Abstract

The use of modern scientific methods and techniques, is considered important topics to solve many of the problems which face some sector, including industrial, service and health. The researcher always intends to use modern methods characterized by accuracy, clarity and speed to reach the optimal solution and be easy at the same time in terms of understanding and application.

the research presented this comparison between the two methods of solution for linear fractional programming models which are linear transformation for Charnas & Cooper , and denominator function restriction method through applied on the oil heaters and gas cookers plant , where the show after reac

Coupling reaction of ( 4-amino antipyrene) with the (L- tyrosine ) gave the new azo ligand 2- ( 4- Antipyrene azo ) - tyrosine .Treatment of this ligand with metal ions (Mn(II) ,Co(II), Ni(II), and Cu(II) )in ethanolic medium in (1:2) (M:L) ratio yield a series of a neutral complexes of the general formula [M(L)2] . The prepared complexes were characterized using flame atomic absorption , FT.IR , UV-Vis spectroscopic and elemental microanalysis (C.H.N) as well as magnetic susceptibility and conductivity measurement

The aim of the work is the synthesis and characterization of the tridentate Schiff base (HL) containing (N and O) as donor atoms type (ONO). The ligand is: (HL) phenyl 2-(2-hydroxybenzylidenamino)benzoate . This ligand was prepared by the reaction of (phenyl 2-aminobenzoate) with salicylaldehyde under reflux in ethanol and few drops of glacial acetic acid which gave the ligand (HL). The prepared ligand was characterized by (FT IR,UV–Vis) spectroscopy, Elemental analysis of carbon, hydrogen and nitrogen (C.H.N.) and melting point. The ligand was reacted with some metal ions under reflux in ethanol with (1 metal :2 ligand )mole ratio which gave complexes of the general formula: [M(L)2]Cl , M = Cr III La III and , Pr III Products were found

The newly synthesized Schiff base ligand (E)-2-((2-phenylhydrazono)methyl)naphthalen-1-ol (phenyl hydrazine derivative), is allowed to react with each of the next mineral ion: Ni2+, Cu2+, Zn2+andCd2+successfully resulting to obtain new metal complexes with different geometric shape. The formation of Schiff base complexes and also the origin Schiff base is indicated using LC-Mass that manifest the obtained molar mass, FT-IR proved the occurrence of coordination through N of azobenzene and O of OH by observing the shifting in azomethines band and appearing of M-N and N-O bands. Moreover, we can also detect by such apparatus, the presence of aquatic water molecule inside the coordination sphere. UV-Vis spectra of all resultants reveale

Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things,

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.

Species of genus Chrotogonus (surface grasshoppers) are phytophagous and damaging to various economical important plants in their seedling stages. In order to know the biodiversity of surface grasshoppers, the detailed study has been conducted from four provinces of Pakistan. During this study, biodiversity, taxonomy, diagnosis, morphometric analysis, habitat, global distribution, and remarks of each species have been described. Total of 826 specimens were collected and sorted out into three species and three subspecies: C. (Chrotogonus) homalodemus homalodemus (Blanchard, 1836), C. (Chrotogonus) homalodemus (Blanchard, 1836), C. (Chrotogonus) trachypter

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia