Abstract

Hexapod robot is a flexible mechanical robot with six legs. It has the ability to walk over terrain. The hexapod robot look likes the insect so it has the same gaits. These gaits are tripod, wave and ripple gaits. Hexapod robot needs to stay statically stable at all the times during each gait in order not to fall with three or more legs continuously contacts with the ground. The safety static stability walking is called (the stability margin). In this paper, the forward and inverse kinematics are derived for each hexapod’s leg in order to simulate the hexapod robot model walking using MATLAB R2010a for all gaits and the geometry in order to derive the equations of the sub-constraint workspaces for each

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Rationing is a commonly used solution for shortages of resources and goods that are vital for the citizens of a country. This paper identifies some common approaches and policies used in rationing as well asrisks that associated to suggesta system for rationing fuelwhichcan work efficiently. Subsequently, addressing all possible security risks and their solutions. The system should theoretically be applicable in emergency situations, requiring less than three months to implement at a low cost and minimal changes to infrastructure.

    This research aims to review the importance of estimating the nonparametric regression function using so-called Canonical Kernel which depends on re-scale the smoothing parameter, which has a large and important role in Kernel  and give the sound amount of smoothing .

We has been shown the importance of this method through the application of these concepts on real data refer to international exchange rates to the U.S. dollar against the Japanese yen for the period from January 2007 to March 2010. The results demonstrated preference the nonparametric estimator with Gaussian on the other nonparametric and parametric regression estima

In this paper three techniques for image compression are implemented. The proposed techniques consist of three dimension (3-D) two level discrete wavelet transform (DWT), 3-D two level discrete multi-wavelet transform (DMWT) and 3-D two level hybrid (wavelet-multiwavelet transform) technique. Daubechies and Haar are used in discrete wavelet transform and Critically Sampled preprocessing is used in discrete multi-wavelet transform. The aim is to maintain to increase the compression ratio (CR) with respect to increase the level of the transformation in case of 3-D transformation, so, the compression ratio is measured for each level. To get a good compression, the image data properties, were measured, such as, image entropy (He), percent root-

Background: Infection with Toxoplasma gondii (T. gondii) leads to activation of T-helper cells (Th-1 and Th-2) which are involved in the synthesis and release of different cytokines which may lead to endothelial dysfunction. Objectives: To evaluate the endothelial function in patients with acute toxoplasmosis. Methods: This case-control study involved 31 patients with toxoplasmosis aged 19 - 47 years matched with 20 healthy subjects. Anti-T. gondii antibody (IgG, IgM, IgA) was determined by direct antigen-antibody reaction. Interleukin-6(IL-6), endothelin-1 (ET-1) and human malondialdehyde (MDA) serum levels were measured. Results: IgM, IgG and IgA levels were high in the infected patients compared with controls (P < 0.01). Furthermore, IL-

       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.

In the present study ten samples of bottled water from Baghdad conservative were taken to measure the concentration of radon gas by using nuclear track detector LR-115.The result obtained are varying from(0.033)to(0.007)pCi.l-1and these values are very low than the allowed limits (5) pCi.l-1, and specific activity from bottled water has been calculated which was vary from (0.00027)to(0.00126) Bq.l-1 and these values are very low than allowed limits (0.0123) Bq.l-1 that mean the bottled water was treated with good treatment to decrease the side effect of radon

        A newly developed analytical method was conducted for the determination of Ketotifen fumarate (KTF) in pharmaceuticals drugs via quenching of continuous fluorescence of 9(10H)-Acridone (ACD). The method was applied using flow injection system of a new homemade ISNAG fluorimeter with fluorescence measurements at ± 90◦ via 2×4 solar cell. The calibration graph was linear in the range of 1-45 mmol/L, with correlation coefficient r = 0.9762 and the limit of detection 29.785 µg/sample from the stepwise dilution for the minimum concentration in the linear dynamic ranged of the calibration graph. The method was successfully applied to the determination of Ketotifen fumarate in two different pharma

In this study, the results of the uranium concentrations and specific activity in 10 rice samples are described using a solid-state track detector (CR-39). Samples were collected from various local Iraqi markets with different origins (Iraq, India, America, and Thailand). Our findings found that the results of uranium concentration in all studied samples are ranging from (0.55 ± 0.28 to 1.74 ± 0.31) ppm with a weighted average of (1.24 ± 0.99) ppm. Also, results demonstrate that the specific activity values of the studied samples swing between values of (6.88 ± 3.52 and 21.49 ± 3.85) Bq/Kg. The obtained results of the studied rice samples are indicated that it is less than the acceptable limit of those studies established by ma