There are many diseases that affect the arteries, especially those related to their elasticity and stiffness, and they can be guessed by estimating and calculating the modulus of elasticity. Hence, the accurate calculation of the elastic modulus leads to an accurate assessment of these diseases, especially in their early stages, which can contribute to the treatment of these diseases early. Most of the calculations used the one-dimensional (1D) modulus of elasticity. From a mechanical point of view, the stresses to which the artery is subjected are not one-dimensional, but three-dimensional. Therefore, estimating at least a two-dimensional (2D) modulus of elasticity will necessarily be more accurate. To the knowledge of researchers, there is a lack of published research on this subject, as well as a paucity of research that designed and implemented a 2D tensile testing device (2DTTD). However, there is no inspection of arterial flexibility and elasticity using the 2DTTD adequately studied before. Therefore, the aim of this work is to design and implement the 2DTTD to scrutinize if there is a difference between the 1D and 2D tensile examination. Different sized rectangular silicone specimens were manually fabricated; they were tested individually using the fabricated 2DTTD, which mainly comprises four actuators synchronously working with the same velocity and axial load force, two at each axis. As expected using the 2DTTD, the dimensions of the specimen remarkably influence the tensile testing results; the strain and stress rates and the modulus of elasticity were influenced. To validate the acquired 2D tensile testing results, the 1D tensile testing was performed using the same fabricated 2DTTD and compared to results gained using another tensile testing apparatus. During the verification process, the input data for models calibration were sufficiently and accurately provided. The results showed reasonable precision and reliability in calculations of the 2D stress and strain rates during the whole deformation process. Each mechanical device that has been used has the possibility to stretch and squeeze the sample and log the change in the specimen elongation. The authors thought that the present experimental methodology was applied to the linear mechanical device successfully, where the encoder that is attached to tested samples was in the principal direction. The present method is used to measure the deformation in a manner that differs from the traditional digital image correlation method, which required a toolset that is more expensive, where it incorporates high-accuracy optical equipment.
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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Bacterial strains were isolated from oil-contaminated soil, in 2018, these isolates were identified, and with the aim of finding out the ability of these isolates to degrede the oil compounds, the color change of medium which added to it isolates was read by the method of Pacto Bushnell Hans. Then the change in the petroleum compounds was read by gas chromatography, for the most effective isolates.
The nine isolated bacterial showed different degrees of color change, and the isolates (Pseudomonas, Bacillus, Micrococcus) outperformed the color change amount (78, 78, 77) %, respectively, compared to the control, and the three isolates together showed the best color change of 90.7. % Compared to the control, and the
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Power-electronic converters are essential elements for the effective interconnection of renewable energy sources to the power grid, as well as to include energy storage units, vehicle charging stations, microgrids, etc. Converter models that provide an accurate representation of their wideband operation and interconnection with other active and passive grid components and systems are necessary for reliable steady state and transient analyses during normal or abnormal grid operating conditions. This paper introduces two Laplace domain-based approaches to model buck and boost DC-DC converters for electromagnetic transient studies. The first approach is an analytical one, where the converter is represented by a two-port admittance model via mo
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A specialized irradiation instrument "created instrument" was designed and created from various kinds and sizes of available plastic household-waste materials. In addition, a neutron beam collimator with a lid was designed and implemented. The collimator is with dimensions of 25 cm in height and 10 cm in inner diameter, while the lid dimensions are 11.5 cm height and outer diameter of 9.9 cm to perfectly match the inner diameter of the collimator with the possibility of movement (opening and closing), and also the shielding of the radioactive 241Am/Be neutron source with a recent activity of 37.5 mCi.
To investigate the efficiency of the "created instrument", ten hydrogenous material samples (ordinary p
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Most available methods for unit hydrographs (SUH) derivation involve manual, subjective fitting of
a hydrograph through a few data points. The use of probability distributions for the derivation of synthetic
hydrographs had received much attention because of its similarity with unit hydrograph properties. In this
paper, the use of two flexible probability distributions is presented. For each distribution the unknown
parameters were derived in terms of the time to peak(tp), and the peak discharge(Qp). A simple Matlab
program is prepared for calculating these parameters and their validity was checked using comparison
with field data. Application to field data shows that the gamma and lognormal distributions had fit well.<
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Ceramic body as a refractory was prepared by using shamoot, which is prepared by firing kaolin Duekhla at 1450 ºC at 2hr ,Flint clay ,Asbestos fiber(Anthophylite type)and Sodium silicts,Phosphoric acid solution as a binder . After miling, siving ,and mixing ,samples were formed, followed that drying, firing at different temperature. Phyical ,thermal and mechanical properties were measured .The conclusion behind the results that the refractory prepared from; 37.5% shamoote,25% Asbestos ,37.5% Flint clay and Phosforic acid solution fired at 1300 ºC gave a refractory material having melting temperature ;1490 ºC, thermal shock resistance 7 cycle, thermal conductivity 2.1w/m2.K, apperan
... Show MoreThe interaction between comet Hale-Bopp tail with the solar wind is investigated in the present paper using magneto-hydrodynamic (MHD) numerical simulation, which accounts for the presence of the interplanetary magnetic field (IMF). The simulation is based on three-dimensional Lax-Wendroff explicit scheme, providing second-order accuracy in space and time. The ions produced from the nucleus of the comet will add considerable effects on the microstructure of the solar wind, thus severely altering its physical properties. The present simulation focuses on careful analysis of these properties by means of simulating the behavior of the comet Hale-Bopp’s tail at 1 AU from the sun. These properties include the changes of the plasma density,
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Financial fraud remains an ever-increasing problem in the financial industry with numerous consequences. The detection of fraudulent online transactions via credit cards has always been done using data mining (DM) techniques. However, fraud detection on credit card transactions (CCTs), which on its own, is a DM problem, has become a serious challenge because of two major reasons, (i) the frequent changes in the pattern of normal and fraudulent online activities, and (ii) the skewed nature of credit card fraud datasets. The detection of fraudulent CCTs mainly depends on the data sampling approach. This paper proposes a combined SVM- MPSO-MMPSO technique for credit card fraud detection. The dataset of CCTs which co
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