maximum Tardiness for n job on the single machine, which is NP-hard problem, first found optimal solutions for it by two methods of Complete Enumeration technique(CEM) and Branch and Bounded ((BAB)). Then use some Local search methods(Descent technique(DM), Simulated Annealing (SA) and Genetic Algorithm (GA)), Develop algorithm called ((A)) to find a solution close to the optimal solution. Finally, compare these methods with each other.

In this paper, a generalized (1+2)-dimensional Jaulent-Miodek equation with a power law nonlinearity is examined, which arises in numerous problems in nonlinear science. The computed conservation laws reside in enormously crucial areas both at the foundations of nonlinear science such as biology, physics and other related areas. Exact solutions are acquired using the Lie symmetry method. In addition to exact solutions, we also present conservation laws. The arbitrary functions in the multipliers lead to infinitely many conservation laws.

A third-order fractional ordinary differential equation (FrODE) is very important in the mathematical modelling of physical problems. Generally, the third-order ODE is solved by converting the differential equation to a system of first-order ODEs. However, it is a lot more efficient in terms of accuracy, a number of function evaluations as well as computational time if the problem can be solved directly using numerical methods. In this paper, we are focused on the derivation of the direct numerical methods which are one, two and three-stage methods for solving third-order FrODEs. The RKD methods with two- and three stages for solving third-order ODEs are adapted for solving special third-order FrDEs. Numerical examples have been evaluated to show the effectiveness of the new methods compared with the analytical method. Numerical experiments are carried out to verify the accuracy and efficiency of the proposed methods. Applications of proposed methods are also presented which yield impressive results for the proposed and modified methods. The numerical solutions of the test problems using proposed methods agree well with the analytical solutions. From the numerical results obtained using proposed methods, we can conclude that the proposed methods in which derived or modified in this paper are very efficient.

Wireless networks are of high significance in current telecommunication systems, and in order to improve these systems, Software-Defined Networks (SDN)is used to centrally monitor and control the whole network with the help of a controller. The design of an SDN-based network is needed to identify the optimal amount of controllers for improving the performance of the network. The controller placement problem is determined on propagation latency but it failed to consider the load balancing, fault tolerance, bandwidth consumption and data transmission rate. Plan to develop a novel technique called Demming Regressive Multiobjective Dragonfly Optimized Controller Placement (DRMDOCP) for an optimal number of controller placement for enhancing network performance during different topologies. By applying DRMDOCP, the optimum number of controllers are selected and placed into the network to improve the overall network's performance. Therefore, a delay minimization-based controller placement strategy is extremely preferred. The simulation of the DRMDOCP technique and existing methods is conducted using a network topology dataset and different performance metrics. The simulation results demonstrate that the DRMDOCP increases the packet delivery, throughput and reduces the average latency, packet drop when compared to the state-of-the-art method.

In this paper, we define and study some separation axioms on Γ-algebra space (gamma algebra space). The relationships between various separation axioms in Γ-algebra space are proved. In addition, the measurable function between two measurable spaces is introduced and some results are discussed.

We introduce '{C}iri'{c} type ZR-contraction to investigate the existence of single fixed point under a binary relation. In the sequel we demonstrate that variety of contractions are obtained as consequences of our contraction. Also we provide illustrative examples to demonstrate the significance of '{C}iri'{c} type ZR-contraction in the existence of fixed point for discontinuous map via binary relation. The paper is concluded by applications to solve an integral equation and a nonlinear matrix equation.

In this study, the mathematical model of four differential equations for organisms that describe the effect of anti-predation behavior, age stage and toxicity have been analyzed. Local bifurcation and Hopf bifurcation have been studied by changing a parameter of a model to study the dynamic behavior determined by bifurcation curves and the occurrence states of bifurcation saddle node, transcritical and pitch fork bifurcation. The potential equilibrium point at which Hopf bifurcation occurs has been determined and the results of the bifurcation behavior analysis have been fully presented using numerical simulation.

In recent years, integrated reverse supply chain practices have been adopted by companies that desire to reduce the negative environmental and social impacts within their supply chains. models and solutions assisted by industry 4.0 technologies have been developed to transform products in the end of their life cycle into new products with different use. There are several methods with different technologies to recycle the wastes, which have been selected and weighted based on the indicators of the industry 4.0 revolution and the wastes sent to recycling centers based on the technology weight. The understudy model is multi-objective, including minimizing transportation costs and environmental effects and maximizing customer response demand. The whale optimization algorithm and the NSGA-II algorithm were also used to solve this model. The results obtained from whale optimization and genetic algorithms have been comprised of each other through comparative indicators of quality, dispersion, uniformity, and solving time. The results showed that the whale algorithm has a higher ability to explore and extract possible points and achieve optimal solutions in all cases. The NSGA-II algorithm was also superior to the whale algorithm in terms of uniformity and solving time. The investigation of changes in solving time with increasing problem size was another confirmation of the NP-hard nature of the understudied problem.

Health-care systems (patient monitoring and diagnosis systems) have recently attracted the interest of researchers. Intelligent software has been used as an agent in this field where research addresses the areas of electronic health (E-health) to develop and improve health care systems and to reduce the effort and time on health users (physicians, nurses) and to follow up on the condition of patients, especially the elderly, and also Intelligent software agents are using a result when most hospitals are out of capacity.  The major goal of this study is to learn how we may use intelligent software for  E-Health to assist physicians, nurses, and other health practitioners in collecting and tracking patient data on a daily basis in order to enhance treatment decisions. How can we achieve this in real-time in a dynamic and non-deterministic environment? As a result, we have built a model-based system such as the Extensible Beliefs Desires and Intentions (EBDI) model as architecture for located autonomic software agents in run-time under dynamic and non-deterministic environments circumstances in this study. We have formalized the EBDI agent in E-Health as an online observer to monitor the state of a  patient linked to sensors (thermometer, glucose meter, oxygen meter, etc.). This agent filters the data (sensor readings) and determines which readings are normal or abnormal before sending the patient's information to the administrative agent, which is a different type of EBDI agent. In addition, the system proposes a treatment plan based on the sensor readings and necessitates the participation of a human doctor in emergency situations. This study proposes developing a monitoring system to follow up (monitor) the health status of patients based on a multi-agent-system (MAS) with the Clustering process (using the K-mean Algorithm), where the system is designed to support warnings and alerts in abnormal health conditions and to call for a doctor's intervention in emergencies (when it is necessary).

A split common fixed point and null point problem (SCFPNPP) which includes the split common fixed point problem, the split common null point problem and other problems related to the fixed point problem and the null point problem is studied. We introduce a Halpern--Ishikawa type algorithm for studying the split common fixed point and null point problem for Lipschitzian J−quasi pseudocontractive operators and maximal monotone operators in real Banach spaces. Moreover, we establish a strong convergence results under some suitable conditions and reduce our main result to the above-mentioned problems. Finally, we applied the study to split feasibility problem (FEP), split equilibrium problem (SEP), split variational inequality problem (SVIP) and split optimization problem (SOP).

The wavelet shrink estimator is an attractive technique when estimating the nonparametric regression functions, but it is very sensitive in the case of a correlation in errors. In this research, a polynomial model of low degree was used for the purpose of addressing the boundary problem in the wavelet reduction in addition to using flexible threshold values in the case of Correlation in errors as it deals with those transactions at each level separately, unlike the comprehensive threshold values that deal with all levels simultaneously, as (Visushrink) methods, (False Discovery Rate) method, (Improvement Thresholding) and (Sureshrink method), as the study was conducted on real monthly data represented in the rates of theft crimes for juveniles in Iraq, specifically the Baghdad governorate, and the risk ratios about those crimes for the years 2008-2018, with a sample size of (128) (Sureshrink) The study also showed an increase in the rate of theft crimes for juveniles in recent years.

In this paper, the seismic performance of the structure is analyzed by introducing a new angular knee element. Cyclic loads are applied to determine the seismic performance. The result is compared with a vertical knee element. The angular knee element showed better seismic performance as compared to the vertical knee element. The new short knee element studied in two positions including 1- a vertical knee element perpendicular to cross-brace is called VSKE 2- An angular knee element that is called ASKE. Using reduced knee section specimens for better performance. Effect of Angular knee element in knee-braced frames (KBFs) under Cyclic Loading was investigated by testing using a half scale specimen mounted on a reaction frame at two positions including horizontally for VSKE and inclined for ASKE. For experimental tests, specimens were loaded by using a hydraulic actuator. Finite element modelling was used for the simulation of detailed behavior using a verified experiment base model. It has been shown that the performance of knee element fuses can improve the ductile behavior of knee braced frames. Based on the results of vertical and angular cases, it has been shown that an increase of angle between main cross brace and short knee element (incline specimen) can reduce maximum equivalent plastic strain (EPEQ) in the web of knee element fuse.

In this paper, we found the estimation of the unknown parameter δ when ϑ is a known parameter in the Pareto distribution. First, we get the maximum probability estimators(MLEs) for unknown parameters. We have obtained the Bayes Estimators of unknown parameter δ using Lindley's approximation. A Monte Carlo simulation is performed and used a programming language R to compare the performance of the method used, and the data set was analyzed for illustration purposes.

We demonstrate that the C−class functions, pair (h,f) upper-class functions, cone C−class functions, 1−1− up-class functions, multiplicative C−class functions, inverse−C−class functions, CF−simulation functions, C∗−class functions are powerful and fascinating weapons for the generalization, improvement, and extension of considerable conclusions obtained in the fixed point theory. Towards the end, we point out some open problems whose answers could be interesting.

Given the importance of maximizing influence in a social network, studies in this field often seek to find the nodes that have the most influence on the social network if designated as primary seeds. In this study, to reduce the complexity of computation algorithms, the problem is divided into several groups that aim to find a group of influential people among users of a social network. In this paper, a framework is introduced for solving the problem of influence maximization, which is based on the member clustering by the K means method, to improve the classification of network users, the data are weighted and the problem is modeled and analyzed as an evolutionary game. Finally, calculate its evolutionary stable strategy. This framework has been tested on real social network data for Abrar University students and we have achieved results such as increasing classification accuracy, reducing error function and finding a stable strategy in the community.

J.Koneiczny in [8] introduced the new notion ∼n notion of conjugacy in semigroups. In this paper, we count the number of conjugacy classes in Partial Transformation semigroup P(A) for an infinite set A with respect to ∼n notion of conjugacy.

Uncertainty exists in many real-life engineering and mechanical problems. Here, we assume that uncertainties are caused by intervals of real numbers. In this paper, we consider the interval nonlinear programming (INLP) problems where the objective function and constraints include interval coefficients. So that the variables are deterministic and sign-restricted. Additionally, the constraints are considered in the form of inequalities. A basic task in INLP is calculating the optimal values range of objective function, which may be computationally very expensive. However, if the boundary functions are available, the problems become much easier to solve. By making these assumptions, an efficient method is proposed to compute the optimal values range using two classic nonlinear problems. Then, the optimal values range are obtained by direct inspection for a special kind of interval polynomial programming (IPP) problems. Two numerical examples are given to verify the effectiveness of the proposed method.

The learning process of blood coagulation practicum for circulatory system material in Biology Education students aims to determine the ability of generic science skills. Descriptive method used in this study with research subjects consisted of 58 6th semester students. Data were obtained using essay tests. Achievement of generic science skills in students is focused on five aspects of generic science skills with research results that show, direct observation 98.83% (very good), symbolic language 38.79% (very poor), modeling 90.95% (very good), logical inference 39.51 % (very little), logic framework 94.90% (very very), with an overall average yield (75.6%) in the medium category. this generic science ability is still needed intensive guidance to increase shn for prospective teacher students biology.

Coronavirus Disease 2019 (Covid-19) is a collection of viruses that can infect the respiratory system. This virus only causes mild respiratory infections, such as the flu. The process of handling Corona infection patients is indeed much different from other disease patients. Covid-19 can cause various symptoms in its sufferer. These symptoms depend on the type of corona virus that attacks, and how serious the infection is. The design of this application uses DFD and ERD modeling which is built using the web-based PHP programming language and uses a MySQL database. The assessment of the efficiency level of COVID-19 was carried out in one of the provinces in Indonesia, namely in the province of Aceh, using samples from 12 existing hospitals. All the hospitals that we have taken data every day will continue to increase and decrease. This application is built using the Data Envelopment Analysis (DEA) method, where the search process is determined by the lindo software. The input criteria in this application are suspected patients, probable patients, confirmed patients, and patients being treated. While the output of this application is the patient recovers, and the patient dies. All the data we get is real data that we get directly from the hospital and the results are confirmed via the Internet. All calculation processes use predetermined formulas. The computational result obtained from the DEA CCR Model on the 8 DMUs is 1, it is the gain from dividing the output variable by the input variable, then the results obtained from 8 DMUs are efficient and 4 DMUs are inefficient.

Palm oil, scientifically known as Elaeis guineensis, is a rapidly growing commercial sector in Southeast Asia with a diverse economic composition. Palm oil plantations are crucial in economic activities and growth, as they generate employment in managing the palm oil quality. However, the lack of nutrients can affect the growth and quality of the crops. The manual detection of palm leaf nutrient deficiency can be one of the challenges as the visual symptoms of the deficiency demonstrate a similar representation. Thus, in this study, the palm leaf nutrient deficiency detection using Convolutional Neural Network (CNN) is proposed. CNN or ConvNet is a branch of deep neural networks in Deep Learning that is commonly used in analysing images and has proven to produce better feature extraction from dataset. A total of 350 images of healthy leaf and six types of palm leaf nutrient deficiency are Nitrogen, Potassium, Magnesium, Boron, Zinc, and Manganese were tested. The application of CNN to a variety of testing datasets returned good detection accuracy at 94.29\%. It can be deduced that the proposed implementation of CNN for palm leaf nutrient deficiency detection is found to be successful. Nonetheless, the number of datasets could be increased in the future to improve the detection performance.

In this paper, we first define the concept of convexity in G-metric spaces. We then use Mann iterative process in this newly defined convex G-metric space to prove some convergence results for some classes of mappings. In this way, we can extend several existence results to those approximating fixed points. Our results are just new in the setting.

Supply chain network design is one of the key issues in strategic chain planning that refers to the supply chain network configuration and as an infrastructure issue in its management, will have lasting effects on other tactical and operational decisions. In other words, the proper design of the supply chain network leads to the achievement of an optimal structure, which makes effective and competitive supply chain management possible. In this study, a problem of selecting a green supplier in terms of sustainability, under uncertainty based on three economic, environmental and social responsibility dimensions are studied by a case study of Sazeh Gostar Saipa Photovoltaic Company active in the pv industry. One of the environmental dimensions of the problem is the design of an efficient and flexible model for evaluating and selecting suppliers based on environmental indicators. One of the features of the proposed model is the use of environmental criteria in the process of selecting a green supplier and also the flexibility of the model in the number of sub-criteria. In this research, the theory of Rough sets has been used to find the weight of sub-environmental criteria. The obtained results confirm the efficiency of the multi-objective mathematical planning model in this research in evaluating suppliers and also using the theory of Rough sets to weight environmental indicators to achieve the above objectives.

The fishing industry has become an important income source in the world. However, fish diseases are considered a serious problem among the fishermen as it tends to spread quickly through the water. In decades, fish diseases have been diagnosed manually by the naked eyes of experienced fish farmers. Despite being time-consuming since some lab works are required in determining the relevant microorganisms that cause the diseases, this classical method most often leads to an inaccurate and misleading result. Accordingly, a fast and inexpensive method is therefore important and desirable. Convolutional Neural Network (CNN) performance has recently been demonstrated in a variety of computer vision and machine learning problems. Thus, a study on fish diseases detection using CNN is proposed. A total of 90 images of healthy leaf and two types of fish diseases which are White spot and Red spot was tested. The application of CNN to a variety of testing datasets returned good detection accuracy at 94.44\%. It can be inferred that the CNN is relatively good in detecting and classifying the type of diseases among infected fishes. Regardless, a study with a better number of datasets could be done in the future to improve the detection performance.

The order picking problem is one of the key elements in warehouse management.  The challenge increases during the new norm when orders can be made by going to the shop and also via online that results in high uncertainty in order volume. Despite that, customer expectation remains on fast delivery which requires the selling organizations to be able to provide fast and efficient service to meet the demand from customers.  In achieving this, among the contributing factors is efficient warehouse management especially in order picking, storage assignment, sufficient resource allocation, adequate manpower handling and proper tasking allocation. Thus, in this paper, a model for order picking is modified by considering the limited picking capacity of the Order Pickers (OP), the S-shaped route in the warehouse plan and the need for complete order (all demanded items are picked). The modified model is adapted as a Dynamic Programming problem with the objective of minimizing the time taken (through minimizing distance travelled) in picking each order. The results show that testing with a set of secondary data, the modified model shows a reduction of 24.19\% in travel distance compared to using Shortest Path Problem (SPP) and Traveling Salesman Problem (TSP). At the same time, the application of the modified model using the real data shows a reduction of 11.6\% in the travelled distance as well as more quality task allocation among the OPs.

We introduce a novel distance structure called a b−interval metric space to generalize and extend metric interval space. Also, we demonstrate that the collection of open balls, which forms a basis of a b−interval metric space, generates a T0−topology on it. Further, we define topological notions like an open ball, closed ball, b−convergence, b−Cauchy sequence and completeness of the space on a b−interval metric space to create an environment for the survival of a near fixed point and a unique equivalence class of near fixed point. Towards the end, we introduce notions of interval circle, fixed interval circle, its equivalence class and established the existence of a near fixed interval circle and its equivalence interval C−class of near fixed interval circle to study the geometric properties of non-unique equivalence C−classes of nearly fixed interval circles.

Capital markets play an important role in the economy and naturally, the proper functioning of the capital market will play a key role in ensuring economic growth. So, if capital markets are efficient, economic development will be ensured. On the other hand, since the Iranian stock market is currently very popular among investors, it is undoubtedly important to examine the behavioral biases of individual investors. Therefore, in this study, the effect of positive self-control behavioral bias on financial security was investigated with the moderating role of financial literacy and risk aversion of individual investors. For this purpose, the statistical population consisted of individual investors, traders and brokers of Tehran Stock Exchange who are directly participating in the market. Also, the statistical sample was calculated to be 421 using Cochran’s formula. A structural equation model using PLS method was used to analyze the data. The results showed that the self-control variable has a significant positive effect on risk aversion and financial security. Also, by using the financial literacy variable as a moderating variable, the intensity of the relationship between self-control and financial security of individual investors increases; that is, the financial literacy of individual stock market investors intensifies the positive relationship between self-control and security of their financial behavior. In addition, by using risk aversion as moderating variable, the intensity of the relationship between self-control and financial security of individual investors is reduced. This means that the risk aversion of individual stock market investors reduces the positive relationship between self-control and the security of their financial behavior.

In this manuscript, we bring into play the essence of a new class of auxiliary functions, C-class functions, and exhibited some fixed point results. Notably, in this draft, we come up with the idea of modified ZF-contractions and enquire the existence and uniqueness of fixed points of such operators in the framework of θ-metric spaces. Concerning the interpretation of the achieved results, some non-trivial examples are also studied. From obtained theorems, we derive several related fixed point results in usual metric spaces and θ-metric spaces.

Radial basis function pseudospectral method is applied to obtain the solution for nonlinear Phi-four time dependant equation with nonhomogeneous initial and boundary conditions.  In this method, the efficient pseudospectral technique is combined with radial basis function to get the best of it. In the proposed method, the radial basis kernels are used to discretize the space derivatives in the Phi-four equation where as a time stepping technique is used to accord with the temporal part of the solution. The given Phi-four equation is transformed into a set of ordinary equations. An ode solver is used to solve the ordinary equations. An effective approach is used to choose the value of the shape parameter for radial basis function. Numerical results are presented to check the validity and accuracy of the method to solve the Phi-four equation.

Large parts of the world's forests are threatened by fires. These fires happen continuously every month around the globe. They are very costly to society and cause serious damage to the ecosystem. This raises the necessity to build a detection system to intervene early and take action. Fire and smoke have various colours, textures, and shapes, which are challenging to detect. In the modern world, neural networks are used extensively in most fields of human activities. For the detection of fire and smoke, we suggest a deep learning technology using transfer learning to extract features of forest fire and smoke. We used a pre-trained Inception-ResNet-v2 network on the ImageNet dataset to be trained on our dataset which consists of 1,102 images for each fire and smoke class. The classification accuracy, precision, recall, F1-Score, and specificity were 99.09\%, 100\%, 98.08\%, 99.09\%, and 98.30\%, respectively. This model has been deployed on a Raspberry Pi device with a camera. For real-time detection, we used the Open CV library to read the camera stream frame by frame and predict the probability of fire or smoke.

‎This work aims to introduce a numerical approximation procedure based on an operational matrix of block pulse functions‎, ‎which is employed in solving integral-algebraic equations arising from the diffusion model‎. ‎It is known that the integral-algebraic equations belong to the class of singular problems‎. ‎The main advantage of this method is the reduction of these singular systems by using an operational matrix to linear lower triangular systems of algebraic equations‎, ‎which is non-singular‎. ‎An estimation of the error and illustrative instances are discussed to evaluate the validity and applicability of the presented method‎.

We propose a multi-objective machine scheduling problem (MSP) in this study. The sum of total flow time, total tardiness, total earliness, and total late work is the topic under discussion. With an arbitrary release date, This paper offers a theoretical analysis, discussion, and proofs for a number of special instances that apply to our topic.

In this paper, a new analytical method is introduced to find the general solution of linear partial differential equations. In this method, each Laplace transform (LT) and Sumudu transform (ST) is used independently along with canonical coordinates. The strength of this method is that it is easy to implement and does not require initial conditions.

In this paper, we consider the problems of suboptimal control for a class of fractional-order optimal control problems with multi-delay argument. The fractional derivative in these problems is in the Caputo sense. To solve the problem, first by a suitable approximation, we replace the Caputo derivative to integer order derivative. The optimal control law consists of an accurate linear feedback term and a nonlinear compensation term which is the limit of an adjoint vector sequence, is obtained by a sensitivity approach. The feed back term is determined by solving Riccati matrix differential equation. By using a finite sum of the series, we can obtain a suboptimal control law. Finally, numerical results are included to demonstrate the validity and applicability of the present technique.

Quality control Charts were used to monitor the number of infections with the emerging corona virus (Covid-19) for the purpose of predicting the extent of the disease's control, knowing the extent of its spread, and determining the injuries if they were within or outside the limits of the control charts. The research aims to use each of the control chart of the (Kernel Principal Component Analysis Control Chart) and (K- Nearest Neighbor Control Chart). As (18) variables representing the governorates of Iraq were used, depending on the daily epidemiological position of the Public Health Department of the Iraqi Ministry of Health. To compare the performance of the charts, a measure of average length of run was adopted, as the results showed that the number of infection with the new Corona virus is out of control, and that the (KNN) chart had better performance in the short term with a relative equality in the performance of the two charts in the medium and long rang

In reducing the effects of collinearity, the ridge estimator (RE) has been consistently demonstrated to be an attractive shrinkage method. In application, when the response variable is binary data, the logistic regression model (LRM) is a well-known model. However, it is known that collinearity negatively affects the variance of maximum likelihood estimator of the LRM. To address this problem, a logistic ridge estimator was proposed by several authors. In this work, a Jackknifing logistic ridge estimator (NJLRE) is proposed and derived. The Monte Carlo simulation results recommend that the NJLRE estimator can bring significant improvement relative to other existing estimators. Furthermore, the real application results demonstrate that the NJLRE estimator outperforms both LRE and MLE in terms of predictive performance.

latent variable models define as a wide class of regression models with latent variables that cannot be directly measured, the most important latent variable models are structural equation models. Structural equation modeling (SEM) is a popular multivariate technique for analyzing the interrelationships between latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. In general, structural equation models includes a measurement equation to characterize latent variables through multiple observable variables and a mean regression type structural equation to investigate how the explanatory latent variables affect the outcomes of interest. Despite the importance of the structural equations model, it does not provide an accurate analysis of the relationships between the latent variables. Therefore, the quantile regression method will be presented within the structural equations model to obtain a comprehensive analysis of the latent variables. we apply the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The posterior inference is performed using asymmetric Laplace distribution. The estimation is done using the Markov Chain Monte  Carlo technique in Bayesian inference. The simulation was implemented assuming different distributions of the error term for the structural equations model and values for the parameters for a small sample size. The method used showed satisfactorily performs results.

The purpose of this article is to analyze the policies and regulatory tools of the Central Bank on financial stability in the Iranian banking system . The method of analysis is descriptive - survey . Data were collected through interviews with experts . The validity and reliability of the research tool were confirmed by marketing professors and experts . Data analysis - using software Lisrel Done . Seven main influential factors from the perspective of experts, which include; Ratio of non-current receivables to total payment facilities (NP) , Cash reserves in assets - bank (CR) , Capital adequacy (AC) , The real exchange rate (RER) , Liquidity (LY) , Leverage (DA) , Atbardakhly paid by banks to the private sector - and (DCP) Was agreed upon by them . Test results KMO That sufficient sample - properly selected . Bartlett's test of sphericity also at the level of 99 percent, which shows a significant correlation - in the community is not zero . That is, the seven factors studied had a significant impact on the financial stability of the banking system . Also, the results of varimax rotation of seven factors showed that their eigenvalues are higher than one . Products of factor loadings higher than 35/0 are . In order to confirm homogeneity Of items - forming any of the scale in terms of content and infrastructure of the five-factor confirmatory factor analysis on the causes - were carried out . To evaluate the model - factor analysis identified several characteristics fitness There is . In the present article, the ratio of chi-square or chi-square to the ratio of freedom (x 2 / df) Root mean square residue - Display RMR As well as a good fit index GFI And the adjusted value of the fitness index for the degree of freedom AFGIWas used . Results showed that all the characteristic elegance at an acceptable level were significantly - have given - in this letter with the factor structure of this scale is a good fit and statements - this scale are consistent with the underlying structures .

In this paper, we introduce and characterize the notion of fuzzy continuous functions and fuzzy homeomorphic topological spaces of fuzzy subspaces. Fuzzy base and fuzzy sub base for a fuzzy topology in fuzzy spaces were also introduced and discussed.

The applications of image classification occupied a wide field in the recognition of faces using the Convolutional Neural Network (CNN) with the mathematical aspect of deep learning based on the MATLAB program. This is not only facial recognition, but also recognizing the parts of the face, eye, mouth, and nose after designing programs in addition to the new algorithms with the theory of CascadeObjectDetector and trainCascadeObjectDetector. The benefit of this work emerges in the areas of security, airports, markets, and so on.

This paper is devoted to introducing a new three dimensions hyperchaotic system and adapting it to enhance the Hight algorithm. The proposal hyperchaotic system with one equilibrium point is mainly derived from the Lorenz system, which we called (3D-NSC). The dynamic analysis of 3D-NSC presents some properties such as; stability of symmetric equilibria; phase diagram, bifurcation and Lyapunov exponents (LE), which are all investigated analytically and numerically.  Also, the circuit design of the 3D-NSC is introduced with some properties. The proposed system is occupied with improving the Hight algorithm. The main propose system is to create a key schedule for the chaotic Hight algorithm. This system is then applied to encrypt different images types. Our proposed system showed high encryption efficiency compared to systems, based on some performance analyzes such as; histogram, pixel change rate (NPCR), standardized variable mean intensity (UACI), pixel correlation, and entropy.

In this paper, the local bifurcation conditions that occur near each of the equilibrium points of the eco-epidemiological system of one prey population apparition with two diseases in the same population of predator have been studied and analyzed, near E1,E2,E3,E4 and E5, a transcritical bifurcation can occur, a saddle-node bifurcation happened near E5. Pitchfork bifurcation was occurrences at E2,E3,E4 and E5. Moreover conditions for Hopf- bifurcation was studied near both of one disease stable point E3,E4 and E5 . About elucidation of the status of local bifurcation the associated of the set of hypothetical parameters with numerical results which assert our analytical results of this model.

The paper introduces the concepts of ϖ-strongly (resp., ϖ-closure, ϖ-weakly) form of continuous functions on bitopological spaces, furthermore we introduce a theorems, characterizing on the class of functions, show how it can be studied from different point of view.

In this paper, a generalized form of the known Rangaig integral transform has been proposed; the general integral form is presented as a new integral transform called the ``general Rangaig integral transform''. The General Rangaig integral transform has been studied and proven for some fundamental functions, its applicability and ability to find the exact solution have been proven via utilizing the transform in solving first- and second-order ordinary differential equations.

The purpose of this paper is to obtain the optimal axial distance from the nozzle to the motor shell for suction of the maximum flow rate of the cooling fluid. Given that there is no laboratory data to evaluate numerical analysis, an attempt was made to validate the numerical coefficient of the NACA0012 blade lift by numerical method to validate the numerical method and compare it with its existing laboratory results. As you can see from the results, the distance between the nozzle and the motor has a very small effect on the cooling flow. In fact, this shows that with the changes in axial distance in the mentioned range (between 0 and 2.5 cm), the amount of suction caused by the hot jet of the hot nozzle output of the nozzle has a very small effect on the cooling flow. To check the suction rate of the nozzle output jet on the cooling flow rate, the problem for different discharge nozzles is numerically analyzed to see how much the effect of the nozzle output fluid on the cooling fluid discharge flow is. The coolant flow of the Flow rate engine nozzle does not change much. This factor indicates that the flow rate of the engine coolant flow is mostly due to the pressure of the coolant itself and does not depend on the suction jet of the nozzle output. In this section, the effect of increasing the length of the engine shell on the cooling rate of the engine cooling is discussed. In this analysis, the shell length was increased by 6.5 cm. The increase in shell length is done with a constant diameter. As the shell length increased, the cooling flow rate increased significantly. As the shell length increases, the cooling flow rate increases by about 52% at the relative inlet pressure of 15,000 Pascals. Keywords: nozzle, engine shell, Flow rate suction, cooling.

A deep learning powerful models of machine learning indicated better performance as precision and speed for images classification. The purpose of this paper is the detection of patients suspected of pneumonia and a novel coronavirus. Convolutional Neural Network (CNN) is utilized for features extract and it classifies, where CNN classify features into three classes are COVID-19, NORMAL, and PNEUMONIA. In CNN updating weights by CNN backpropagation and SGDM optimization algorithms in the training stage. The performance of CNN on the dataset is a combination between Chest X-Ray dataset (1583-NORMAL images and 4272-PNEUMONIA images) and COVID-19 dataset (126-images) for automatically anticipate whether a patient has COVID-19 or PNEUMONIA, where accuracy 94.31\%  and F1-Score 88.48\% in case 60\% training, 20\% testing, and 20\% validation.

The current analysis employs the Riccati and modified simple equation methods to retrieve new optical solitons for highly dispersive nonlinear Schrodinger-type equation (NLSE). With cubic-quintic-septic law (also known as a polynomial) of refractive index and perturbation terms having cubic nonlinearity, 1-optical solitons in the form of hyperbolic, periodic, and rational are derived. the two schemes offer an influential mathematical tool for solving NLSEs in various areas of applied sciences.

This paper deals with the study about the formulation of spatial regression models for independent and dependent fuzzy variables, while the parameters crisp values, which are estimated by the maximum likelihood method. In this paper, has been formulated Fuzzy Pure Spatial Autoregressive Model (FPSAM) from the fuzzy general spatial model, and applied for Trapezoidal fuzzy number in the domain traffic accidents for a number of cities in Iraq for the year 2018 and that after converting the Trapezoidal fuzzy number into crisp values by centroid method, calculations the results by Matlab language.

In this paper, new improvements, refinements and extensions to show that an FhFh-convex function on time scales satisfies Hermite-Hadamard inequality is given in several directions. Examples and applications are as well provided to further support the results obtained.

Let R be a commutative ring with identity and let M be an R-module. This study presents the nearly locally hollow module that's a strong form of a hollow module. We present that an R-module M is nearly locally hollow if M has a unique semi-maximal sub-module that contains all small sub-modules of M. The current study deals with this class of modules and gives several fundamental properties related to this concept.

This paper presents the delay Bessel's problem, and therefore the basic definitions, theorems, applications, and corollaries will be reviewed during this paper.

In this work, we invested a kind of fuzzy soft quasi-normal operator namely fuzzy soft (n−N˜)-quasi-normal operator this modification of fuzzy soft bounded linear quasi-normal operator appear in recently many papers. Some properties and operation about this operator have been given, also more conditions given to get some theorems in this study.

We study a superlinear and subcritical p-Kirchhoff-type problem which is variational and depends upon a real parameter λ. The nonlocal term forces some of the fiber maps associated with the energy functional to have two critical points. This suggests multiplicity of solutions, and indeed, we show the existence of a local minimum and a mountain pass-type solution. We characterize the first parameter λ∗0 for which the local minimum has nonnegative energy when λ≥λ∗0. Moreover, we characterize the extremal parameter λ∗ for which if λ>λ∗; then, the only solution to the
p-Kirchhoff problem is the zero function. In fact, λ∗ can be characterized in terms of the best constant of Sobolev embeddings. We also study the asymptotic behavior of the solutions when λ↓0

The estimation of statistical parameters for multivariate data can lead to wasted information if the missing values are neglected, which in return will lead to inaccurate estimates, therefore the incomplete data must be estimated using one of the statistical estimation methods to obtain accurate results and thus obtaining good estimates for the parameters.
Missing values is considered one of the most important problems that researchers encounter and the most common, and in the case of the multivariate skew normal distribution (MSN) the presence of this problem will lead to weak and misleading conclusions for the research, which calls for treating this problem and in return obtaining efficient and convincing results. The aim of this paper is to estimate the missing values for the multivariate skew normal distribution function using the K-nearest neighbors Imputation (KNN). After estimating the missing values, the parameters are estimated using Genetic Algorithm (GA), and the Bayesian Approach was also used to estimate the missing values and find the estimates for the parameters. Using simulation, the Mean Squared Error (MSE) was calculated to find out which method is the best for estimation by comparing the two methods using different sample sizes (400, 600, and 800). The (GA) that is based on the (KNN) algorithm to estimate the missing values proved to be better and more efficient than the Bayesian Approach in terms of the results.

The concept of structural reliability analysis presented a methodology used to verify the efficiency of an engineering structure in the design and experimentation stage to provide support for a more balanced design between structural integrity and the requirements for it, because it combines probabilistic and statistical techniques with science in the specialized field through the regular and harmonic use of analytical tools, in addition to empirical data available. This is done by calculating the probability of structural failure because it is a measure of how well the studied design works according to the resistance factors (components) on which it depends and strength (operational conditions). There are several techniques used to analyse reliability and compute the probability of structural failure, and among those techniques is the third-moment technique based on the first-order reliability method. In this paper, the researcher proposes the development of the third-moment technique using the Downhill Simplex algorithm, the proposed technique was applied to a numerical example, and it was highly efficient compared to the original third-moment technique.

This paper deals with an epidemiological system with stage-structured, harvesting and refuge for only prey, the disease of type (SIS) is just in the immature of the prey. The sufficient conditions guaranteeing the occurrence of local bifurcation and the Hopf bifurcation for the system are obtained. Further, the validity of our main results was demonstrated by numerical analysis.

In this paper, the shifted power low distribution is studied in the direction of Fractioned moment. This type of distribution is a generalization for standard power low distributions. In this study, the fractional definition of Caputo is used to generalize the fractional moments of the maintained type of distribution to get a closed useful form.

In this paper, we simulated the methods of estimation of Pareto exponential distribution parameters, using various experiments including default parameter values (0.5, 1, 5), sample sizes (10,250,100), and Iterations (D = 1000). The Maximum likelihood method and the standard Bayes method were used with informative prior distribution (Gamma distribution) and the non-information prior distribution (Uniform distribution), and symmetric loss function (quadratic loss function), and asymmetric (General Entropy loss function). The Bayes method gave complex mathematical formulas that were solved by Lindley approximation. The results of the simulation experiments showed the advantage of the standard Bayes method in the information prior and the quadratic loss function.

The main aim of the paper is to reduce the Total Harmonic Distortion of a 9-level Hybrid Cascaded Multilevel Inverter for a standalone Photovoltaic system operated at Maximum powerpoint. The maximum power point is tracked using perturb and observe (P\&O) method, a boost converter is used to implement this process. The output power is calculated using a small perturbation in the terminal voltage. The comparison between two cases of hybrid cascaded multilevel inverter using Phase Disposition Pulse Width Modulation has been done. Total harmonic distortion is minimized when one bridge is run in square wave mode and the other bridges are operated in pulse width modulation mode, according to the results of this comparison. The value of THD for HMLI output voltage is equal to 12.85 percent when all bridges are operated in PWM mode, according to simulation results. Finally, the results of the computer simulations using the MATLAB software are presented and discussed, along with a comparison of pulse width modulation strategies.

The goal of any profit organization is to bolster its revenue by providing useful suggestions to its customer base. In order to achieve this, vast research is being undertaken by companies such as Netflix and Amazon on their Recommendation Systems and providing users with choices, they are most likely to click on. The purpose of this paper is to provide a holistic view of types of Recommendation Engines and how they are implemented, scaled and can provide a basis for revenue generation. The focus would be to implement a Recommendation Engine on PySpark using the ALS (Alternate Least Square) method. Besides, Neo-4j and Cypher query language for implementing recommendations on a graph database and analyzing how heterogeneous information can be levied to tackle the infamous cold start problem in recommender engines would be explored. The dataset used for analysis is the Group-lens 100K Movie-lens dataset and the algorithm is implemented to best fit the dataset. Further, an in-depth comparison of several techniques has been carried out on the basis of different metrics, hyper-parameter selection and the number of epochs used. The claims have been justified by evaluating the performance of the model depending on the different use cases, thus aiding in predictive analytics of the movie, as per the interest of the customer using visualization tools.

There have been much interest in analysis of stochastic differential equation with long memory, represented by fractional diffusion process, this property have been proved itself in financial mathematic as intrinsic character of financial time series, so finding an appropriate method for estimate and analyze stochastic differential equations with long memory is a very important contemporary topic, in this paper we suggest a method for a system of stochastic differential equations with long memory, also we use the Bayesian methodology to incorporate the advanced knowledge , in addition we apply renormalized integral known in literature as Wick-It\^{o}-Skorohod to solve problem of arbitrage in stochastic models (which yield inefficient mathematical stochastic models for financial market), some of conventional methods like quasi maximum likelihood , Separable Integral-Matching for Ordinary Differential Equations, and multivariate Brownian method are used to be compared with the suggested method. The suggested method has been proved to be very accurate. The estimated model used to calculate the portfolio of assets quantities allocation.

It is known that when the multicollinearity exists in the gamma regression model, the variance of maximum likelihood estimator is unstable and high. In this article, a new Liu-type estimator based on (r-(k-d)) class estimator in gamma regression model is proposed. The performance of the proposed estimator is studied and comparisons are done with others. Depending on the simulation and real data results in the sense of mean squared error, the proposed estimator is superior to the other estimators.

The matrix-variate generalized hyperbolic distribution is heavy-tailed mixed continuous skewed probability distribution. This distribution has multi applications in the field of economics, risk management, especially in stock modeling. This paper includes the estimate of the location matrix θ for the multivariate partial linear regression model, which is one of the multivariate semiparametric regression models when the random error follows a matrix-variate generalized hyperbolic distribution in the Bayesian technique depending on non-informative and informative prior information, estimating the location matrix under balanced and unbalanced loss function and the shape parameters (λ, ψ, ν), skewness matrix (δ), the scale matrix (Σ) are known. In addition, estimation the smoothing parameter by a proposed method depending on the rule of thumb, the proposed kernel function depending on the mixed Gaussian kernel. the researchers concluded when non-informative and informative prior information is available that the posterior probability distribution for the location matrix θ is a matrix-variate generalized hyperbolic distribution, through the experimental side, it was found that the proposed kernel function is overriding than the Gaussian kernel function in estimate the location matrix and under informative prior information.

The Log-Logistic distribution is one of the important statistical distributions as it can be applied in many fields ( biological, chemical and physical experiments) and its importance comes from the importance of determining the survival and risk function for these experiments. The research will work to determine the characteristics of the distribution through the use of order statistics to estimate its parameters using the approved standard Bayes method On the squared loss function (Bayslf) and determining the optimal method by comparing it with the MLE method according to a simulation method by taking different models for default values for parameter and different sample sizes and with MSE, IMSE comparison criteria as well as applying it to real data for breast cancer patients and determine survival and risk function

In recent decades, free zones have been proposed as an effective lever for the growth and improvement of countries’ economies. These zones can play an irreplaceable role in this direction as one of the axes of attracting capital. Identifying opportunities and threats to attract capital in free zones, Removing barriers, creating and providing a suitable platform for attracting domestic and foreign investors, are effective factors in investing in free zones, which in turn lead to economic growth and development of these areas. Considering the significant effects of free zones on the welfare of residents in these areas, the purpose of this study was to investigate the effect of effective factors of domestic and foreign investment in the Arvand Free Zone on economic welfare indicators (per capita income, unemployment rate and inflation) in Khuzestan province. During the period 1385-1396, which at first, while obtaining the opinions of 40 managers, experts and residents of the region in order to formulate appropriate strategies and strategies to achieve the goals of creating the region based on the potential and advantages for domestic and foreign investment, using From the technique (SWOT), we identify the factors affecting the indoor and outdoor environment in order to discover the strengths and weaknesses (internal factors) and threats and opportunities (external factors) and then using the model method to explain with a break Extensive (ARDL) The effect of domestic and foreign investment in the region on welfare indicators in Khuzestan province is examined. The results of this study in the first part of the analysis (SWOT), show that the most important opportunities in the development of free trade zone based on maximum investment attraction, respectively, the strategic geographical location of the Arvand Free Zone and the use of regional markets. The results of this study in the second part showed that according to the current conditions of the domestic investment region, it has had stronger effects on welfare indicators than foreign investment.

The aim of this article is to associate a tritopological space with a locally finite graph, and induce a new type of topology from three well-known topologies on the same locally finite graph. These are the three tritopologies proposed recently to associate topological spaces with undirected graphs, the first (Independent Topology), the second is (Non-Incidence Topology) and the third is (Graphic Topology). Then some results and properties of these tritopological spaces and new induced topologies were investigated. Giving a fundamental step toward studying some properties of locally finite graphs by their corresponding tritopological spaces is our motivation.

Doctors, especially surgeons in remote areas, need help to overcome the problem of transmission of infection resulting from the repeated use of surgical instruments without sterilization, and that is why researchers sought to find solutions. This study presented a design and model made for a low-cost solar sterilizer. It relied on benefits from the glasshouse properties to generate the wet steam that was required for sterilization at a temperature of 121.1 ◦C and a pressure of 2.1 bar inside an aluminum pressure cylinder that was placed inside an insulated chamber, whose dimensions were 50 cm wide, 30 cm high, and 93 cm deep. Its front face was made of 10 mm-thick thermal glass. The top face was used as a frame for fixing the cylinder. The outer surface of the cylinder was coated with a matte black paint to increase heat absorption. The model was tested in the absence of surgical equipment using half a liter of pure water, and it took 128 minutes to achieve a steam with the properties necessary for sterilization. The test started when the temperature of the water inside the cylinder was 56.1°C. In climatic conditions in which the average intensity of solar radiation was 903 W/m2 and the average ambient temperature was 34°C. Then the performance was tested with the presence of 1.2 kg of instruments. The presence of the surgical instruments increased the time required to achieve the temperature and pressure required for sterilization by 25.78%. And to increase the thermal enablement of the system, reflective panels were placed to reflect the solar radiation towards the surface of the cylinder. This procedure achieved a 6.2% reduction in the time required for the sterilization requirement for steam. The tests were conducted over several days and under different conditions, each achieving the necessary steam for sterilization.

One of the goals of financial institutions is to strengthen the economic infrastructure in developing the financial sphere. In this regard, financial institutions should take the necessary planning to increase their incomes, and if they do not pay attention, the consequences can be predicted for this group of economic activists Increasing income and reducing the risk of bankruptcy are among the most important goals for financial institutions and enterprises. Therefore, considering the increase of income and the integration approach based on the selection of partners in the field of banking, this paper presents a mathematical model based on reducing the risk of bankruptcy. The multi-objective genetic algorithm method has been used to solve and optimize the model. The proposed method was implemented on real data related to ten Iranian banks and the results led to the formation of a financial firm with a combination of banks to maximize the income and minimize the bankruptcy risk.

One of the vertex-degree based topological indices is Sombor index which is denoted by SO(G), and defined by SO(G) = P uv∈E(G) p d 2 u + d 2 v where du, dv are the degree of vertices u and v in the graph G respectively. In this paper, we are focusing on computing the Sombor index of some graph operations, more precisely join and corona product of two graphs. The original graphs that have been the base of this paper are path, cycle and complete graphs.

In this research, an attempt to address a problem that is still intractable for researchers in the field of diagnosing dynamic systems, including state space models, which are related to determining the type of process appropriate to the nature of the system and determining its rank. Accordingly, the wavelet method will be adopted in the diagnostic process and the parameters of these models will be estimated using the least squares method, applying this to real data and comparing the results based on a set of statistical and engineering criteria using the ready-made program Matlab.

The main purpose of this article is a comparative analysis of the optimal level of production capacity of leading industrial industries in the construction sector in the years (2001-2018). In this article, we compare six different industries that are leading industries in manufacturing. These industries are: (Cement, lime and gypsum, wood and wood products, glass and ceramics, construction equipment, home and decorative, stone), in order to determine the optimal level of production capacity in the industries of the leading factories with double-digit ISIC code.Rev.2 and ISIC.Rev.3 Translog cost function was used. Production efficiency is the equivalence of scale traction or cost traction (inverse of scale traction) with one. To obtain the optimal level of production, first from the equation of scale elasticity or cost elasticity with one, a production level was obtained which naturally satisfies the first condition, then the second condition in production was obtained for research purposes. The results of the analysis of the leading production capacity of the construction sector showed; in the cement, gypsum and lime industries, showed that the amount of optimal production capacity in the cement, gypsum and lime industries was estimated at 190%. The wood and products industries use just over 28% of their nominal capacity. In the glass and ceramic industries, the capacity utilization was estimated at 154%. Construction equipment Capacity utilization was estimated at 137%. Home and decorative industries; Capacity utilization was estimated at 149%. Stone industry, the nominal production capacity of these industries is about 41%. Capacity utilization was estimated at 78% in the analysis.

The main target of this article is devoted to the generalization types of fuzzy contra continuous mapping so-called fuzzy contra t generalized pre− continuous mapping and fuzzy contra t∗ generalized pre− continuous mapping by utilizing fuzzy tgp-closed sets and fuzzy t∗gp-closed sets. We look into a few of their characteristics and discuss the relationship between these types and how they relate to other types of fuzzy mappings. Furthermore, we provide some examples that demonstrate that the inverse is not always true.

In this manuscript, we investigate solutions of the partial differential equations (PDEs) arising in mathematical physics with local fractional derivative operators (LFDOs). To get approximate solutions to these equations, we utilize the reduce differential transform method (RDTM) which is based upon the LFDOs. Illustrative examples are given to show the accuracy and reliable results. The obtained solutions show that the present method is an efficient and simple tool for solving the linear and nonlinear PDEs within the LFDOs.

In this paper, we study the existence and uniqueness of mild solutions for nonlinear fractional differential equations subject to nonlocal integral boundary conditions in the frame of a ψ-Hilfer fractional derivative. Further, we discuss different kinds of stability of Ulam-Hyers for mild solutions to the given problem. Using the fixed point theorems together with generalized Gronwall inequality the desired outcomes are proven. The obtained results generalize many previous works that contain special cases of function ψ. At the end, some pertinent examples demonstrating the effectiveness of the theoretical results are presented.

In this paper, we study the existence and uniqueness of weak solution to a Dirichlet boundary value problems for the following nonlinear degenerate elliptic problems
−div[ω1A(x,∇u)+ν2B(x,u,∇u)]+ν1C(x,u)+ω2|u|p−2u=f−divF,
where 1<p<∞, ω1, ν2, ν1 and ω2 are Ap-weight functions, and A:Ω×Rn⟶Rn, B:Ω×R×Rn⟶Rn, C:Ω×R⟶R are Carat'eodory functions that satisfy some conditions and the right-hand side term f−divF belongs to Lp′(Ω,ω1−p′2)+∏j=1nLp′(Ω,ω1−p′1). We will use the Browder-Minty Theorem and the weighted Sobolev spaces theory to prove the existence and uniqueness of weak solution in the weighted Sobolev space W1,p0(Ω,ω1,ω2).

The Johnson Bonded Distribution (JSBD) is one of the distributions belonging to the Johnson Distribution family (JD) This family is considered one of the flexible distributions that enable it to represent the random behaviour of many phenomena. The method of percentile estimation and the Mixture method (Maximum Likelihood & Least Square) were used to find estimates of the distribution parameters. The simulation results showed the advantage of the Mixture method at large sample sizes, the accuracy of the two methods is close at average sample sizes.

In the present paper, We introduce a new kind of Toeplitz operators on the spaces of 2π periodic holomorphic functions on the upper halfplane equipped with an integral norm similar to the norm of Lp spaces. We prove the boundedness of Toeplitz operators in the case of bounded symbols. Also, we state some open problems for unbounded symbols and other cases in which our spaces are not Hilbert spaces.

The problem of minimizing a function of three criteria maximum earliness, the sum of completion times and maximum lateness in a hierarchical method is presented in this paper. A set of n independent jobs has to be scheduled on a single machine that is continuously available from time zero onwards and that can handle no more than one job at a time. Job j(j=1,2,…,n) requires processing during a given positive uninterrupted time pj. An algorithm to find the best possible solution is proposed for the problem of three criteria maximum earliness, the sum of completion times and maximum lateness in a hierarchical case.

The Liu estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of Inter-correlated (multicollinearity). The negative binomial regression model is a well-known model in the application when the response variable is non-negative integers or counts. However, it is known that multicollinearity negatively affects the variance of the maximum likelihood estimator of the negative binomial coefficients. To overcome this problem, a negative binomial Liu estimator has been proposed by numerous researchers. In this paper, a Jackknifed Liu-type negative binomial estimator (JNBLTE) is proposed and derived. The idea behind the JNBLTE is to decrease the shrinkage parameter and, therefore, the resultant estimator can be better with a small amount of bias. Our Monte Carlo simulation results suggest that the JNBLTE estimator can bring significant improvement relative to other existing estimators. In addition, the real application results demonstrate that the JNBLTE estimator outperforms both the negative binomial Liu estimator and maximum likelihood estimators in terms of predictive performance.

Increasing the number of microgrids will challenge centralized control and telecommunications control structures. Multifunctional systems are a good alternative to decentralized control and management structures. The focus of this article is on designing multifactorial control for voltage, frequency and active power control. Past studies have spent their ability to stabilize the voltage at different terminals of a microgrid on a value. This can be technically and economically criticized. Different terminal voltages can be set to different values. It is not necessary to set the voltage of each terminal to the value of the apex. Economically, given the optimal load distribution, it may be possible to stabilize all terminals on an unjustified value. In this study, a protocol has been proposed by which the voltage at different terminals of a microgrid is able to stabilize at different values. In this paper, due to the stabilization of the voltage of different terminals on different values, the optimal distribution of reactive power is created. To regulate the voltage at the terminals where the voltage control devices are not available, the voltage corresponding to these terminals can be considered as one of the optimal reactive power dissipation constraints; in this case, the improvement of the voltage profile in critical terminals is facilitated. The article has tested the comparative method of fixed time and the postmodern method on a similar system. And it has been shown that the time-constant adaptive method has a better convergence time than the regression method. But the simplicity of the backlog method is significant in some applications (where there are no power-sensitive loads).

A new distance-based homogeneity test for circular data is considered in this paper. This test statistic could be used with a permutation test to detect the homogeneity between the distributions of two angular groups. A comparison between the proposed test against the Randomisation Watson test (RWT) and Wheeler Watson test (WWT) is addressed as well. The power of the new test has been computed and compared against RWT and WWT powers based on the angular simulated data which have been generated from the von Mises distributions. The simulation study based on the power demonstrates that the proposed statistical test outperforms classical tests.

In this paper, a class of harmonic univalent functions has been studied by using q-analogue of the derivative operator for complex harmonic functions. We have obtained a sufficient condition, a representation theorem for this harmonic univalent functions class and some other geometric properties.

In this paper, some common fixed point results for four mappings satisfying generalized contractive condition in a generalized b-metric spaces are proved. Advantage of our work in comparison with studies done in the context of b-metric is that, the b-metric functions used in the theorems are not necessarily continuous. So, our results extend and improve several comparable results obtained previously. To show the validity of our work, we also prove that the same results hold even if the space is endowed with two metrics.

Stereo vision applications and systems are being grown common and widespread, especially with technological advancement. These systems and applications are widely applied in multiple fields and areas including autonomous robots, navigation, movie producing industry three-dimensional measurements, 3D reconstruction, object tracking, security system and identification systems, and augmented reality applications, and etc. Recently, much attention has been paid to new algorithms and practical systems in parallel with advanced technologies. High competition has been realized among a wide range of scholars, developers, and researchers toward developing high efficient systems and techniques. In this paper, we mainly present significant literature on some existing stereo vision algorithm techniques and systems in stereo vision and image processing areas. Where for many decades until recent, remarkable works and researches have been performed to develop methods with high accuracy and fast time processing. These systems and methods can be easily integrated with several applications as newly created solutions for various stereo vision problems such as low acracy, mismatching algorithms, correspondence problems, and more. The authors abroad have introduced numerous methods to solve the previous issues with different structures and specified targets.

The purpose of this article is to investigate and determine the factors affecting integration and acquisition activities in Iran. For this purpose, two hypotheses were determined and the annual data of 30 companies of the Tehran Stock Exchange in the period 2008-2020 were used to test the hypotheses. To test the hypotheses, the neural network method optimized with the Jaya optimization method was used to examine the effect of the studied variables including firm size and growth ability, risk growth, asset return, equity return, and financial leverage. Analysis using neural networks is used to isolate and clarify the effects of each of the studied variables. The analysis of the neural network showed that the three variables, exchange rate, interest rate, and stock price are very important in the process of changes in merger and acquisition activities in the country. Exchange rate fluctuations in particular have a very elastic effect on mergers and acquisitions, indicating that price effects are important in determining the flow of domestic and foreign investment. The stock market index had a positive effect on domestic mergers and acquisitions. Interest rates had a negative effect on mergers and acquisitions.

In this paper, the perturbation method is employed to obtain an approximate solution of some examples of the Bratu equation by choosing the different values of ε and comparison with the exact solutions. It can be seen that the perturbation method is an alternative technique to be considered in solving many practical problems involving differential equations.

The variety of weighted Average techniques that fall within the structure of exploratory data analysis (EDA) has driven a diversity of competitive results of data analysis. In spite of this variation, the outcomes obtained from the Hanning procedure have some similarities, which makes judging and analysis of data are difficult somehow. Many searches were attitude to get the best performance of technique to give a kind of satisfaction for the researchers who work on the area of EDA. The proposed method is based on calculating the weighted average by applying geometric and harmonic mean to be compared with the existing technique that uses the arithmetic mean. The application of the proposal techniques is done on daily financial data in Malaysia that issues Sukuk of funds in Islamic banking and financial business. The results of applying the proposed techniques on real data sets showed a competitive performance for these standards in the field of comparison of techniques undergoing the smoothing procedures.

Doubly-fed induction generators are sensitive to network faults and will be isolated from the circuit when a fault occurs. According to the new rules of the network, wind turbines should be able to continue working in fault conditions and should not be isolated from the system when the network fault occurs. This article proposes a Fuzzy-PID controller for controlling the doubly-fed induction generator rotor side converter in fault conditions. This controller is only added to the circuit in fault conditions and it reduces the current of the rotor circuit with the proper control of the rotor side converter. When the network voltage is in the normal state, rotor side converter control is performed with the PI controller. The proposed method is implemented in MATLAB Simulink software. The simulation results confirm the better performance of the proposed method in the proper control of the rotor side converter in fault conditions.

The problem of synchronization with extended dissipativity for Markovian Jump Recurrent Neural Networks (MJRNNs) is investigated. For MJRNNs, a new memory sampled - data extended dissipative control approach is suggested here. Some sufficient conditions in terms of Linear Matrix Inequalities (LMIs) are acquired by suitably establishing a relevant Lyapunov - Krasovskii functional (LKF), wherein the master and the slave system of MJRNNs are quadratically stable. At last, a numerical section is provided, along with one of the applications in circuit theory that clearly illustrates the efficacy of the proposed method’s performance.

Web services composition is one of the most fundamental and pressing issues when developing Webbased systems in accordance with Service-Oriented Architecture. The main challenge is selecting and composing a proper set of web services to fulfil various demands of clients over the World-Wide- Web. This paper reports results of a systematic literature review conducted on the state of the art of achievements in web services composition. It starts with a set of research questions that lead to a set of relevant keywords for querying four well-known digital libraries. Search results are then filtered through based on some inclusion and exclusion criteria. Finally, 269 most relevant documents were identified. The extracted information from these resources is analysed and results are reported, concerning three major viewpoints: bibliography, research quality, and strategy.

In this paper, we propose to solve nonlinear functional equations given in an infinite-dimensional Banach space by linearizing first and then discretizing the linear iterative equations. We establish new sufficient conditions which provide new criteria for dealing with convergence results. These conditions define a class of discretization schemes. Some numerical examples confirm the theoretical results by treating an integro-differential equation.

Multi-label data classification differs from traditional single-label data classification, in which each input sample participated with just one class tag. As a result of the presence of multiple class tags, the learning process is affected, and single-label classification can no longer be used. Methods for changing this problem have been developed. By using these methods, one can run the usual classifier classes on the data. Multi-label classification algorithms are used in a variety of fields, including text classification and semantic image annotation. A novel multi-label classification method based on deep learning and feature selection is presented in this paper with specific meta-label-specific features. The results of experiments on different multi-label datasets demonstrate that the proposed method is more efficient than previous methods.

In this paper, (h1,h2)-convex and s-convex functions are merged to form (h1,h2,s)-convex function. Inequalities of the Hermite-Hadamard (H-H) and Fejer's types will then be extended by using the (h1,h2,s)-convex function and its derivatives. Some special cases for these extended H-H and Fejer's inequalities are also explored in order to get the previously specified results. The relationship between newly constructed Hermite-Hadamard (H−H) and Fejer's types of inequalities with the average (mean) values are also discussed.

In this paper our main goal is to study the climatology and variability of the frequency of thunderstorm days over Bangladesh region throughout the year. It has been found that the mean thunderstorm days increase significantly from March to May, i.e. during the pre-monsoon season, although the graphical devices show that there does not seem to be much deviation from the occurrences of thunderstorms each year. The mean monthly and seasonal thunderstorm days were maximum in 1993, followed by that in 1997; whereas it was a minimum in the year 1980, with an extension in its frequency in the subsequent years 1981 and 1982. The coefficient of variation of both annual and seasonal thunderstorm days is minimum over the areas of maximum frequency of mean thunderstorm days and vice-versa. The time-domain analysis confirms that the occurrence happened to be maximum in the year 1991, although each and every state did not witness thunderstorms every year. Also some other time-domain models like autocorrelation and seasonal integrated moving average provide adequate evidence for exploring the number of thunderstorms which happen to confirm the trend of occurrence of thunderstorm over the years.

The accuracy of temperature forecasting in maximum and minimum cases is important to control the environmental effects. In this study, integrated autoregressive and moving average (ARIMA) model is used to forecast climatic temperature variable in maximum and minimum cases in Mosul, Iraq as traditional method. Neuro-Fuzzy (NF) is also proposed as modern approach to improve the forecasting results. The results in this study reflect outperforming in forecasting for NF approach comparing to ARIMA model. In conclusion, NF approach can be used for more accuracy to forecast climatic temperature datasets in maximum and minimum cases.

Estimating the central mean subspace without requiring to designate a model is achieved via MAVE method. The original p predictors are replaced with d-linear combinations (LC) of predictors in MAVE, where d<p without loss of any information about the regression. However, it is known that the interpretation of the estimated effective dimension reduction (EDR) direction is not easy due to each EDR direction is a LC of all the original predictors. The PACS method is an oracle procedure. In this article, a group variable selection method (SMAVE-PACS) is proposed. The sufficient dimension reduction (SDR) concepts and group variable selection are emerged through SMAVE-PACS. SMAVE-PACS produces sparse and accurate solutions with the ability of group identification. SMAVE-PACS extended PACS to multi-dimensional regression under SDR conditions. In addition, a method for estimating the structural dimension was proposed. The effectiveness of the SMAVE-PACS is checked through simulation and real data.

In this paper, we introduce a new 2- parameters family of distributions named [0,1] Truncated Inverse Weibull - G family ([0,1] TIW-G) family, to generate new types of continues distributions. A special model namely, [0,1] Truncated Inverse Weibull Rayleigh distribution ([0,1] TIWR) distribution is considered and defined and some of the statistical properties are derived. Parameter's estimations using MLE method is provided and a simulation is given to determine the accuracy of the method used above. To demonstrate the utility of the distribution in nowday's applications, we explore and investigate the death rates of COVID-19 in Iraq in the period from 14 December 2020 to 30 April 2021.

That there are indicators or statistical transactions that have appeared in a large way in recent times to describe, summarize and analyse spatial data, when a study is done of many phenomena or a disease is studied, whether it is on humans or animals, we need to analyze the spatial data resulting from those phenomena, as it includes observations of the spatial units. For example, countries or provinces ... etc., all of these are linked to certain points or locations. The study uses the maximum likelihood method to estimate the parameters of the General Spatial Model by employing the model to study cancer which shows the relationship between the dependent variable Y represented by the number of patients and the explanatory variables represented ( average age, tumor size, treame, hormone, immunity) in light of the effect of spatial juxtaposition and using Rook neigh boring criteria. One of the most important conclusions reached is the emergence of significant effects of some explanatory variables on the dependent variable Y, and the estimated values of the dependent variable Y are close to the real values of the same variable.

In the past few years, many methods have been proposed to generate new distributions. In this paper, we introduce [0,1] truncated half logistic - half logistic distribution ([0,1] THL-HLD). Some of important statistical properties of this distribution will be derived. These properties are rth raw moments function, hazard rate function, stress-strength function and Shannon entropy.

Survival functions estimators can be affected by outlier, and thus these estimations move away from their real values, especially with the increasing in the outlier ratios within the sample of the random variable. The research included a comparison of a number of Bayesian methods for the estimations of survival functions of burr- X distribution with the percentages of different outliers within the sample. Simulation results showed the effect of the estimation methods by sample size and the percentage of outliers, and the real values of the parameters distribution. Mean square error was adopted as a measure to compare the estimation methods with a number of simulation experiments. The research also included a case study of Covid-19 for practical application. Other estimation methods can be taken (maximum likelihood estimation method, moment method, and shrinkage method) to note the possibility of being affected by outlier values

The skewness and scale parameters of the asymmetric Laplace distribution are estimated with Bayesian methods using quadratic loss function and the weighted quadratic loss function, respectively, based on the functions of the prior of the gamma distribution and the exponential distribution for each of the skewness and scale parameters. These estimates were compared using integral mean square error, which was based on the real data technique of the stock prices the Iraqi market. The results revealed that the bayes estimator outperformed the quadratic loss function under the weighted quadratic loss function.

A new probability model for positively skewed datasets like economic data, medical, engineering and other sciences was developed in this paper. The new distribution is named the Topp-Leone Flexible Weibull distribution and it was generated using the Topp-Leone-G family of distributions. This new distribution has three parameters and it is very flexible in fitting several and different datasets. Its basic mathematical properties were studied and the method of maximum likelihood estimation was used for the estimation of model parameters. A real life dataset was used to illustrate the flexibility of the distribution and it was found that the new model provides a better fit to real life datasets than the Topp Leone Burr XII, Topp Leone Lomax and Exponentiated Generalized Flexible Weibull distributions.

This paper aims to study the parameters estimation methods of the stationary mixed model (autoregressive-moving average) of low order ARMA (1, 1) regarding to time domain analysis in univariate time series. Using the approximating methods: Back Forecasting (BF), Classical Conditional Maximum Likelihood (CC) and Proposed Conditional Maximum Likelihood(PC). A comparison is done among the three methods by Mean Squared Error (MSE) using several simulation experiments; the obtained results from the empirical analysis indicate that the accuracy of the proposed conditional method is better than the classical conditional method.

In the information age, the decision-making process depends on data analysis. Most life problems are characterized by indeterminacy. In this research, we study data tables that change with time (changing information systems) and we use the concept of uncertain probabilities to introduce stochastic variables in data tables that changed over time. The uncertain probabilities are based on constructing neighborhoods on the set of objects in information systems and use topological approximations. Some examples are given to indicate the suggested approach. These concepts help in obtaining decisions using mathematical models in which no codes are used, or converting verbal variables into digital, and this helps to make approximations close to reality.

This paper aims to measure the effect of the volatility on the daily closing price for the (Iraqi dinar against US dollar) from (21 July 2011) until (21 July 2021) using the models of asymmetric general autoregressive conditional heterogeneity (APGARCH and AVGARCH). The parameter estimated by Maximum Likelihood Estimation method and the error term assumed two distributional (General error distribution and Student’s t distribution), the results showed that the APGARCH(1,2) with error term distributed (Student’s t) distribution is the best model for the return series of the (IQ/USD) exchange rate to get the lowest value according to the information criteria for determining ranks (AIC, BIC) in addition to the presence of the asymmetric effect of the leverage, and this is evidence that negative shocks affect volatility more than positive shocks (the impact of the positive shocks is less than the impact of the negative shocks).

The present study examines the effect of conventional hashtags & cashtags at social networks on the stock returns of S&P500 companies. In the first step, by creating an optimization portfolio that consists of S&P 500 companies (Fundamental analysis) abnormal stock returns have been tested. (similar to the 1989 study, Jane A. OU and Stephen H. Penman). Then, in the second step of the research, the effect of reduction free float of companies on abnormal stock returns has been tested. Next, the reduction of the explanatory power of EPS and BV information content as representatives of financial statements (income statement and balance sheet) on stock returns has been tested, and finally, in the fourth step, the effect of conventional hashtags & cashtags in social networks at the site Stocktwits.com has been tested. The findings of this research showed that: although Jane A. OU and Stephen H. Penman 1989 rejected the hypothesis of a semi-strong efficient market in the companies surveyed, at this study and of course in the S & P500 companies, the hypothesis of a semi-strong efficient market was confirmed. The effect of declining free float has led to abnormal returns on the S & P500 companies. The explanatory power of EPS and BV information content on stock returns have diminished over time, which has been due to the hashtags & cashtags content of financial statements on social networks. In other words, news about the status of companies is rapidly affecting stock returns through virtual networks.

In this paper, we investigated the performance of Bayesian Computational methods for estimating the parameters of the multinomial Logistic regression model. We discussed two of the most common Bayesian computational algorithms: the Random walk Metropolis-Hastings (RWM) and Slice algorithms and their application to estimating the parameters of the addiction model as well as comparing the performance of these algorithms using the mean square error (MSE) criterion. The results revealed that the performance of the algorithms is excellent, with a slight superiority to the RWM algorithm.

The current work details the behaviour of a finite Markovian queueing system with a vacation in which the server may face problems of breakdowns while in service. The repair process does start immediately after a breakdown which immediately resumes the service. During this period any new customer is allowed to join the system. Whenever the server finds nobody, the server goes on vacation and resumes service after N customers are accumulated. Meanwhile, it triggers pre-service called start-up. Further, we considered two types of repair facilities for the broken-down server with an optional probability. The server first provides essential service to all customers and the second optional service will be provided with a probability of “p”. The customer may renege in the first phase of service. We adopted Runge-Kutta Method to find Transient state probabilities and computed various performance indices like the expected length of the system, the mean waiting time etc. We then performed the sensitivity analysis to explore the effect of different parameters.

In this paper, we introduce a new operator Ωc g(z) associated with polylogarithm function, applying it on the subclasses AHΣ∗ B (γ, k) of meromorphic starlike bi-univalent functions of order γ, and AHΣe∗ B (γ, k) of meromorphic strongly starlike bi-univalent functions of order γ, also we find estimates on the coefficients |b0| and |b1| for functions in these subclasses.

This paper proved the existence and uniqueness of the solution of the Schr¨odinger equation with singular potential and initial data in the Colombeau algebra Ge.

In this paper, we obtain a formula for residues and prove Laurent expansion and expansion to Taylor series for A(z)-analytic functions.

In this paper, using the potential of microgrid surface batteries, the frequency support of the power system in different operating modes was discussed. The study suggested that the process could be supported by a set of batteries in the microgrid (in the mains-connected mode and in the island mode, with the decentralized participation of each, in frequency support (power system or microgrid) in Different operating modes were addressed. The approach used in this paper was to use a convex optimization algorithm. In this way, the batteries independently measured the system frequency and considering their charging mode injected the optimal power into the system, and A convex optimization algorithm will prove that the local optimal point (scattered battery injection) is the optimal point. It will also be universal (optimal system frequency control). Studies in system simulation in two modes of microgrid connected to the main grid and island microgrid have yielded several points.

In this paper, we introduced the estimation of parameters for distributions that have multi-double truncation by the maximum likelihood method. We depended in the applied study on data of disease of Covid-19 by using one double truncation. Some statistical data were analyzed and unknown parameters and were compared with some distribution.

In this work, we introduce and investigate a new class k − UST e s(q, m, γ, ς) of analytic functions in the open unit disc U with negative coefficients. The aim of this study is to determine coefficient estimates, neighborhoods and partial sums for functions u belonging to this class.

Modelling of count data has been of extreme interest to researchers. However, in practice, count data is often identified with overdispersion or underdispersion. The Conway Maxwell Poisson regression model (CMPRE) has been proven powerful in modelling count data with a wide range of dispersion. In regression modeling, it is known that multicollinearity negatively affects the variance of the maximum likelihood estimator. To address this problem, shrinkage estimators, such as Liu and Liu-type estimators have been consistently verified to be attractive to decrease the effects of multicollinearity. However, these shrinkage estimators are considered biased estimators. In this study, the jackknife approach and its modified version are proposed for modeling count data with CMPRE. These two estimators are proposed to reduce the effects of multicollinearity and the biasedness of using the Liu-type estimator simultaneously. The results of Monte Carlo simulation and real data recommend that the proposed estimators were significant improvement relative to other competitor estimators, in terms of absolute bias and mean squared error with superiority to the modified jackknifed Liu-type estimator.

In this paper, using continuous, injective, and sequentially convergent mappings on a group, new generalizations of Kannan and Chatterjea’s fixed points in Banach groups are presented. We generalize contractions with constants to prove some fixed point theorems in a Banach group. Moreover, nondecreasing continuous functions from the set of positive real numbers to itself are used to introduce a new extension of contractions on normed groups.

Our aim in this work, general formula for solution some partial differential equations are derived, which have applications in other sciences. Elzaki transformation was used to obtain these formulas. Moreover, some examples are solved using these formulas.

We generalized the concept of a function poorly open to intuition raster spaces and we get some new descriptions of the opening up of the weak functions among the intuitive point areas. Furthermore, we are investigating some of the characteristics of these functions compared to the relevant functions.

This paper aims to highlight existence and uniqueness results for a coupled system of nonlinear fractional q-difference subject to nonlinear more general four-point boundary conditions are treated. Our analysis relies on two approaches, the topological degree for condensing maps via a priori estimate method and the Banach contraction principle fixed point theorem. Finally, Two examples illustrating the effectiveness of the theoretical results are presented.

The aim of this paper is the needs to analyze the survival time for patients with Covide-19b who remains in the hospital until death, so it is necessary to study the survival times and estimate the reliability. The problem of finding the best distribution that fits the data is the key idea to analyze the data accurately. Consequently, the misspecifying of the distribution that fit the data leads to poor quality inference criteria of the phenomenon, also leads to unreliable reliability estimations. Many data of sciences areas are of different probability distributions depending on the nature of the phenomenon within the studied communities Some of the data are represented simple phenomena that cope with a unique probability distribution, and some of which are very complex and heterogeneous systems that force the researchers to use probability distributions fits the behavior of this random phenomenon. Many works in the field of reliability, failure and survival times, and the function of reliability (survival) follows some common distributions such as the Exponential distribution, Weibull distribution and other distributions. In this paper we introduced the survival function that follows an important distribution in survival modeling, that is called the Lindley distribution with two Parameters, taking into account two forms of this distribution, one of them we proposed based on different forms of the probability density function and finding the survival function for the distribution and compared to other distributions using several methods of estimation including the Maximum Likelihood Estimator (MLE), (percentiles estimators) by using Monte Carlo simulation experiments and comparing using the Integrated Mean Square Error (IMSE), (-2ln L) and AIC to achieve the best estimate of survival function among the distributions, as well as a real data analysis conducted for the survival times for patients with COVID-19 stay in hospital until death. The proposed distribution fitted the data very well in the Maximum Likelihood method compared with the other distribution.

This project aims to solve the second kind of Volterra, Fredholm integrodifferential equations, and mixed integral equations (VIDE, FIDEs and MIEs respectively) will be solved using the LaguerreChebyshev Petrov-Galerkin method (PGM). By solving three cases to show how the recommended technique works in this study, we established the PGM to find the approximate solution for linear VIDEs, FIDEs, and MIEs.

In this paper, the lattice derivatives and their properties are introduced and next to the generalized stability of lattice derivatives is investigated using the direct and fixed point method.

In our research, we present a study to determine the required sample size (the water body) to estimate the Spearman correlation coefficient in terms of its strength and direction for the variables of the total annual evaporation and annual humidity so that we have a symbol for the total annual evaporation (a) and the annual total humidity (b) for three provinces in Iraq, which are Baghdad, Najaf and Babel for a period of ten Years 1991 to 2000, we know that the Simple Linear Correlation Coefficient (Spearman) is a tool for people who work in statistical analysis. The result of the correlation of total annual evaporation and annual total humidity in Baghdad governorate was negative and strong, and the result of the correlation of total annual evaporation with the humidity of Najaf Governorate was positive and weak, and the result of the correlation between total annual evaporation and annual humidity in Babil Governorate was positive and weak.