The gravity method is a measurement of relatively noticeable variations in the Earth’s gravitational field caused by lateral variations in rock's density. In the current research, a new technique is applied on the previous Bouguer map of gravity surveys (conducted from 1940–1950) of the last century, by selecting certain areas in the South-Western desert of Iraqi-territory within the provinces' administrative boundary of Najaf and Anbar. Depending on the theory of gravity inversion where gravity values could be reflected to density-contrast variations with the depths; so, gravity data inversion can be utilized to calculate the models of density and velocity from four selected depth-slices 9.63 Km, 1.1 Km, 0.682 Km and 0.407 Km.

The study objective was to conduct Pharmacoeconomics study (cost-effective analysis) between infliximab reference (Remicade) and its biosimilar (Remsima) in patients with rheumatoid arthritis (RA) in Iraqi hospitals.

This is a retrospective multicenter pharmacoeconomic analysis conducted at two large teaching governmental hospitals in Baghdad, Iraq which provided infliximab to patients with RA. Data were collected from patient’s medical records and face-to-face interviews with the patients from December 2021 to April 2022.

The study included 57 patients with rheumatoid arthritis (RA).  The patients were categorized into two groups according to the type of infliximab they received over 30 weeks: 27 patients received

Background: The aim of this study is to evaluate the color change ∆E of the dental enamel following treatment with 2 kinds of protector (icon infiltrant, clinpro varnish) before fixed orthodontic treatment to avoid the possible white spot lesions. Materials and Methods: Fifty four subjects treated with fixed appliances were divided into 3 groups: the 1st group was control, while the 2nd and 3rd groups were treated with icon infiltrant and clinpro varnish before bonding procedure, respectively. Color parameters (L,a,b) were recorded for the middle and gingival thirds before and after bonding procedure to get the ∆E of each group. Results: One-way ANOVA test showed a non-significant difference in ∆E between the 3 groups a

This study investigates the impact of spatial resolution enhancement on supervised classification accuracy using Landsat 9 satellite imagery, achieved through pan-sharpening techniques leveraging Sentinel-2 data. Various methods were employed to synthesize a panchromatic (PAN) band from Sentinel-2 data, including dimension reduction algorithms and weighted averages based on correlation coefficients and standard deviation. Three pan-sharpening algorithms (Gram-Schmidt, Principal Components Analysis, Nearest Neighbour Diffusion) were employed, and their efficacy was assessed using seven fidelity criteria. Classification tasks were performed utilizing Support Vector Machine and Maximum Likelihood algorithms. Results reveal that specifi

This study employs wavelet transforms to address the issue of boundary effects. Additionally, it utilizes probit transform techniques, which are based on probit functions, to estimate the copula density function. This estimation is dependent on the empirical distribution function of the variables. The density is estimated within a transformed domain. Recent research indicates that the early implementations of this strategy may have been more efficient. Nevertheless, in this work, we implemented two novel methodologies utilizing probit transform and wavelet transform. We then proceeded to evaluate and contrast these methodologies using three specific criteria: root mean square error (RMSE), Akaike information criterion (AIC), and log

Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin

This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them