In this research, has been to building a multi objective Stochastic Aggregate Production Planning model for General al Mansour company Data with Stochastic  demand under changing of market and uncertainty environment in aim to draw strong production plans.  The analysis to derive insights on management issues regular and extra labour costs and the costs of maintaining inventories and good policy choice under the influence medium and optimistic adoption of the model of random has adoption form and had adopted two objective functions total cost function (the core) and income and function for a random template priority compared with fixed forms with objective function and the results showed that the model of two phases wit

Composite materials are widely used in the engineered assets as aerospace structures, marine and air navigation owing to their high strength/weight ratios. Detection and identification of damage in the composite structures are considered as an important part of monitoring and repairing of structural systems during the service to avoid instantaneous failure. Effective cost and reliability are essential during the process of detecting. The Lamb wave method is an effective and sensitive technique to tiny damage and can be applied for structural health monitoring using low energy sensors; it can provide good information about the condition of the structure during its operation by analyzing the propagation of the wave in the

Background: Periodontal diseases are bacterial infections of the gingiva, bone and attachment fibers that support the teeth and hold them in the jaw. α-amylase is an enzyme, produced mainly by parotid gland and it seems to play a role in maintaining mucosal immunity. Aims of the study: Determine the salivary levels of α-Amylase and flow rate and their correlations with clinical periodontal parameters(Plaque Index , Gingival Index , Bleeding on Probing , Probing Pocket Depth , and Clinical Attachment Level ) and the correlation between α-Amylase with flow rate of study groups that consist of ( patients had gingivitis and patients had chronic periodontitis with different severities(mild ,moderate ,severe) and control group . Ma

We observed strong nonlinear absorption in the CdS nanoparticles of dimension in the range 50-100 nm when irradiant with femtosecond pulsed laser at 800 nm and 120 GW/cm 2 irradiance intensity. The repetition rate and average power were 250 kHz and

This study aims to fabricate and assess the β-tricalcium phosphate (β-TCP) bioactive ceramic coat layer on bioinert ceramic zirconia implants through the direct laser melting technique by applying a long-pulsed Nd:YAG laser of 1064 nm. Surface morphologies, adherence, and structural change in the coatings were evaluated by optical microscopy, field emission scanning electron microscope, hardness, and x-ray diffractometer. The elastic modulus (EM) of the coating was also determined using the nanoindentation test. The quality of the coating was improved when the laser power was 90 W with a decrease in the scan speed to 4 mm s−1. The chemical composition of the coat was maintained after laser processing; also, the Energy Dispersive

Fiber Bragg Grating has many advantages where it can be used as a temperature sensor, pressure sensor or even as a refractive index sensor. Designing each of this fiber Bragg grating sensors should include some requirements. Fiber Bragg grating refractive index sensor is a very important application. In order to increase the sensing ability of fiber Bragg gratings, many methods were followed. In our proposed work, the fiber Bragg grating was written in a D-shaped optical fiber by using a phase mask method with KrFexcimer. The resultant fiber Bragg grating has a high reflectivity 99.99% with a Bragg wavelength of 1551.2 nm as a best result obtained from a phase mask with a grating period of 1057 nm. In this work it was found that the rota

background: human epidermal growth factor receptor-2 (her2/neu) is related to growth factor receptors with alkaline kinase activity and it is regarded as important prognostic and therapeutic factor that can depended on in breast cancer therapy. HER2/neu expression by immunohistochemistry (IHC) is submitted to a great in terob server inconsistency. Subsequently additional confirmatory tests for assessment of gene alterations and amplification status are needed for patients with early or metastatic breast cancer. In situ hybridization techniques and specifically Chromogenic in situ hybridization (CISH) was arise as a practical, cost-effective, and alternative to fluorescent in situ hybridization in testing for gene alterationAims of the study

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-

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