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Determination of Magnitudes and Orientation of the Paleostress of Bekhme Structure in Shaqlawa area, Northerneastern Iraq
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This study presents determination of the paleostress magnitudes and orientation of Bekhme Structure in Shaqlawa area northeastern Iraq. Paleostress Analysis of slip-fault measurements is performed using Right dihedral, Lisle diagram and Mohr Circles methods. Depending on Mohr Circles, Bott law and vertical thickness, the magnitudes of the paleostress at the time of the tectonic activity were determined. Firstly, Georient Software was used to estimate the orientation of the paleostresses (σ1, σ2 and σ3). Secondly, using the rupture –friction law, taking into account depth of the overburden and the vertical stress (σv) was calculated to determine the magnitude of the paleostresses (σ1=4500 bars, σ2=1900 bars and σ3=700 bars). The high magnitudes of the principal stress axes may be referred to the active tectonic events which led to the deformation of the area during Cretaceous and Tertiary periods. The study area shows that the poles of the measured faults lie in the reactivated area of Mohr circles. This indicates the unstability of the study area. The study area is important, due to possibility of exiting of the hydrocarbon is deposited. Fold- Thrust belt mark the deformation fronts of the major orogeny that form from the collision of Arabian Plate with Turkish and Iranian Plates.

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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