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CROSS-SECTIONAL REGRESSION WITH PROXIES: A SEMI-PARAMETRIC METHOD
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This study investigates asset returns within the Iraq Stock Exchange by employing both the Fama-MacBeth regression model and the Fama-French three-factor model. The research involves the estimation of cross-sectional regressions wherein model parameters are subject to temporal variation, and the independent variables function as proxies. The dataset comprises information from the first quarter of 2010 to the first quarter of 2024, encompassing 22 publicly listed companies across six industrial sectors. The study explores methodological advancements through the application of the Single Index Model (SIM) and Kernel Weighted Regression (KWR) in both time series and cross-sectional analyses. The SIM outperformed the KWR approach in estimating time-varying beta coefficients, yielding a mean Root Mean Squared Error (RMSE) of 0.14316. Furthermore, the integrated KWR-SIM methodology achieved the lowest Adjusted Root Mean Squared Error (ARMSE) value of 0.08152 when modelling the association between risk factors and asset returns within the cross-sectional analytical framework. Statistical tests for significance produced heterogeneous responses of the returns on assets in the Iraqi financial market to the Fama-French posited economic variables. The estimated coefficients for the betas showed significant oscillations for all assets, confirming changes in economic conditions. The results add to our knowledge of the risk-reward relationship in the context of emerging markets and provide methodological insights into financial asset pricing. The evidence indicates that the KWR-SIM method has better capabilities for model fitting

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Publication Date
Fri Mar 20 2026
Journal Name
Pharmaceutics
Analytical Physicochemical and Functional Studies to Compare AryoTrust, a Follow-On Biologics, with the Originator Trastuzumab (Herceptin)
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Background: Trastuzumab is a blockbuster monoclonal antibody that has revolutionized the treatment of HER2-positive breast and gastric cancers. With the increasing availability of biosimilar monoclonal antibodies in clinical practice, independent verification of biosimilarity using products sampled from a real-world supply chain is important to assure clinicians and the patients to use these products confidently. Objective: The aim of this study is to assess the biosimilarity of AryoTrust, a trastuzumab biosimilar, in comparison with the reference product Herceptin. AryoTrust and Herceptin products were randomly withdrawn from Iraqi hospitals to reflect medicines administered in real clinical settings. Methods: AryoTrust and Hercept

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Publication Date
Sat Jun 01 2019
Journal Name
Collegian
Medication adherence and predictive factors in patients with cardiovascular disease: A comparison study between Australia and Iraq
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Publication Date
Tue Jun 28 2022
Journal Name
Research Journal Of Pharmacy And Technology
A Case-control Study to Determine Metalloproteinase-12 and Lysyl Oxidase Levels in Iraqi women with Osteoporosis
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The current study aimed to ascertain the levels of matrix metalloproteinase-12 (MMP-12) and Lysyl oxidase (LOX) in osteoporosis patients and their correlation with alkaline phosphatase (ALP), magnesium (Mg), vitamin D (Vit D), calcium (Ca), phosphorus (P), and T-score %. 110 participants recruited from Baghdad Teaching Hospital, Iraq, were enrolled in this study from November 2019 to March 2020). The participants were divided into two groups: Group 1 comprised 60 osteoporotic women and group 2 consisted of 50 healthy women. (MMP and LOX) were estimated using a quantitative enzyme-linked immunosorbent assay (ELISA. The results showed significant differences in serum LOX, age, ALP, Mg, and T-score %, while no significant differences i

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Publication Date
Tue Dec 01 2020
Journal Name
Baghdad Science Journal
A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset
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Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

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Publication Date
Mon Jan 01 2024
Journal Name
Ieee Access
Patient Monitoring System Based on Internet of Things: A Review and Related Challenges With Open Research Issues
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Publication Date
Tue Oct 15 2024
Journal Name
Civileng
Structural Performance of a Hollow-Core Square Concrete Column Longitudinally Reinforced with GFRP Bars under Concentric Load
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Concrete columns with hollow-core sections find widespread application owing to their excellent structural efficiency and efficient material utilization. However, corrosion poses a challenge in concrete buildings with steel reinforcement. This paper explores the possibility of using glass fiber-reinforced polymer (GFRP) reinforcement as a non-corrosive and economically viable substitute for steel reinforcement in short square hollow concrete columns. Twelve hollow short columns were meticulously prepared in the laboratory experiments and subjected to pure axial compressive loads until failure. All columns featured a hollow square section with exterior dimensions of (180 × 180) mm and 900 mm height. The columns were categorized into

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Publication Date
Mon Nov 28 2016
Journal Name
Separation Science And Technology
Using sewage sludge as a permeable reactive barrier for remediation of groundwater contaminated with lead and phenol
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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review
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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

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Publication Date
Fri Feb 03 2023
Journal Name
Buildings
Flexural Performance of a Novel Steel Cold-Formed Beam–PSSDB Slab Composite System Filled with Concrete Material
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In this study, the flexural performance of a new composite beam–slab system filled with concrete material was investigated, where this system was mainly prepared from lightweight cold-formed steel sections of a beam and a deck slab for carrying heavy floor loads as another concept of a conventional composite system with a lower cost impact. For this purpose, seven samples of a profile steel sheet–dry board deck slab (PSSDB/PDS) carried by a steel cold-formed C-purlins beam (CB) were prepared and named “composite CBPDS specimen”, which were tested under a static bending load. Specifically, the effects of the profile steel sheet (PSS) direction (parallel or perpendicular to the span of the specimen) using different C-purlins c

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Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Hydraulic Flow Units for Jeribe Reservoir in Jambour Oil Field Applying Flow Zone Indicator Method
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The Jeribe reservoir in the Jambour Oil Field is a complex and heterogeneous carbonate reservoir characterized by a wide range of permeability variations. Due to limited availability of core plugs in most wells, it becomes crucial to establish correlations between cored wells and apply them to uncored wells for predicting permeability. In recent years, the Flow Zone Indicator (FZI) approach has gained significant applicability for predicting hydraulic flow units (HFUs) and identifying rock types within the reservoir units.    This paper aims to develop a permeability model based on the principles of the Flow Zone Indicator. Analysis of core permeability versus core porosity plot and Reservoir Quality Index (RQI) - Normalized poros

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