Metal corrosion is a destructive process for many industrial operations, including oil well acidizing and acid pickling. Therefore, numerous efforts made by many researchers to control the steel corrosion. In the present work, A (E)-4-(((4-(5-mercapto-1,3,4-oxadiazol-2-yl) phenyl) amino) methyl)-2-methoxyphenol (MOPM) has been synthesized and characterized as a new corrosion inhibitor for mild steel in 0.1 M hydrochloric acid. FTIR and 1 HNMR were used in the diagnosis of MOPM, while electrochemical polarization technique was employed to test the performance of inhibitor at various temperatures and inhibitor concentrations. Electrochemical studies showed that MOPM acts as a mixed-type inhibitor with a maximum inhibition efficiency of

Encryption of data is translating data to another shape or symbol which enables people only with an access to the secret key or a password that can read it. The data which are encrypted are generally referred to as cipher text, while data which are unencrypted are known plain text. Entropy can be used as a measure which gives the number of bits that are needed for coding the data of an image. As the values of pixel within an image are dispensed through further gray-levels, the entropy increases. The aim of this research is to compare between CAST-128 with proposed adaptive key and RSA encryption methods for video frames to determine the more accurate method with highest entropy. The first method is achieved by applying the "CAST-128" and

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

This study had succeeded in producing a new graphical representation of James abacus called nested chain abacus. Nested chain abacus provides a unique mathematical expression to encode each tile (image) using a partition theory where each form or shape of tile will be associated with exactly one partition.Furthermore, an algorithm of nested chain abacus movement will be constructed, which can be applied in tiling theory.

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

This study focused on the improvement of the quality of gasoline and enhancing its octane number by the reduction of n-paraffins using zeolite 5A. This study was made using batch and continuous mode. The parameters which affected the n-paraffin removal efficiency for each mode were studied. Temperature (30 and 40 ˚C) and mixing time up to 120 min for different amounts of zeolite ranging (10-60 g) were investigated in a batch mode. A maximum removal efficiency of 64% was obtained using 60 g of zeolite at 30 ˚C after a mixing time 120 min. The effect of feed flow rate (0.3-0.8 l/hr) and bed height (10-20 cm) were also studied in a continuous mode. The equilibrium isotherm study was made using different amounts of zeolite (2-20 g) and the