In this article, the nonlinear problem of Jeffery-Hamel flow has been solved analytically and numerically by using reliable iterative and numerical methods. The approximate solutions obtained by using the Daftardar-Jafari method namely (DJM), Temimi-Ansari method namely (TAM) and Banach contraction method namely (BCM). The obtained solutions are discussed numerically, in comparison with other numerical solutions obtained from the fourth order Runge-Kutta (RK4), Euler and previous analytic methods available in literature. In addition, the convergence of the proposed methods is given based on the Banach fixed point theorem. The results reveal that the presented methods are reliable, effective and applicable to solve other nonlinear problems.

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

charge transfer complex formed by interaction between the p- aminodiphenylamine (PADPA) as electron donor with iodine as electron acceptor in ethanol at 250C as evidenced by color change and absorption. The spectrum obtained from complex PADPA – Iodine shows absorptions bands at 586 nm. All the variables which affected on the stability of complex were studies such as temperature, pH, time and concentration of acceptor. The linearity of the method was observed within a concentration rang (10–165) mg.L-1 and with a correlation coefficient (0.9996), while the molar absorbitivity and sandell sensitivity were (4643.32) L.mol-1.cm-1 and (0.0943) μg.cm-2, respectively. The adsorption of complex PADPA–I2 was studied using adsorbent surfaces

Abstract

              The current research sought to demonstrate the effect of material flow cost accounting on reducing products through the application of material flow cost accounting technique, which works on the optimal utilization of materials and energy and the reduction of environmental impacts.The research aims to clarify the knowledge foundations for material flow cost accounting, in addition to studying the material flow cost accounting technique that helps reduce the cost of products and make them environmentally friendly. To achieve this, the research relied on the descriptive approach with regard to the theoretical aspect of the resea

Charge transfer in styryl dyes STQ-1, STQ-2,and STQ-3 with organic media system has been studied theoretically depending on the Franck- Condon rule and continuum dielectric model . The reorientation energies (eV) were evaluated theoretically depending on dipole momentum, dielectric constant , and refrective index n. The rate constant of charge transfer has been calculated depending on the reorientation energy (eV) ,effective free energy , potential height barrier , and coupling coefficient . A matlap program has been written to calculated the rate constant of charge transfer and other parameter. The results of calculations show that STQ-2 dye is more reaction for charge transfer compare with STQ-1 and STQ-3 dyes

In the current research the absorption and fluorescence spectrum
of Coumarin (334) and Rhodamine (590) in ethanol solvent at
different concentration (10-3, 10-4, 10-5) M had been studied. The
absorption intensity of these dyes increases as the Concentration
increase in addition to that the spectrum was shifted towards the
longer wavelength (red shift). The energy transfer process has been
investigated after achievement this condition. The fluorescence peak
intensity of donor molecule was decrease and its bandwidth will
increases on the contrary of the acceptor molecule its intensity
increase gradually and its bandwidth decreases as the acceptor
concentration increase.

Abstract: In the current research the absorption and fluorescence spectrum of Coumarin (334) and Rhodamine (590) in ethanol solvent at different concentration (10-3, 10-4, 10-5) M had been studied. The absorption intensity of these dyes increases as the Concentration increase in addition to that the spectrum was shifted towards the longer wavelength (red shift). The energy transfer process has been investigated after achievement this condition. The fluorescence peak intensity of donor molecule was decrease and its bandwidth will increases on the contrary of the acceptor molecule its intensity increase gradually and its bandwidth decreases as the acceptor concentration increase.

In this paper, The transfer function model in the time series was estimated using different methods, including parametric Represented by the method of the Conditional Likelihood Function, as well as the use of abilities nonparametric are in two methods  local linear regression and cubic smoothing spline method, This research aims to compare those capabilities with the nonlinear transfer function model by using the style of simulation and the study of two models as output variable and one model as input variable in addition t

Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin