Choosing antimicrobials is a common dilemma when the expected rate of bacterial resistance is high. The observed resistance values in unequal groups of isolates tested for different antimicrobials can be misleading. This can affect the decision to recommend one antibiotic over the other. We analyzed recalled data with the statistical consideration of unequal sample groups. Data was collected concerning children suspected to have typhoid fever at Al Alwyia Pediatric Teaching Hospital in Baghdad, Iraq. The study period extended from September 2021 to September 2022. A novel algorithm was developed to compare the drug sensitivity among unequal numbers of Salmonella typhi (S. Typhi) isolates tested with different antibacterials.

a porentioncsisteve has been carried out of the corrosion behavior of inconel(600) in chloride ions (Cl) over the tempreatures 293 over the temperatures 308K in both the dcacrated and the alloy

In this work, corrosion parameters were evaluated using potentiodynamic polarization curves. In order to determine corrosion parameters of potential and current density of the interesting metal, carbon steel, environmental conditions of external corrosion of buried carbon steel pipeline in Iraqi soil were prepared in the laboratory using simulated prepared conditions. Solutions of sodium chloride at different concentrations (300, 1100, 1900, 2700, and 3500 ppm) were used. pH of solution were acidic at pH =5, and alkaline at pH = 9. Laboratory conditions were similar to those of Iraqi soil where the pipelines were buried. Temperature was constant at 20 °C. Potentiodynamic polarization curves, of potential vs. log current density, were ob

In this work, a deep computational study has been conducted to assign several qualities for the graph ⁠. Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes