Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Antimicrobial and antiyeast activity of ethanolic and aqueous extract of grape fruit seed (Citrus paradise ; Rutaceaa) was examined against 10 bacterial and 2 yeast strains. The level of the antimicrobial effects was established using an in vitro agar assay and minimum inhibitory concentration (MIC). In general ethanolic extract were more effective on gram positive bacteria than gram negative bacteria and strongest antimicrobial effect against Streptococcus pyogenes and Salmonella entritidis. Other tested bacteria and yeasts were sensitive to extract ranging from 4 to 16 mg/ml and more.

The crude aqueous extract of menthespicata , the objective of this study was to investigate the effects of this extraction , on the histological changes of the ovares and levels of sex hormone , ( FSH , LH , Estradiol ) in albino female mice . the extract was given orally for( 45 ) days . fourty mature female mice were used in this study , the animals divided into four major groups . each group was include ten mice . the first three groups was given different concentration )) (21 , 14 , 7 %) . While the fourth group considered as control group which had been administrated tab water . For ( 45 ) days each group had been killed for hormonal assay in blood

Background: In this work, a fingerprint powder was used to reveal latent fingerprints from different surfaces. This powder was derived from the Date fronds as activated carbon. Methods: In preparing the activated carbon, three parameters were studied: activation time, activation temperature, and impregnation ratio. Fourier Transform Infrared Spectroscopy (FTIR) was used to characterize the prepared Date frond activated carbon (DFAC) as well as the raw material (Date frond plant). Brunauer-Emmett-Teller (BET) was used to measure the specific surface area of DFAC. The surface shape and the element composition of the prepared powder were investigated using (SEM-EDS) analysis. A Central Composite Design (CCD) was employed to determine th

ABSTRACT

         The effect of adding raw bacteriocin produced by Lactobacillus bulgaricus to cheese curd at an amount of (5 and 10 and 15) mL/kg cheese as a biological preservative to prolong the shelf life of soft cheese, in addition to the control treatment, knowing that each 1 mL of bacteriocin filter contains 15 units/ mL of bacteriocin. The results of the physicochemical, microbial and sensory tests for cheese stored at refrigerator temperature for a period (zero) to (21) d of adding bacteriocin showed the superiority of the treatment of cheese added to 15 mL/kg cheese of bacteriocin over the rest of the other treatments during the storage period, wh

Rutting is a crucial concern impacting asphalt concrete pavements’ stability and long-term performance, negatively affecting vehicle drivers’ comfort and safety. This research aims to evaluate the permanent deformation of pavement under different traffic and environmental conditions using an Artificial Neural Network (ANN) prediction model. The model was built based on the outcomes of an experimental uniaxial repeated loading test of 306 cylindrical specimens. Twelve independent variables representing the materials’ properties, mix design parameters, loading settings, and environmental conditions were implemented in the model, resulting in a total of 3214 data points. The network accomplished high prediction accuracy with an R

ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous rel

Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability