This is a contextual study in face and isotope science, and I have made it in one of the terms faces and isotopes, which is the word (bad). Quranic also, and that is at every aspect they mentioned.
The nature of the research required that it be divided into three sections:
The first topic: I singled it out to show the types of contextual connotations.
- The second topic: I singled it out to define the word bad and its meaning.
- The third topic: I devoted it to the study of the word bad and explaining the significance of the Quranic context on the additional meaning and the original meaning.
Conclusion: It mentioned the most important results, which are:
1- The significance of the Quranic context is one of the most important ways of interpreting the Qur’an, as it stems from the Qur’an itself.
2- The term “bad” is a comprehensive and comprehensive word for many meanings that have an impact on the human psyche.
3 - The fact that the words in the faces are intended according to the meanings that they bear in the origin of the term.
4- Additional meanings change according to context and clues.
The present study aims to describe the histological structure of kidney of, (Herpestes javanicus ) that inhabits Iraqi lands. Transverse sections of kidney stained with hematoxylin and eosin showed two distinct regions, the outer thin darkly stained cortex and inner thick lightly stained medulla, which further subdivided into external and internal medullary zones linked with one conical renal papilla. The lateral margin of the outer medullary tissue forms a secondary renal pyramid with a specialized fornix. All the nephrons in the kidney start with the renal corpuscle [Malpighian], which is formed from two distinct parts, these are a centrally located glomerulus, which represented by a tuft of blood capillaries and an outer Bowman’s capsu
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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
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