Background/Aim: The timing of a tooth's eruption can be affected by a variety of factors. The nutritional status has an impact on the development of a child's body. The purpose of the study was to analyse the number of children aged 6 to 9 in an Iraqi Arab population who had erupted permanent first molars and to examine how nutritional status affected the timing and level of emergence. Methods: A total of 330 boys and girls, in first grade elementary school, made up the sample. First molars that had erupted were noted, along with the level of the eruption. Each child's nutritional status was evaluated by recording their height and weight and body mass index (BMI) value was compared to the 2007 WHO reference. Results: Girls had highe

The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be

The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting

Background: Trauma to the anterior teeth is a common injury in young children. Themaxillary incisors being the most affected. Although root fractures are rare, theydo occur and were previously and often considered hopeless and were extracted.The time between the injury and the initiation of treatment, level of the fractureline, and stage of root development are some criteria to be considered whenchoosing a treatment approach for a complicated tooth fracture. This case reportdescribes the management of a traumatized immature maxillary central incisorwith Elise class IV fracture with vertical oblique subgingival fracture of the root.Materials and method: Apexification was carried out using biodentine followed byremoval of the fracture

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

Background: Dental caries is one of the most significant problems in world health care. Restoring carious primary teeth is one of the major treatment goals for Children, and the light activated resin restoration materials like composite, resin-modified glass ionomer and polyacid-modified which was introduced in dentistry in 1970, widely used in clinical dentistry but its application increased dramatically in recent years because of its biocompatibility, color matching, good adhesive properties of its resemblance in physical and mechanical aspects to tooth. The aim of this study: To evaluate the microleakage of Polyacid-Modified Composite resin Compared to Flowable Hybrid Composite and Resin-Modified Glass ionomer cement. Materials and me

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

This paper focused on the stone matrix asphalt (SMA) technology that was developed essentially to guard against rutting distress. For this procedure, fibers play a racy role in stabilizing and preventing the drain down problem caused by the necessity of high binder content coupled with their strengthening effect. A set of specimens with cylindrical and slab shapes were fabricated by inclusions jute, polyester, and carbon fibers. For each type, three contents of 0.25%, 0.5%, and 0.75% by weight of mixture were added by lengths of 5, 7.5, and 10 mm. The prepared mixtures were tested to gain the essential pertained parameters discriminated by the values of drain down, Marshall quotient, rut depth, and dynamic stability. It

Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect