Hard-grade asphalt binders, such as AC 20–30, offer excellent resistance to permanent deformation but are inherently brittle, making them highly susceptible to fatigue and low-temperature cracking. While polymer modification addresses these issues, virgin polymers remain expensive. Despite the growing interest in recycled plastics, the rheological impact of complex waste streams, specifically polyvinyl chloride (PVC) derived from flex banners containing plasticizers, on excessively stiff binders within the complete Superpave Performance Grading (PG) framework remains critically underexplored. This study introduces a novel valorization approach by utilizing solvent-extracted flex banner waste (WPVC) as a dual-action modifier. It leverages

Gypseous soil covers approximately 30% of Iraqi lands and is widely used in geotechnical and construction engineering as it is. The demand for residential complexes has increased, so one of the significant challenges in studying gypsum soil due to its unique behavior is understanding its interaction with foundations, such as strip and square footing. This is because there is a lack of experiments that provide total displacement diagrams or failure envelopes, which are well-considered for non-problematic soil. The aim is to address a comprehensive understanding of the micromechanical properties of dry, saturated, and treated gypseous sandy soils and to analyze the interaction of strip base with this type of soil using particle image

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

The Reasons behind the decadence of the studies concerning the evening school in Salah al Deen A field study

Background: Myocardial infarction (MI) is distinguished by the necrosis of myocardial cells as a result of substantial and prolonged ischemia. Anxiety, problems sleeping, and feelings of depression are some of the most common psychosocial consequences of having a myocardial infarction. Aim: The purpose of this study is to evaluate the effects of post-myocardial infarction on patients' levels of anxiety, depression, and quality of sleep. Method: The collection of data from 94 individuals with MI was carried out according to a descriptive cross-sectional design. Sleep quality, depression, and anxiety were evaluated using standard questionnaires. Results: 69.1% of the participants reported having trouble getting quality sleep. The perc