In the current work various types of epoxy composites were added to concrete to enhance its effectiveness as a gamma- ray shield. Four epoxy samples of (E/clay/B4C) S1, (E/Mag/B4C) S2, (EPIL) S3 and (Ep) S4 were used in a comparative study of gamma radiation attenuation properties of these shields that calculating using Mont Carlo code (MCNP-5). Adopting Win X-com software and Artificial Neural Network (ANN), µ/ρ revealed great compliance with MCNP-5. By applying (µ/ρ) output for gamma at different energies, HVL, TVL and MFP have been also estimated. ANN technique was simulated to estimate (µ/ρ) and dose rates. According to the results, µ/ρ of all epoxy samples scored higher than standard concrete. Both S2 and S3 samples having h

Background: Odontogenic cysts are characterized by their sluggish growth and ability to enlarge, primarily affecting the oral and maxillofacial tissues. Timely diagnosis and treatment are crucial to prevent potentially serious consequences. The present study aimed to evaluate and compare the immunohistochemical expression of cytokeratin 19 in the epithelium of odontogenic keratocyst, dentigerous, and radicular cysts.

Methods: This study analyzed forty-five formalin-fixed, paraffin-embedded tissue blocks containing odontogenic cysts. The sample consisted of fifteen odontogenic keratocysts, fifteen dentigerous cysts, and

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Background: COVID-19 pandemic has influenced all life aspects; Dental staff, like other healthcare providers, may be exposed to COVID-19 as part of their work and its psychological impacts on healthcare workers should not be ignored

Objectives: To assess the anxiety, and fear from COVID-19 pandemic in dentists working in specialist dental centers: sample the Al-Resafa health directorate, and its relation between the anxiety, and COVID-19 fear with some of their demographic variables

Subjects and Methods: A cross-sectional study was conducted on 2nd Jan. to 14th Feb. 2021, by an electronic version of questionnaire through Google-form; the questionnaire was formed based on Mental-Health-American-Org

Background:sThe aims of this study were to evaluate and compare the ability of three different techniques to obdurate simulated lateral canals, evaluate the effect of the main canal curvature on obturation of lateral canals and compare the gutta-percha penetration between coronal and apical lateral canals. Materials and methods: Resin blocks with 30 straight and 30 curved were used in this study. Each canal has two parallel lateral canals. The main canal has 0.3 mm apical diameter and 0.04 taper. The canals were divided into six groups according to canal curvature and obturation techniques used (n=10): Groups C1 and C2: straight and curved canals obturated with continuous wave technique using E&Q masterTM system. Groups O1 and O2: straight

Arcology is a new vision of urban design for densely populated settlements. It is referred to as the urban formation aimed at achieving the highest level of utilization in the least area of land to reduce human impact on the environment. Moreover, the integration of this formation with the environment will work to reduce energy consumption, and achieve an economy that is characterized by adaptation and self-sufficiency, leading towards sustainable urbanism. The current study deals with this new concept in sustainable urban design, in an attempt to investigate its impact on traditional urban formation and to make a comparison between them. The lack of a clear conception of the term ‘arcology’ and comparing it with the organic compact urb

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m

Autorías: Nuha Mohsin Dhahi, Muhammad Hamza Shihab. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 6, 2022. Artículo de Revista en Dialnet.