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

Image is an important digital information that used in many internet of things (IoT) applications such as transport, healthcare, agriculture, military, vehicles and wildlife. etc. Also, any image has very important characteristic such as large size, strong correlation and huge redundancy, therefore, encrypting it by using single key Advanced Encryption Standard (AES) through IoT communication technologies makes it vulnerable to many threats, thus, the pixels that have the same values will be encrypted to another pixels that have same values when they use the same key. The contribution of this work is to increase the security of transferred image. This paper proposed multiple key AES algorithm (MECCAES) to improve the security of the tran

Enhancement of the performance for hybrid solar air conditioning system was presented in this paper. The refrigerant temperature leaving the condenser was controlled using three-way valve, this valve was installed after the compressor to regulate refrigerant flow rate towards the solar system. A control system using data logger, sensors and computer was proposed to set the opening valve ratio. The function of control program using LabVIEW software is to obtain a minimum refrigerant temperature from the condenser outlet to enhance the overall COP of the unit by increasing the degree of subcooled refrigerant. A variable load electrical heater with coiled pipe was used instead of the solar collector and the storage tank to simulate the sola

Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200

     Bacillus subtilis, an isolate of bacillus genus, was obtained from the laboratories of Ministry of Science and Technology. The best efficient Bacillus subtilis isolate in cellulose and semi-cellulose hydrolysis was treated with Dielectric-barrier Discharge (DBD). Atmospheric cold plasma technique (non-thermal) was used by exposing them at different times (2, 3, 4 and 5 mins) separately as a first stage, and then 60 seconds after any treatment separately as a second stage. After 48 hours, the difference between the plasma source and the sample was fixed at 0.5 cm. The results showed a variation in the growth of the isolate according to the exposure time by the appearance of culture turbidity and the estimation o