This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita

Recently, numerous the generalizations of Hurwitz-Lerch zeta functions are investigated and introduced. In this paper, by using the extended generalized Hurwitz-Lerch zeta function, a new Salagean’s differential operator is studied. Based on this new operator, a new geometric class and yielded coefficient bounds, growth and distortion result, radii of convexity, star-likeness, close-to-convexity, as well as extreme points are discussed.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

The experiment was conducted to study the effect of leaves extract of Salvia sclarea , Rosmarinus officinalis and Thymus vulgaris with 10% and 30% concentration on germination of seeds and growth of seedlings . The effect of these extracts on infection percentage of seeds decay and surface growth of Rhizoctonia solani . The results showed that the three extracts effected significantly to reduced percentage of seeds germination, acceleration of germination , promoter indicator , infection percentage of seeds decay and surface growth of R. solani especially in 30% concentration .

     Pregnancy and delivery are physiological conditions that are marked by abrupt alterations to hormones, immunological and molecular characters. The current study aimed to evaluate oxytocin (OT), prolactin (PRL), cortisol and insulin growth factor-2 (IGF-2) levels as physiological biomarkers; programmed cell death protein-1 (PD-1), programmed cell death ligand-1 (PD-L1),interleukin-6 (IL-6) as immunological biomarkers, and single nucleotide polymorphisms (SNPs; rs53576 and rs2254298) of oxytocin receptor gene OXTR as molecular factors in samples of Iraqi women undergoing caesarean section (CS) and normal delivery (ND). Blood samples were collected from 96 pregnant women at term with ages ranging between 16-43 years. Regarding