Natural Radioactive Decay

This chapter is primarily concerned with natural radioactive decay. Generally speaking, there are two types of natural radioactive decays: alpha decays “which contain two neutrons and two protons” emitted from radon gas; additionally, nuclear decay by emission of photons (γ-decay). This chapter aims to describe γ and alpha loss of nuclei and demonstrates how to measure the radioactive material naturally using solid-state nuclear track detector (SSNTD) and high purity Germanium detector (HPGD). Also, methods of measuring the different characteristics of the alpha particle using the track profile technique (TPT) will be presented. Finally, results will be presented in the alpha and radon measurements. The concentration of aerosols has attracted much attention by many researchers in the past decade. Research has shown that aerosols are responsible for harmful chemical reactions that lead to the physical degradation of the stratospheric ozone layer. Moreover, aerosols increase the risk of developing cancer in humans when inhaled in large proportions. Therefore, neutron activation analysis (NAA) is a very important application to measure these concentrations

Characterization of Inertial Electrostatic Confinement Fusion Plasma Device

Inetial electrostatic confinment fusion (IECF) device constructed at the Egyptian Atomic Energy Authority (EAEA-IEC), is introduced the characterization of the IEC plasma device. The x-ray and visible light emissions in IEC plasma device were investigated by employing time -resolved detector and measure of the total amount of visible light using lux meter

Synthesis of Gemini cationic surfactants based on natural nicotinic acid and evaluation of their inhibition performance at C-steel/1 M HCl interface: Electrochemical and computational investigations

Herein, we prepare effective Gemini cationic surfactants (CSII, CSIV) and characterize them using FT-IR and 1HNMR spectroscopy. The adsorptive properties of CSII and CSIV at HCl/air and C-steel/HCl interfaces were examined with surface tension and electrochemical parameters, respectively. The critical micelle concentration (CMC) of the CSII and CSIV indicated their adsorption affinity at the HCl/air interface. Where, aliphatic chains increase surface coverage percentage and aid in surfactant adsorption. The electrochemical parameters of C-steel in 1 M HCl were studied using electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP) at different temperatures. The charge transfer resistance of the C-steel electrode was enhanced from 28.2 Ω.cm2 to 770.79 and 831.45 Ω.cm2 after adding 5 × 10−4 M of CSII and CSIV, respectively. Both CSII and CSIV act as mixed inhibitors with inhibition performance exceeding 97% due to their highly adsorption affinity. The chemical adsorption affinity of these compounds is suggested by the higher adsorption energy (∆G*ads) values (>−40 kJ mol−1) according to the Langmuir isotherm model. The theoretical calculations including DFT, and Monte Carlo simulation (MCs) provide insight into the relationship between corrosion inhibition and molecular structure, where the calculated parameters agree with the experimental results

Characterization of an Atmospheric-pressure Cold Plasma Jet

Cold atmospheric pressure plasma jets are playing important role in various plasma applications. Each jet is characterized by providing its operational parameters such as power, type of gas, plasma temperature and density, electrode system and geometrical jet (radius, length). The velocity of the plasma jet has been observed by time of flight (TOF) using optical fiber cable and Photomultiplier tube, the measured average plasma velocity is about 106 cm/sec

VirNet: Deep attention model for viral reads identification.

Metagenomics shows a promising understanding of function and diversity of the microbial communities due to the difficulty of studying microorganism with pure culture isolation. Moreover, the viral identification is considered one of the essential steps in studying microbial communities. Several studies show different methods to identify viruses in mixed metagenomic data using homology and statistical techniques. These techniques have many limitations due to viral genome diversity. In this work, we propose a deep attention model for viral identification of metagenomic data. For testing purpose, we generated fragments of viruses and bacteria from RefSeq genomes with different lengths to find the best hyperparameters for our model. Then, we simulated both microbiome and virome high throughput data from our test dataset with aim of validating our approach. We compared our tool to the state-of-the-art statistical tool for viral identification and found the performance of VirNet much better regarding accuracy on the same testing data

Segmentation of Spectral Plant Images Using Generative Adversary Network Techniques

The spectral image analysis of complex analytic systems is usually performed in analytical chemistry. Signals associated with the key analytics present in an image scene are extracted during spectral image analysis. Accordingly, the first step in spectral image analysis is to segment the image in order to extract the applicable signals for analysis. In contrast, using traditional methods of image segmentation in chronometry makes it difficult to extract the relevant signals. None of the approaches incorporate contextual information present in an image scene; therefore, the classification is limited to thresholds or pixels only. An image translation pixel-to-pixel (p2p) method for segmenting spectral images using a generative adversary network (GAN) is presented in this paper. The p2p GAN forms two neuronal models. During the production and detection processes, the representation learns how to segment ethereal images precisely. For the evaluation of the results, a partial discriminate analysis of the least-squares method was used to classify the images based on thresholds and pixels. From the experimental results, it was determined that the GAN-based p2p segmentation performs the best segmentation with an overall accuracy of 0.98 ± 0.06. This result shows that image processing techniques using deep learning contribute to enhanced spectral image processing. The outcomes of this research demonstrated the effectiveness of image-processing techniques that use deep learning to enhance spectral-image processing

Smart fpga-based system for enhancing educational programs

[abstract not available