Excessive skewness which occurs sometimes in the data is represented as an obstacle against normal distribution. So, recent studies have witnessed activity in studying the skew-normal distribution (SND) that matches the skewness data which is regarded as a special case of the normal distribution with additional skewness parameter (α), which gives more flexibility to the normal distribution. When estimating the parameters of (SND), we face the problem of the non-linear equation and by using the method of Maximum Likelihood estimation (ML) their solutions will be inaccurate and unreliable. To solve this problem, two methods can be used that are: the genetic algorithm (GA) and the iterative reweighting algorithm (IR) based on the Maximum Likelihood method. Monte Carlo simulation was used with different skewness levels and sample sizes, and the superiority of the results was compared. It was concluded that (SND) model estimation using (GA) is the best when the samples sizes are small and medium, while large samples indicate that the (IR) algorithm is the best. The study was also done using real data to find the parameter estimation and a comparison between the superiority of the results based on (AIC, BIC, Mse and Def) criteria.
To decrease the impact on the environment of building waste, the recycled aggregate may be used in various sustainable engineering applications, such as roller compacted concrete pavement (RCCP). This research examined how using recycled aggregate as a partial replacement for natural aggregate as coarse or fine affected the mechanical properties of roller-compacted concrete pavement. The recycled aggregate was crushed and sieved to coarse and fine aggregate before being used in the roller-compacted concrete pavement. Compressive strength, splitting tensile strength, and flexural strength were all evaluated after the samples were prepared at 28 and 90 days of curing. According to the study's findings, the partial replacem
... Show MoreBackground: Stroke is an acute neurologic injury and represents the 2nd leading cause of mortality worldwide, and also the most leading cause of acquired disability and morbidity in adults.
Objective: Effect and association between stroke and risk factors.
Type of the study: A retrospective study.
Methods: The study conducted on 312 patients in 2016, all data were collected from patients’ files from the emergency unit, which included basic demographic and disease characteristic, co morbid diseases, risk factors, final diagnosis.
Results: both previous stroke, ischemic heart disease was strong predictor of new
... Show MoreThe current research studies the innovative thinking system in the field of the interior design, and the extent of the possibility of activating its work mechanisms as a strategy for the redesigning principle according the variables of the contemporary social thinking. The research aims at revealing the nature of the thinking criteria and requirements that provide strategic values that guide the interior designer and the architect to organize the mechanism the act of designing. It also contributes in dealing with the design product through activating its ability in innovation and redesigning.
The research consists of the concept of innovation, the&nbs
... Show MoreThis study looks into the many methods that are used in the risk assessment procedure that is used in the construction industry nowadays. As a result of the slow adoption of novel assessment methods, professionals frequently resort to strategies that have previously been validated as being successful. When it comes to risk assessment, having a precise analytical tool that uses the cost of risk as a measurement and draws on the knowledge of professionals could potentially assist bridge the gap between theory and practice. This step will examine relevant literature, sort articles according to their published year, and identify domains and qualities. Consequently, the most significant findings have been presented in a manne
... Show MoreIn an intensive study of the various species of the Euglenophyceae under different environmental conditions, the algal samples were collected monthly in twelve springs and six related streams from September 2019 to August 2020 within Shaglawa district-Erbil Province in virgin areas for phycolimnological study. Twenty species of Euglenophyceaen are identified as a new record for the algal flora. These taxa consist of Colacium vesiculosum, Lepocinclis salina and L.wangi, Eutreptia viridis, Euglena chlamydophora, E. clavata,
... Show MoreIn light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen
... Show MoreThis study examines traveling wave solutions of the SIS epidemic model with nonlocal dispersion and delay. The research shows that a key factor in determining whether traveling waves exist is the basic reproduction number R0. In particular, the system permits nontrivial traveling wave solutions for σ≥σ∗ for R0>1, whereas there are no such solutions for σ<σ∗. This is because there is a minimal wave speed σ∗>0. On the other hand, there are no traveling wave solutions when R0≤1. In conclusion, we provide several numerical simulations that illustrate the existence of TWS.
Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature
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