The aquatic Heptageniidae family of Ava-sheen branch (Greater Zab Tributary) in Duhok Governorate/ Iraq was studied. Samples were collected using Surber stream sampler to study their diagnostic morphological characteristics and molecular phylogenetic profile using a nuclear gene 16s ribosomal RNA. The morphological and molecular identification supported that the three species, Epeorus longimanus, Heptagenia elegantula and Ephemerella cornutus belong to Heptageniidae family, while the molecular results also confirmed the monophyletic origin of these three genera.

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

There is an increasing interest in the use of plant extracts as therapeutic agents, particularly their capacity to inhibit the growth of pathogenic microorganisms. In this study antibacterial effect of Malva sylvestris, Anastatica hierochuntica and Vitis vinifera leaves extracts were evaluated against Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, Staphylococcus aureus and Proteus mirabilis. The in vitro antibacterial activity was performed using agar well diffusion method and the minimum inhibitory concentration (MIC) was determined by microtitration technique. The result indicated that the extract of V. vinifera leaves inhibited with the growth of gram-positive bacteria, as well as gram-negative bacteria while the extract

In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen

This paper presents a robust algorithm for the assessment of risk priority for medical equipment based on the calculation of static and dynamic risk factors and Kohnen Self Organization Maps (SOM). Four risk parameters have been calculated for 345 medical devices in two general hospitals in Baghdad. Static risk factor components (equipment function and physical risk) and dynamics risk components (maintenance requirements and risk points) have been calculated. These risk components are used as an input to the unsupervised Kohonen self organization maps. The accuracy of the network was found to be equal to 98% for the proposed system. We conclude that the proposed model gives fast and accurate assessment for risk priority and it works as p

Background: Polymethyl methacrylate (PMMA) is the most commonly used material in denture fabrication. The material is far from ideal in fulfilling the mechanical requirements. Midline fracture; poor thermal conductivity and water sorption, are common problem in this material. The purpose of this study was to evaluate the effect of addition of surface treated Aluminum oxide nano fillers on some properties of heat cured (PMMA). Materials and methods: In addition to controlled group of heat cured PMMA the silanized (Al2O3) nanoparticles was added to PMMA powder by weight in three different percentages 1wt%, 2wt% and 3wt%, mixed by probe ultra-sonication machine. 200 specimens were constructed and divided into 5 groups according to the test (e

Background:    Hip fractures are common, morbid, and costly health events that threaten independence and function of older patients . Early functional recovery is extremely important for elderly patients with femoral intertrochanteric fractures to shorten their hospital stay, omit the necessity of the further nursing and care services, and reduce associated medical expenditures . Assessment of  (ADL) is an important predictor of outcomes Of orthopedic surgery in very early postoperative period at two weeks and a month postoperatively using Japanese orthopedic score get a quick recovery of ADL & return the patient to his or her premorbid level function reducing the morbidity and mortality.