The incorporation of recycled concrete aggregate (RCA) into asphalt concrete supports circular economy goals by reducing reliance on virgin materials and minimizing construction waste. However, RCA’s inherent limitations, such as high porosity, microcracking, and poor interfacial bonding, compromise the structural integrity and durability of asphalt mixtures. This study introduces sugarcane molasses (SCM), a naturally derived, carbohydrate-rich byproduct of sugarcane refining, as a novel and eco-friendly surface treatment for RCA aimed at enhancing its compatibility with asphalt binders. SCM was applied at 5-6% by weight of RCA replacing coarse aggregate at varying levels (0-100%) to assess its effect on asphalt mixture performance. A com

One hundred forty three of Klebsiellapneumoniae isolates had been collected from some hospitals in Baghdad city. The isolates were taken from different clinical specimens.Antimicrobial susceptibility test was carried out towards fifteen antimicrobial agents by using Vitek2 system with Antimicrobial susceptibility test cards. The results of antibiogram showed that the local isolates were possess highly resistance towards most antimicrobial agents under study. The high resistance wastoAmpicillin while the low resistance was to Imipenem.Two methods were used for detection of Extended Spectrum Beta Lactamases (ESBLs) production; first methods by using of Vitek2 system,thesecondmethods by using of polymerase chain reaction (PCR) technique to dis

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

ABSTRACT Background: Blood donation is process obtaining, analyzing, preparing, and preserving blood and its many components essential for life-saving that enhances health and lowers mortality rates. Despite the fact that giving blood is frequently demonstrated to be a voluntary service, certain studies highlight a number of factors that affect blood donations such as Gender, past donor experiences including syncopal reactions, venipuncture pain, anxiety, and donor satisfaction, all have an impact on the decision to donate blood.in addition, one of the main reasons donors reject to donate blood is pain associated with venipuncture. Aim: Evaluate the effectiveness of shotblocker on pain during venipuncture in adult blood donors compared to

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop