Ibn al-Haytham is the pioneer of optics, which is a science that had described optics circumstances. Optics or landscape science as it was known by the Arab and Muslim scientists at the time when  is a science known as the conditions of sighters in their quantity and quality as the proximity and distance from the beholder , the different forms , conditions and what mediate between the beholder and the microscopes , to explain useful knowledge of what obscures the sight of the conditions of sighters and is used to measure the space Distant criminality and  mirrors.

Ibn al-Haitham wrote his famous book The Book of Mirrors and studied mistakes adopted the m

Biodiesel production from microalgae depends on the biomass and lipid production. Both biomass and lipid accumulation is controlled by several factors. The effect of various culture media (BG11, BBM, and Urea), nutrients stress [nitrogen (N), phosphorous (P), magnesium (Mg) and carbonate (CO₃)] and gamma (γ) radiation on the growth and lipid accumulation of   Dictyochloropsis splendida were investigated. The highest biomass and lipid yield of D. splendida were achieved on BG11 medium. Cultivation of D. splendida in a medium containing 3000 mg L⁻¹ N, or 160 mg L⁻¹ P, or 113 mg L⁻¹ Mg, or 20 mg L^-1 CO_3, led to enhanced growth rate. While u

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Solar collectors, in general, are utilized to convert the solar energy into heat energy, where it is employed to generate electricity. The non-concentrating solar collector with a circular shape was adopted in the present study. Ambient air is heated under a translucent roof where buoyant air is drawn from outside periphery towards the collector center (tower base). The present study is aimed to predict and visualize the thermal-hydrodynamic behavior for airflow under inclined roof of the solar air collector, SAC. Three-dimensional of the SAC model using the re-normalization group, RNG, k−ε turbulence viscus model is simulated. The simulation was carried out by using ANSYS-FLUENT 14.5. The simulation

In this paper, investigates the biosynthesis of gold nanoparticles (AuNPs) by biochemical method using Myrtus communis leaves extract as reducing agent and Chloroauric acid (HAuCl4) as precursors. X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and FTIR were used in addition to UV-visible spectroscopy (UV) in order to characterize the AuNPs. The biosynthesized AuNPs exhibited inhibitory effects on alpha amylase and alkaline phosphatase in sera of patient with type 2 Diabetes Miletus and the sera of healthy control subjects; the inhibition percentage with alpha amylase was 72 % and 45 % for patient and control group respectively. Oral consent obtained from the most of patients and healthy subjects before them being under