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jnhm-515
MULTIVARIATE ANALYSIS OF THE STEM ANATOMICAL CHARACTERS OF TERMINALIA L. (COMBRETACEAE) IN EGYPT
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A comparative investigation of the anatomical characters through a microscopical examination of the prepared transverse sections of the stem was carried out. Six plates with 32 photomicrographs were provided to convincingly show the considerable variations of anatomical characters within the nine examined species. The matrix of 18 anatomical characters which included nine quantitative and nine qualitative was applied for the clustering analysis (CA) followed by the principal component analysis (PCA) using the Multivariate Analysis of Ecological Data, PC-ORD.
The results exhibited significant variations among the species resulting in the construction of an artificial key; this key accurately represents a sufficient tool to display the considerable variation among the recognized species prominently. The distinction between Terminalia L., 1767 species based on significant variations in the elements of stem anatomy; axial parenchyma and ray characteristics were considered as important parameters, while vessel diameter, fiber wall thickness, etc. were considered minor characters to differentiate between the studied species. The potential usefulness of the differentiation of these species properly maintains a profound efficiency in pharmaceutical and traditional medicine.

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Publication Date
Thu Jun 20 2024
Journal Name
Fizjoterapia Polska
Development Artificial Neural Network (ANN) computing model to analyses men's 100¬meter sprint performance trends
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Coaches and analysts face a significant challenge of inaccurate estimation when analyzing Men's 100 Meter Sprint Performance, particularly when there is limited data available. This necessitates the use of modern technologies to address the problem of inaccurate estimation. Unfortunately, current methods used to estimate Men's 100 Meter Sprint Performance indexes in Iraq are ineffective, highlighting the need to adopt new and advanced technologies that are fast, accurate, and flexible. Therefore, the objective of this study was to utilize an advanced method known as artificial neural networks to estimate four key indexes: Accelerate First of 10 meters, Speed Rate, Time First of 10 meters, and Reaction Time. The application of artifi

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