Extracorporeal shock wave lithotripsy (ESWL) is considered a standard treatment for nephrolith or kidney stones measuring less than 20 mm. Anatomical, machine-related, and stone factors play pivotal roles in treatment outcomes, the latter being the leading role. This paper examined the relationship between stone density on native CT scans and ESWL treatment to remove renal stones concerning several treatments. One hundred and twenty patients (64 males and 56 females) were enrolled and completed the study from April 2019 to September 2020. Inclusion criteria were a single renal pelvis stone of 5–20 mm to be treated for the first time in adult patients with no urinary or musculoskeletal anatomical abnormalities. We assessed patients

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Background: This study was conducted to evaluate the hard palate bone density and thickness during 3rd and 4th decades and their relationships with body mass index (BMI) and compositions, to allow more accurate mini-implant placement. Materials and method: Computed tomographic (CT) images were obtained for 60 patients (30 males and 30 females) with age range 20-39 years. The hard palate bone density and thickness were measured at 20 sites at the intersection of five anterioposterior and four mediolateral reference lines with 6 and 3 mm intervals from incisive foramen and mid-palatal suture respectively. Diagnostic scale operates according to the bioelectric impedance analysis principle was used to measure body weight; percentages of body fa

Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The

The Coronavirus Disease 2019 (COVID-19) pandemic has caused an unprecedented disruption in medical education and healthcare systems worldwide. The disease can cause life-threatening conditions and it presents challenges for medical education, as instructors must deliver lectures safely, while ensuring the integrity and continuity of the medical education process. It is therefore important to assess the usability of online learning methods, and to determine their feasibility and adequacy for medical students. We aimed to provide an overview of the situation experienced by medical students during the COVID-19 pandemic, and to determine the knowledge, attitudes, and practices of medical students regarding electronic medical education.

Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat

The consequences of ionizing radiation-induced oxidative stress on radiographers in X-ray and CT-scan departments utilizing several biochemical were analyzed. The study found highly considerable discrepancies in the interplay between radiation levels and gender in terms of mean Malondialdehyde (MAD), Vitamin D3 (Vit.D3), Triiodothyronine (T3), Thyroxine (T4), and High-Density Lipoprotein (HDL), but not Thyroid Stimulating Hormone (TSH), cholesterol, triglyceride (TG) and Low-Density Lipoprotein (LDL). The findings indicated that malondialdehyde is a useful biomarker for assessing oxidative stress in radiographers with exposure to ionizing radiation.

In recent decades, the identification of faces with and without masks from visual data, such as video and still images, has become a captivating research subject. This is primarily due to the global spread of the Corona pandemic, which has altered the appearance of the world and necessitated the use of masks as a vital measure for epidemic prevention. Intellectual development based on artificial intelligence and computers plays a decisive role in the issue of epidemic safety, as the topic of facial recognition and identifying individuals who wear masks or not was most prominent in the introduction and in-depth education. This research proposes the creation of an advanced system capable of accurately identifying faces, both with and