Due to the easily access to the satellite images, Google Earth (GE) images have become more popular than other online virtual globes. However, the popularity of GE is not an indication of its accuracy. A considerable amount of literature has been published on evaluating the positional accuracy of GE data; however there are few studies which have investigated the subject of improving the GE accuracy. In this paper, a practical method for enhancing the horizontal positional accuracy of GE is suggested by establishing ten reference points, in University of Baghdad main campus, using different Global Navigation Satellite System (GNSS) observation techniques: Rapid Static, Post-Processing Kinematic, and Network. Then, the GE image for the study

Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution. This review is concerned with Different

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

Background: Although various imaging modalities are available for evaluating suspicious breast lesions, ultrasound-based Shear-Wave Elastography (SWE) is an advanced, non-invasive technique complementary to grayscale sonography. This technique evaluates the elasticity of a specific tissue by applying sonic pressure to that tissue.

Objective: The aim is to assess the role of SWE in evaluating solid breast masses in correlation to histopathological study results.

Subjects and Methods: This prospective study was done in a tertiary care teaching hospital from September 2019 to August 2020. A study population of 50 women aged 18 years or above with an

Background: This study was designed to measure the displacement pattern of posterior palatal seal (pps) area in different forms of the palate and with different impression techniques. Materials and method: This study was used to measure the displacement pattern of (pps)in different palatal shapes by using different impression materials Korrecta wax No.4,Green compound and design of House for pps for each palatal forms by using a 3D Scanner of CAD/CAM and measuring the distance between 2 points in pps area by using Caural Threw. Result: The results show highly significant differences between these techniques and the control group (impression with light body) Conclusion: The physiological impression technique of pps with Korecta wax no.4

    The current study performed in order to detect and quantify epicatechin in two tea samples of Camellia sinensis (black and green tea) by thin layer chromatography (TLC) and high performance liquid chromatography (HPLC). Extraction of epicatechin from black and green tea was done by using two different methods: maceration (cold extraction method) and decoction (hot extraction method) involved using three different solvents which are  absolute ethanol, 50% aqueous ethanol and water  for both extraction methods using room temperature and direct heat respectively. Crude extracts of two tea samples that obtained from two methods were fractionated by using two solvents with different polarity (chloroform and

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Development of a precise and delicate reaction has been acquired for the determination of vancomycin hydrochloride using batch and cloud point extraction (CPE) methods. The first method is based on the formation of azo dye as a result of diazotized dapsone coupled with vancomycin HCl (VAN) in a basic medium. The sensitivity of this reaction was enhanced by utilizing a nonionic surfactant (Triton X-114) and the cloud point extraction technique (second method). The azo dye formed was extracted into the surfactant-rich phase, dissolved in ethanol and detected at λ_max 440 nm spectrophotometrically. The reaction was investigated using both batch and CPE methods (with and without extraction), and a simple comparison between the two