Colloidal silver nanoparticles were prepared by single step green synthesis using aqueous extracts of the leaves of thyme as a function of different molar concentration of AgNO₃ (1,2,3,4 mM(. The Field Emission Scanning Electron Microscopy (FESEM), UV-Visible and X-ray diffraction (XRD) were used to characterize the resultant AgNPs. The surface Plasmon resonance was observed at wavelength of 444 nm. The four intensive peaks of XRD pattern indicate the crystalline nature and the face centered cubic structure of the AgNPs. The average crystallite size of the AgNPs ranged from 18 to 22 nm. The FESEM image illustrated the well dispersion of the AgNPs and the spherical shape of the nanoparticles with a particle size distribution be

     In the present work, the possibility of treating many types of radioactive sources was examined practically. Six types of sealed radioactive sources were selected: ¹³⁷Cs, ¹³³Ba, ⁹⁰Sr, ¹⁵²Eu, ²²⁶Ra, and ²⁴¹Am. The sources were exposed to a neutron flux emitted from ²⁴¹Am/Be source for 33 days. The results showed a measurable reduction of activity for ²²⁶Ra, ²⁴¹Am, and ¹⁵²Eu, while the other radionuclides, ¹³⁷Cs, ¹³³Ba, and ⁹⁰Sr, showed less response to neutron incineration.

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

       Remote sensing is a source of up-to-date information. The present study relied on various approaches for gathering information, including descriptive, quantitative and quantitative analytical processes. Particularly,  we conducted the analysis of the satellite data ETM + of the satellite Landsat7 and the digital models of Digital Elevation Model of SRTM using ArcGIS9.2. The model depends on primary mathematical equations and  constitutes an essential base for GIS applications that rely on data, computer, and software, performing the processes of data entry, analysis and processing. This paper deals with the geomorphological characteristics of a selected study area in Kirkuk province. The cha

     World statistics declare that aging has direct correlations with more and more health problems with comorbid conditions. As healthcare communities evolve with a massive amount of data at a faster pace, it is essential to predict, assist, and prevent diseases at the right time, especially for elders. Similarly, many researchers have discussed that elders suffer extensively due to chronic health conditions.  This work was performed to review literature studies on prediction systems for various chronic illnesses of elderly people. Most of the reviewed papers proposed machine learning prediction models combined with, or without, other related intelligence techniques for chronic disease detection of elderly patie

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of t

      Background: Left ventricular function and volumes have major diagnostic and prognostic importance in patients with various cardiac diseases, such as ischemic heart disease which is a life-threatening heart disease condition characterized by systolic dysfunction and a decrease in cardiac output. 

According to left ventricular ejection fraction, the degree of ischemic heart disease was classified as mild, moderate, and severe. To determine cardiac function and hemodynamics, the echocardiography technique is used, which is a noninvasive diagnostic method.

Patients and Methods: The study included 216 patients between 25 and 75 years old; 121 males and 95 females; 265 normal individuals (age range: 25 to 75 years ol