Fluorescent Carbon Quantum Dots (CQDS) are a new kind of carbon nanoparticles that have appeared recently and have collected much interest as potential competitors to conventional semiconductor quantum dots (QDs). In addition to their comparable fluorescent properties, CQDs have the desired specifications of environmental friendliness, low toxicity, simple synthetic routes, low cost and surface passivation The functionalization of CQDS allow the control of their physicochemical properties. The main aim of this kind of researches is to account the variables that cannot be measured directly from practical experiments. Therefore, the work here is focused on the account energy gap of bulk (Eg bulk) by theoretically method (simulation) after

Ytterbium-doped (Y2O3), (Sc2O3) and (YAG) crystals are very important for high-power thindisk lasers. These lasers have shown their ability to operate quasi-three-level materials with high
efficiency as well as high thermal conductivity ratio for crystalline hosts. All these reasons have
required studying this type of laser. In the present work, the analytical solution was found for the
equation of laser output power, pumping threshold power, and efficiency of a quasi-three-level
thin disk laser. The numerical solution of these equations was also found through the Matlab
program at the fundamental transverse mode, at a temperature of 299K0
and with high pumping
capabilities in order to know the e

This paper addresses the substrate temperature effect on the structure, morphological and optical properties of copper oxide (CuO) thin films deposited by pulsed laser deposition (PLD) method on sapphire substrate of 150nm thickness. The films deposited at two different substrate temperatures (473 and 673)K. The atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR) and UV-VIS transmission spectroscopy were employed to characterize the size, morphology, crystalline structure and optical properties of the prepared thin films. The surface characteristics were studied by using AFM. It is found that as the substrate temperature increases, the grain size increased but the surface roughness decreased.  The FTIR spec

In this article, the influence of group nano transition metal oxides such as {(MnO₂), (Fe₂O₃) and (CuO)} thin films on the (ZnO-TiO2) electric characteristics have been analyzed. The prepared films deposited on glass substrate laser Nd-YAG with wavelength (ℷ =1064 nm) ,energy of (800mJ) and number of shots (400). The density of the film was found to be (200 nm) at room temperature (RT) and annealing temperature (573K).Using DC Conductivity and Hall Effect, we obtained the electrical properties of the films. The DC Conductivity shows that that the activation energies decrease while the σRT at annealing temperature with different elements increases the formation of mixed oxides. The Hall effect, the elec

The production and analysis of an optimal interference pattern for the optical fiber interferometer of a 193.1THz continuous laser source was simulated by comparing the spectral spectroscopy of the two arms of interferometer to be used as a heterodyne detection in sensing the body range, speed, and direction of movement by delaying the time between the arms.

     The study showed that the fringe pattern can be sensed a range by the free spectral range FSR and the velocity by the fringe separation FS and the direction by the fringe spatial frequency FSF.

     A global pandemic has emerged as a result of the widespread coronavirus disease (COVID-19). Deep learning (DL) techniques are used to diagnose COVID-19 based on many chest X-ray. Due to the scarcity of available X-ray images, the performance of DL for COVID-19 detection is lagging, underdeveloped, and suffering from overfitting. Overfitting happens when a network trains a function with an  incredibly high variance to represent the training data perfectly. Consequently, medical images lack the availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts. As the COVID-19 virus is an infectious disease, these datasets are scarce, and it is difficult to get large datasets

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

DeepFake is a concern for celebrities and everyone because it is simple to create. DeepFake images, especially high-quality ones, are difficult to detect using people, local descriptors, and current approaches. On the other hand, video manipulation detection is more accessible than an image, which many state-of-the-art systems offer. Moreover, the detection of video manipulation depends entirely on its detection through images. Many worked on DeepFake detection in images, but they had complex mathematical calculations in preprocessing steps, and many limitations, including that the face must be in front, the eyes have to be open, and the mouth should be open with the appearance of teeth, etc. Also, the accuracy of their counterfeit detectio

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN),

     Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes.  The data augmentation techniques have been used. In addition to dropout and weight reg