The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when diagnosing a tissue sample. Small, unnoticeable changes in pixel density may indicate the beginning of cancer or tear tissue in the early stages. These details even expert pathologists might miss. Artificial intelligence (A.I.) and D.L. revolutionized radiology by enhancing efficiency and accuracy of both interpretative and non-interpretive jobs. When you look at AI applications, you should think about how they might work. Convolutional Neural Network (C.N.N.) is a part of D.L. that can be used to diagnose knee problems. There are existing algorithms that can detect and categorize cartilage lesions, meniscus tears on M.R.I., offer an automated quantitative evaluation of healing, and forecast who is most likely to have recurring meniscus tears based on radiographs.
A modified chemical method was used to prepare titanium dioxide nanoparticles (TiO2 NPs), which were diagnosed by several techniques: X-ray diffraction, Fourier transform infrared, field emission scaning electron microscopy, energy disperse X-ray, and UV-visible spectroscopy, which proved the success of the preparation process at the nanoscale level. Where the titanium oxide particles have an average particle size equal to 6.8 nm, titanium dioxide particles were used in the process of adsorption of Congo red dye from its aqueous solutions using a batch system. The titanium oxide particles gave an adsorption efficiency of Congo red dye up to more than 79 %. The experimental data of the adsorption process were analyzed with kinetic models and
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The insect is diagnosed and named by the National Center of Biotechnology Information (NCBI), USA as the Mint leaf Beetle Chrysolina herbacea alnadawi (Duftschmid, 1825), (Coleoptera: Chrysomelidae). The diagnosis was performed depending on the DNA analysis by 73% similarity with Chrysolina herbacea (Duftschmid, 1825) sequence, In the present study. It is recorded as a new insect pest on mint plant Mentha puleguim (L,1753) (Lamiaceae). DNA analysis confirmend that it is recorded for the first time in Iraq and the Arab world as well as the Middle East. Those insects were observed initially during August 2017 in residential gardens of Al-Bonooq district in Baghdad / Iraq.
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The study was conducted to measure diatom species diversity in the lotic ecosystem across the Wasit Province for 12 months. The quantitative study of diatoms (phytoplankton) was investigated in the Tigris river. The density of algae was ranged from 60989 cell×103/l to 112780.82 cell×103/l in the five sites. These algae were belonging to 39 genera. The richness index values ranged from 1.53 at site 5 in January 2016 to 6.34 at site 1 and June2015. Shannon-Weiner diversity index (H´) was 2.33 in February 2016 and 3.72 in June 2015 both values at site 3, whereas Evenness index was 0.54 at site 5 in March2016 and 0.98 at site 1 in both August2015 and May2016. The lack of homogeneity of the appearance of species indicates the dominance of a
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This paper investigates the experimental response of composite reinforced concrete with GFRP and steel I-sections under limited cycles of repeated load. The practical work included testing four beams. A reference beam, two composite beams with pultruded GFRP I-sections, and a composite beam with a steel I-beam were subjected to repeated loading. The repeated loading test started by loading gradually up to a maximum of 75% of the ultimate static failure load for five loading and unloading cycles. After that, the specimens were reloaded gradually until failure. All test specimens were tested under a three-point load. Experimental results showed that the ductility index increased for the composite beams relative to the reference specim
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Glass Fiber Reinforced Polymer (GFRP) beams have gained attention due to their promising mechanical properties and potential for structural applications. Combining GFRP core and encasing materials creates a composite beam with superior mechanical properties. This paper describes the testing encased GFRP beams as composite Reinforced Concrete (RC) beams under low-velocity impact load. Theoretical analysis was used with practical results to simulate the tested beams' behavior and predict the generated energies during the impact loading. The impact response was investigated using repeated drops of 42.5 kg falling mass from various heights. An analysis was performed using accelerometer readings to calculate the generalized inertial load
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in this paper, we study and investigate a simple donor-acceptor model for charge transfer formation using a quantum transition theory. The transfer parameters which enhanced the charge transfer and the rate of the charge transfer have been calculated. Then, we study the net charge transfer through interface of Cu/F8 contact devices and evaluate all transfer coefficients. The charge transfer rate of transfer processes is found to be dominated in the low orientation free energy and increased a little in decreased potential at interface comparison to the high potential at interface. The increased transition energy results in increasing the orientation of Cu to F8. The transfer in the system was more active when the system has large driving for
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Anastatica Hierochuntica L. Used As an Alternative of Conjugated Estrogen (Premarin) in Rabbit Females