The current study was to examine the reliability and effectiveness of using most abundant, inexpensive waste in the form of scrap raw zero valent aluminum ZVAI and zero valent iron ZVI for the capture, retard, and removal of one of the most serious and hazardous heavy metals cadmium dissolved in water. Batch tests were conducted to examine contact time (0-250) min, sorbent dose (0.25-1 g ZVAI/100 mL and 2-8 g ZVI/100 mL), initial pH (3-6), pollutant concentration of 50mg/L initially, and speed of agitation (0-250) rpm . Maximum contaminant removal efficiency corresponding to (90 %) for cadmium at 250 min contact time, 1g ZVAI/ 6g ZVI sorbent mass ratio, pH 5.5, pollutant concentration of 50 mg/L initially, and 250 rpm agitation speed wer

Solid dispersion is an attractive tool of pharmaceutical technology used to improve the physical properties of drugs. Among these properties is to enhance the solubility of the drugs.
Rebamipide is a poorly soluble drug of class IV of biopharmaceutical classification system (BCS).
Rebamipide is used as potent antiulcer, mucoprotective drug, by stimulating the generation of prostoglandine enhanced mucosal protection.
Rebamipide was formulated as a solid dispersion using different polymers such as pluronic F-127, PEG6000, PVP K30, and TPGS by using different preparation methods solvent evaporation, fusion, and kneading methods.
It was seen that rebamipide was successfully dispersed in a homogenous solid dispersion matrix by sol

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

This research aims to develop new spectrophotometric analytical method to determine drug compound Salbutamol by reaction it with ferric chloride in presence potassium ferricyanide in acid median to formation of Prussian blue complex to determine it by uv-vis spectrophotmetric at wavelengths rang(700-750)nm . Study the optimal experimental condition for determination drug and found the follows: 1- Volume of(10M) H2SO4 to determine of drug is 1.5 ml . 2- Volume and concentration of K3Fe(CN)6 is 1.5 ml ,0.2% . 3- Volume and concentration of FeCl3 is 2.5ml , 0.2%. 4- Temperature has been found 80 . 5- Reaction time is 15 minute . 6- Order of addition is (drug + K3Fe(CN)6+ FeCl3 + acid) . Concentration rang (0.025-5 ppm) , limit detecti

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database