An optical system including quantum dot cylindrical Fresnel lens (CFL) has been designed by using Zemax optical designing program. Quantum dot cylindrical Fresnel lens has a relatively small thickness compared to conventional lenses and high absorbance. It contains grooves in the form of parallel lines, and each groove represents an individual lens that works to change the path of light falling on it to a single focal line. (CFL) is characterized by its small focal length despite its large area and small thickness, due to the nature of its design that gives this feature, which is applied in many optical systems (imaging and non- imaging system). In this paper, the visual properties of the (CFL) were studied as it is one of the impor

Mn2+ and Ce3+ Doped ZnS nanocrystals were prepared by a simple microwave irradiation method under mild condition. The starting materials for the synthesis of Mn2+ and Ce3+ Doped ZnS P nanocrystals were zinc acetate as zinc source, thioacetamide as a sulfur source, manganese chloride and Cerium chloride as manganese and cerium sources respectively (R & M Chemical) and ethylene glycol as a solvent. All chemicals were analytical grade products and used without further purification. The nanocrystals of Mn2+ and Ce3+ Doped ZnS P with cubic structure were characterized by X-ray powder diffraction (XRD), the morphology of the film is seen by field effect scanning electron microscopy (FESEM). The composition of the samples is analyzed by EDS. The s

Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed

Melanoma, a highly malignant form of skin cancer, affects individuals of all genders and is associated with high mortality rates, especially in advanced stages. The use of tele-dermatology has emerged as a proficient diagnostic approach for skin lesions and is particularly beneficial in rural areas with limited access to dermatologists. However, accurately, and efficiently segmenting melanoma remains a challenging task due to the significant diversity observed in the morphology, pigmentation, and dimensions of cutaneous nevi. To address this challenge, we propose a novel approach called DenseUNet-169 with a dilated convolution encoder-decoder for automatic segmentation of RGB dermascopic images. By incorporating dilated convolution,

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Internal control system is a safety valve that preserves economic units assets and ensure the accuracy of financial data, as well as to obligation in the laws, regulations, administrative policies ,and improve the efficiency, effectiveness and economic of operation, so it has become imperative for these units attention to internal and developed control system The research problem in exposure the economic units when the exercise of their business to many of the risks to growth or hinder the achievement of its objectives and the risks (financial, operational, strategy, risk) and not it rely on risk Assessment according to modern scientific methods, as in Brown's risk Classification, Which led to the weakness of the internal control identif

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de