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jeasiq-3045
The Cluster Analysis by Using Nonparametric Cubic B-Spline Modeling for Longitudinal Data
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Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.

In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.

The longitudinal balanced data profile was compiled into subgroups by penalizing the pairwise distances between the coefficients of the cubic B-spline model using one of the common penalize functions, the Minimax Concave Penalty function (MCP). This method, in turn, works to determine the number of clusters through one of the model selection criteria, Bayesian information criteria (BIC), and we used optimization methods to solve their equations. Therefore, we applied the alternative direction method of the ADMM multiplier algorithm to reach approximate solutions to find the estimators of the nonparametric model using R statistical software.
Longitudinally balanced data were generated in the simulation study, as the number of subjects was 60 and the number of repeats (time) was 10 for each subject. The simulation was iterated 100 times, and it showed that employing the MCP partial methods on the cubic model can group profiles into clusters, which is the aim of this paper.

 

Paper type: Research paper.

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Publication Date
Fri Aug 01 2025
Journal Name
Imam Jaafar Al-sadiq University Journal Of Human And Social Sciences
Examination of Identity and Alienation in Postcolonial Literature: Analysis of Nervous Conditions by Tsitsi Dangarembga
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This research explores the themes of identity and alienation in Tsitsi Dangarembga's famous novel, Nervous Conditions, through Kimberlé Crenshaw's intersectionality theory. The story takes place in postcolonial Zimbabwe and delivers a fascinating illustration of the intersecting domains of gender, race, class, and colonial legacies that shape the characters' experiences of identity and alienation. Benefitting from Crenshaw's intersectional paradigm, this article explores the multidimensional interface of societal categorizations and power relations in the novel, revealing the complex dynamics of individuals as they negotiate their identities in a postcolonial context. Through a thorough examination of Tambudzai's journey and the problems o

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Publication Date
Fri May 30 2025
Journal Name
Iraqi Journal Of Science
A Novel Approach for Synthesizing the Pan-chromatic Band to (10 m) of Landsat 9 Based on Sentinel-2 Data to Improve Classification Performance
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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

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Investigating the Influence of Precursor Concentration on the Photodegradation of Methylene Blue using Biosynthesized ZnO from Pometia pinnata Leaf Extracts
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The ZnO nanoparticles were synthesized at various precursor concentrations i.e. 0.05, 0.1, and 0.5 M by biosynthesis method based on Pometia pinnata Leaf Extracts. Initial nanoparticle concentration influenced the optical bandgap, shape, and structure of nanoparticles. The photodegradation process was carried out under UV illumination. The efficiency of MB degradation was determined by measuring the decrease in MB concentration and by analyzing the optical absorption at 663 nm recorded by UV-Vis spectroscopy. Results showed that the biosynthesized ZnO nanoparticles exhibited efficient photodegradation of MB, with a maximum degradation rate of 80% after 90 minutes of exposure to UV-C light. The study highlights the potential of Pometia pi

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Publication Date
Sun Sep 01 2024
Journal Name
Egyptian Journal Of Botany
Molecular data unravels three freshwater peridinioids in the Baghdad Island Tourist Lake, with reporting Palatinus apiculatus (Peridiniopsidaceae, Peridiniales) as a new record for the Iraqi algal flora
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Publication Date
Wed Mar 18 2020
Journal Name
Baghdad Science Journal
A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis
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          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

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Publication Date
Tue Sep 08 2020
Journal Name
Baghdad Science Journal
Turbid of Water By Using Fuzzy C- Means and Hard K- Means
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In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

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Publication Date
Fri Nov 29 2024
Journal Name
The Iraqi Geological Journal
Data Driven Approach for Predicting Pore Pressure of Oil and Gas Wells, Case Study of Iraq Southern Oilfields
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Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

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Publication Date
Tue Jun 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of weighted estimated method and proposed method (BEMW) for estimation of semi-parametric model under incomplete data
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Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

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Publication Date
Fri Mar 31 2017
Journal Name
Iraqi Journal Of Biotechnology
Reliable Reference Gene for Normalization of RT- qPCR Data in Human Cancer Cell Lines
Subjected to Gene Knockdown
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Quantitative real-time Polymerase Chain Reaction (RT-qPCR) has become a valuable molecular technique in biomedical research. The selection of suitable endogenous reference genes is necessary for normalization of target gene expression in RT-qPCR experiments. The aim of this study was to determine the suitability of each 18S rRNA and ACTB as internal control genes for normalization of RT-qPCR data in some human cell lines transfected with small interfering RNA (siRNA). Four cancer cell lines including MCF-7, T47D, MDA-MB-231 and Hela cells along with HEK293 representing an embryonic cell line were depleted of E2F6 using siRNA specific for E2F6 compared to negative control cells, which were transfected with siRNA not specific for any gene. Us

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Publication Date
Sun Mar 06 2016
Journal Name
Baghdad Science Journal
Investigation of the dielectric Properties of (PPAB) terminated by phenylenediamine doped by Na2[Fe(CN)5.NO].2H2O using Lumped equivalent circuit
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The aim of this paper is to demonstrate the effect of Na2[Fe(CN)5.NO].2H2O impurity (0.1 M) concentration on the dielectrical properties of poly (P-Aminobenzaldehyde) terminated by pheneylenediamine in the frequency and temperature ranges (1-100)KHz and (283-348) K respectively.These properties include dissipation factor, series and parallel resistance, series and parallel capacitance, real and imaginary part of the dielectric constant, a.c conductivity and impedance (real and imaginary) part, that have been deduced from equivalent circuit. The investigation shows that adding Na2[Fe(CN)5.NO].2H2O as additive to the polymer lead to increase of the dielectric constant with increasing temperature and it is decreasing with increasing the freq

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