This paper predicts the resilient modulus (Mr) for warm mix asphalt (WMA) mixtures prepared using aspha-min. Various predictor variables were analyzed, including asphalt cement types, asphalt contents, nominal maximum aggregate sizes (NMAS), filler content, test temperatures, and loading times. Univariate and multivariate analyses were conducted to examine the behavior of each predictor variable individually and collectively. Through univariate analysis, it was observed that Mr exhibited an inverse trend with asphalt cement grade, NMAS, test temperature, and load duration. Although Mr increased slightly with higher filler and asphalt content, the magnitude of this increase was minimal. Multivariate analysis revealed that the rate of change

Two galaxies have been chosen, spiral galaxy NGC 5005 and elliptical galaxy NGC 4278 to study their photometric properties by using surface photometric techniques with griz-Filters. Observations are obtained from the Sloan Digital Sky Survey (SDSS). The data reduction of all images have done, like bias and flat field, by SDSS pipeline. The overall structure of the two galaxies (a bulge, a disk), together with isophotal contour maps, surface brightness profiles and a bulge/disk decomposition of the galaxy images were performed, although the disk position angle, ellipticity and inclination of the galaxies have been estimated.

          Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina

Data generated from modern applications and the internet in healthcare is extensive and rapidly expanding. Therefore, one of the significant success factors for any application is understanding and extracting meaningful information using digital analytics tools. These tools will positively impact the application's performance and handle the challenges that can be faced to create highly consistent, logical, and information-rich summaries. This paper contains three main objectives: First, it provides several analytics methodologies that help to analyze datasets and extract useful information from them as preprocessing steps in any classification model to determine the dataset characteristics. Also, this paper provides a comparative st

Periodontitis is a chronic inflammatory disease resulted from aggravated immune response to a dysbiotic subgingival microbiota of a susceptible host. Consequences of periodontitis are not only limited to the devastating effect on the oral cavity but extends to affect general health of the individual and also exerts economic burdens on the health systems worldwide. Despite these serious outcomes of periodontitis; however, they are avoidable by early diagnosis with proper preventive measures or non-invasive interventions at earlier stages of the disease. Clinically, diagnosis of periodontitis could be overlooked due to certain limitations of the conventional diagnostic methods such as periodontal charting and radiographs. Utilization of re

New hydrazone derivatives of Fenoprofen were synthesized and evaluated for their anti-inflammatory activity by means of egg white induced paw edema method. All the synthesized target compounds were characterized by FT-IR spectroscopy, 1HNMR analysis and by measure of their physical properties. The synthesis of the target compounds(H1-H4) was accomplished by multistep reaction procedures. The synthesized target compounds were show activity in reducing paw edema thickness and their anti-inflammatory effect was comparable to that of the standard (Fenoprofen) except for compound H3 which show anti-inflammatory activity higher than Fenoprofen.

The Capparis spinosa L. is a species has a great interest in the field of traditional medicine for its pharmacological properties with many bioactive compounds. Our study is aiming at the recovery of this species through a phytochemical analysis and an evaluation of antibacterial and antioxidant activities of leaves of Capparis spinosa L. collected from natural habitats within the region of Al-Jadriya, Baghdad, Iraq. Phytochemical investigation demonstrated the presence of flavonoids, phenols, alkaloids, tannins, and glycosides in the methanolic extract of leaves. The quantitative analysis of total phenolic contents is being performed by Folin-Ciocalteau method and expressed in terms of gallic acid equivalents. C. spinosa exhibited progress

The calibration of a low-speed wind tunnel (LSWT) test section had been made in the present work. The tunnel was designed and constructed at the Aerodynamics Lab. in the Mechanical Engineering Department/University of Baghdad. The test section design speed is 70 m/s. Frictional loses and uniformity of the flow inside the test section had been tested and calibrated based on the British standards for flow inside ducts and conduits. Pitot-static tube, boundary layer Pitot tube were the main instruments which were used in the present work to measure the flow characteristics with emphasize on the velocity uniformity and boundary layer growth along the walls of the test section. It is found that the maximum calibrated velocity for empty test s