Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

Separation of Trigonelline, the major alkaloid in fenugreek seeds, is difficult because the extract of these seeds usually contains Trigonelline, choline, mucilage, and steroidal saponins, in addition to some other substances. This study amis to isolate the quaternary ammonium alkaloid (Trigonelline) and choline from fenugreek seeds (Trigonella-foenum graecum L.) which have similar physiochemical properties by modifying of the classical method. Seeds were defatted and then extracted with methanol. The presence of alkaloids was detected by using Mayer's and Dragendorff's reagents. In this work, trigonilline was isolated with traces of choline by subsequent processes of purification using analytical and preparative TLC techniques.

We aimed to examine the effect of amoxicillin and azithromycin suspensions on the microhardness of sliver-reinforced glass ionomer and nano-resin modified glass ionomer (GI). Method: Thirty discs (2mm height x 4mm diameter) of each type of GI were prepared, which were randomly assigned to amoxicillin, azithromycin, and artificial saliva groups. Microhardness was evaluated by Vickers hardness test before and after three immersion cycles. Results: The overall model (P < 0.001), before/after intervention (P < 0.001), intervention group (type of antibiotic) (P=0.013), and type of glass ionomer (P < 0.001) showed significant differences among study groups (P < 0.001). Post hoc test showed only non-significant before/after difference for Azithrom

Resin-modified glass ionomer cement tends to shrink due to polymerization of the resin component. Additionally, they are more prone to syneresis and imbibition during the setting process. This in vitro study evaluates the impact of chitosan, a biopolymer that is, both biomaterial and biocompatible, on the strength of dentin bonding and compares it with ACTIVA Bio-ACTIVE Restorative. The present study was aimed to assess the impact of including chitosan into Fuji II on the shear bond strength between. the restoration material and tooth dentin,

The permeability estimates for the uncored wells and a porosity function adopting a modified flow zone index-permeability crossplot are given in this work. The issues with implementing that approach were mostly crossplots, due to the influence of geological heterogeneity, did not show a clear connection (scatter data). Carbonate reservoir flow units may now be identified and characterized using a new approach, which has been formally confirmed. Due to the comparable distribution and flow of clastic and carbonate rock fluids, this zoning method is most effective for reservoirs with significant primary and secondary porosity. The equations and correlations here are more generalizable since they connect these variables by combining cor

Objective: In this study ,the effects of silver nanoparticles (Ag NPs)were investigated on the liver and kidney tissues. Methodology: The produced nanoparticles have an average particle size of about 30 nm. Eighteen male albino rats were used by dividing them into three groups, each group comprise 6 rats. First group(control group) given food and water like other groups by liberty. Second group was tail injected by (AgNPs) at dose of (0.4 mg/kg. body weight/day). Third group was injected by (AgNPs) at dose of (0.6 mg/kg. body weight/day) for 15 days. All animals were sacrified at the end of experiment. The liver and kidney tissues specimens were fixed in 10% formalin and histological preparations were carried out then stained with H&E. Path