Background: The COVID-19 pandemic has an immense effect not only on the social and economic lives of people but also on the surgical lives of surgeons, residents, nursing staff, and patients as well as ground level staff. Amidst this COVID pandemic, emergency surgeries were being done but at a decreased rate, whereas elective cases depended on the will of hospitals, surgeons, and patients. Study aims to promulgate a "Neo–Surgical Check Box" by amalgamating the WHO surgical checklist and the results obtained from the questionnaires.

Subjects and Methods: After receiving ethical clearance from the Institute Ethical Committee, an online questionnaire with 50 questions divided into

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

The pandemic of coronavirus disease 2019 (COVID-19), first reported in China, in December 2019 and since then the digestive tract involvement of  COVID-19 has been progressively described. In this review, I summed recent studies, which have addressed the pathophysiology of COVID-19-induced gastrointestinal symptoms, their prevalence, and bowel pathological and radiological findings of infected patients. The effects of gut microbiota on SARS-CoV-2 and the challenges of nutritional therapy of the infected patients are depicted.  Moreover, I provide a concise summary of the recommendations on the management of inflammatory bowel disease, colorectal cancer, and performing endoscopy in the COVID era. Finally, the COVID pancreatic re

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contras

Worldwide, there is an increased reliance on COVID-19-related health messages to curb the COVID-19 outbreak. Therefore, it is vital to provide a well-prepared and authentic translation of English-language messages to reach culturally and linguistically diverse audiences. However, few studies, if any, focus on how non-English-speaking readers receive and linguistically accept the lexical choices in the messages translated into their language. The present study tested a sample of translated Arabic COVID-19-related texts that were obtained from the World Health Organization and Australian New South Wales Health websites. This study investigated to that extent Arabic readers would receive translated COVID-19 health messages and whether the t

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated