Mosquitoes like Culex quinquefasciatus are the primary vector that transmits many causes of diseases such as filariasis, Japanese encephalitis, and West Nile virus, in many countries around the world. The development in the scientific fields, such as nanotechnology, leads to use this technique in control programs of insects including mosquitoes through the use of green synthesis of nanoemulsions based on plant products such as castor oil.  Castor oil nanoemulsion was formulated in various ratios comprising of castor oil, ethanol, tween 80, and deionized water by ultrasonication. Thermodynamic assay improved that the formula of (10 ml) of castor oil, ethanol (5ml), tween 80 (14 ml) and deionized water (71ml)   was mor

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

       The current research aims to build a training program for chemistry teachers based on the knowledge economy and its impact on the productive thinking of their students. To achieve the objectives of the research, the following hypothesis was formulated:

   There is no statistically significant difference at (0.05) level of significance between the average grades of the students participating in the training program according to the knowledge economy and the average grades of the students who did not participate in the training program in the test of productive thinking. The study sample consisted of (288) second intermediate grade students divided into (152) for the control group

This work includes design, implementation and testing of a microcontroller – based spectrum analyzer system. Both hardware and software structures are built to verify the main functions that are required by such system. Their design utilizes the permissible and available tools to achieve the main functions of the system in such a way to be modularly permitting any adaptation for a specific changing in the application environment. The analysis technique, mainly, depends on the Fourier analysis based methods of spectral analysis with the necessary required preconditioning processes. The software required for waveform analysis has been prepared. The spectrum of the waveform has been displayed, and the instrument accuracy has been checked.

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Fullerene nanotube was synthesized in this research by pyrolysis of plastic waste Polypropylene (PP) at 1000 ° C for two hours in a closed reactor made from stainless steel using molybdenum oxide (MoO₃) as a catalyst and nitrogen gas. The resultant carbon was purified and characterized by energy dispersive X-ray spectroscopy (EDX), X-ray powder diffraction (XRD). The surface characteristics of C₆₀ nanotubes were observed with the Field emission scanning electron microscopy (FESEM). The carbon is evenly spread and has the highest concentration from SEM-EDX characterization. The result of XRD and FESEM shows that C₆₀ nanotubes are present in Nano figures, synthesized at 1000 ° C and with pyrolysis tempera

The effect of short range correlations on the inelastic longitudinal Coulomb form
factors for the lowest four excited 2+ states in 18O is analyzed. This effect (which
depends on the correlation parameter β) is inserted into the ground state charge
density distribution through the Jastrow type correlation function. The single particle
harmonic oscillator wave function is used with an oscillator size parameter b. The
parameters β and b are, considered as free parameters, adjusted for each excited state
separately so as to reproduce the experimental root mean square charge radius of
18O. The model space of 18O does not contribute to the transition charge density. As
a result, the inelastic Coulomb form factor of 18

In this work we study the influence of the laser pulse energy and ablation time on the aluminum nanoparticles productivity during nanosecond laser ablation of bulk aluminum immersed in liquid.
Aluminum nanoparticles were synthesized by pulsed laser ablation of Al targets in ethanol for 3-8 minutes using the 1064 nm wavelength of a Nd:YAG laser with energies of 300-500 mJ per pulse.The laser energy was varied between 300 and 500 mJ/pulse, whereas the ablation time was set to 5 minutes. UV-Visible absorption spectra was used for the characterization and comparison of products.