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The Characterisation of the Craniofacial Morphology of Infants Born With Zika Virus; Innovative Approach for Public Health Surveillance and Broad Clinical Applications
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Background: This study was carried out in response to the Zika virus epidemic, which constituted a public health emergency, and to the 2019 WHO calling for strengthened surveillance for the early detection of related microcephaly. The main aim of the study was to phenotype the craniofacial morphology of microcephaly using a novel approach and new measurements, and relate the characteristics to brain abnormalities in Zika-infected infants in Brazil to improve clinical surveillance.

Methods: 3D images of the face and the cranial vault of 44 Zika-infected infants and matched healthy controls were captured using a 3D stereophotogrammetry system. The CT scans of the brain of the infected infants were analysed. Principal component analysis (PCA) was applied to characterise the craniofacial morphology. In addition to the head circumference (HC), a new measurement, head height (HH), was introduced to measure the cranial vault. The level of brain abnormality present in the CT scans was assessed; the severity of parenchymal volume loss and ventriculomegaly was quantified. Student's t-test and Spearman's Rho statistical test have been applied.

Findings: The PCA identified a significant difference (p < 0.001) between the cranial vaults and the face of the Zika infants and that of the controls. Spearman's rank-order correlation coefficients show that the head height (HH) has a strong correlation (0.87 in Zika infants; 0.82 in controls) with the morphology of the cranial vaults, which are higher than the correlation with the routinely used head circumference (HC). Also, the head height (HH) has a moderate negative correlation (−0.48) with the brain abnormalities of parenchymal volume loss.

Interpretation: It is discovered that the head height (HH) is the most sensitive and discriminatory measure of the severity of cranial deformity, which should be used for clinical surveillance of the Zika syndrome, evaluation of other craniofacial syndromes and assessment of various treatment modalities.

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Publication Date
Sat Oct 28 2023
Journal Name
Journal Of Optics
Advancements and challenges in pulsed laser-deposited hydrophobic CeO2 film for broadband antireflection applications
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Due to the remarkable progress in photovoltaic technology, enhancing efficiency and minimized the costs have emerged as global challenges for the solar industry. A crucial aspect of this advancement involves the creation of solar cell antireflection coating, which play a significant role in minimizing sunlight reflection on the cell surface. In this study, we report on the optimization of the characteristics of CeO2 films prepared by pulsed laser deposition through the variation of laser energy density. The deposited CeO2 nanostructure films have been used as an effective antireflection coating (ARC) and light-trapping morphology to improve the efficiency of silicon crystalline solar cell. The film’s thickness increases as laser fluence i

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Publication Date
Tue Jun 09 2026
Journal Name
Journal Of Baghdad College Of Dentistry
Effect of the examination stress on periodontal health status and salivary IL-1? among Iraqi dental students
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Background: Periodontal diseases (PD) are common chronic inflammatory diseases caused by pathogenic microorganisms colonizing the gingival area and inducing local and systemic elevations of pro-inflammatory cytokines resulting in tissue destruction by a destructive inflammatory process. Stress was considered as one of the important risk factors that cause many inflammatory diseases including PD. The purpose of this study wasto determines and compares clinical periodontal parameters (PLI, GI and BOP), stress level and salivary IL-1? level among dental students before, during and after mid-year exam, also to find the correlation among stress, IL-1? and clinical periodontal parameters. Materials and methods: The sample was consisted of 24 dent

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Publication Date
Mon May 22 2023
Journal Name
Journal Of Applied Business And Technology
Toxic Workplace, Mental Health and Employee Well-being, the Moderator Role of Paternalistic Leadership, an Empirical Study
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Based on previous research results that recognized the role of paternalistic leadership in promoting a positive work climate, this study explored the impact of a toxic work environment on the mental health and well-being of employees. We used the quantitative methodology to collect and analyze data. A sample of 108 participants from Iraqi internet service provider (ISPs) companies represented the purposive study sample. We targeted employees who experienced the COVID-19 pandemic. All data was collected through an electronic questionnaire (Google and Microsoft Forms). The research model was tested using structural equation modeling (SEM). The results showed a negative effect of the toxic workplace on the mental health of employees. T

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Publication Date
Sun Jan 01 2017
Journal Name
Journal Of The College Of Languages (jcl)
« L'agressivité de la parole et de l'acte » dans Haute Surveillance et Les Bonnes de Jean Genet
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Sommaire

                      La question de la violence et de l'agressivité  porte une importance remarquable dans l’esprit de Jean Genet qui estime le crime   en le considérant comme beau. Il lui donne une valeur suprême. Il le glorifie avec ses personnages criminels.   Le langage théâtral genétien est violent qui conduit à la fin à l’acte agressif, le crime et la mort. Ce théâtre qui aborde la condition humaine retrace sur la scène le chemin tragique de l’homme moderne.               

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Publication Date
Thu Jun 01 2023
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
An optimized deep learning model for optical character recognition applications
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The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

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Publication Date
Fri Feb 18 2022
Journal Name
Coatings
Non-Polar Gallium Nitride for Photodetection Applications: A Systematic Review
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Ultraviolet photodetectors have been widely utilized in several applications, such as advanced communication, ozone sensing, air purification, flame detection, etc. Gallium nitride and its compound semiconductors have been promising candidates in photodetection applications. Unlike polar gallium nitride-based optoelectronics, non-polar gallium nitride-based optoelectronics have gained huge attention due to the piezoelectric and spontaneous polarization effect–induced quantum confined-stark effect being eliminated. In turn, non-polar gallium nitride-based photodetectors portray higher efficiency and faster response compared to the polar growth direction. To date, however, a systematic literature review of non-polar gallium nitride-

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Publication Date
Fri Apr 14 2023
Journal Name
Journal Of Big Data
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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Abstract<p>Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for</p> ... Show More
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Publication Date
Mon Mar 30 2026
Journal Name
Iraqi Journal Of Science
A modified time series model using conditional and unconditional estimations with applications to a real dataset
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Modern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan

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Publication Date
Sat Oct 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Assessing the quality of health services applied research 5qs in Baghdad teaching hospital
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Abstract

       This research aims to assess and measure the quality of health services provided by the Baghdad Educational Hospital using  five dimensions of 5Qs , Developer model by (Zinelidin), 2006), the problem of the research is to try to determine the quality of health services provided at the hospital as well as the investigation of the existence of the elements of quality contribute to achieve the requirements of customers and their needs, and used for the purposes of this research two approaches, first used Applied Research to 5Qs dimension model and use the Checklist as a Key tool to collection and analysis of data and the second descriptive analytical me

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Non-linear support vector machine classification models using kernel tricks with applications
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The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

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