In this paper, some estimators of the unknown shape parameter and reliability function  of Basic Gompertz distribution (BGD) have been obtained, such as MLE, UMVUE, and MINMSE, in addition to estimating Bayesian estimators under Scale invariant squared error loss function assuming informative prior represented by Gamma distribution and non-informative prior by using Jefferys prior. Using Monte Carlo simulation method, these estimators of the shape parameter and R(t), have been compared based on mean squared errors and integrated mean squared, respectively

Internet of Things (IoT) technology could be an effective solution to accomplish real-time retrieval of historical electronic health records (EHRs) to present better service of healthcare. In a pilgrimage environment such as the Hajj, IoT can be applied by identifying the non-local patients as electronic tags, and the tag data can be read by wireless sensors. The data that is collected using Radio-Frequency Identification (RFID) can be acquired from a Wireless Sensor Network (WSN) in order to accomplish many decisions, such as sending an ambulance to a patient’s location, sending an emergency alert to his immediate family circle, and retrieving his EHR from a database. The main contribution of this research is to propose a conceptual IoT

      Long memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify and estimate the long memory parameter in partially integrated time series. One of the most common models used to represent time series that have a long memory is the ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are a fractional number called the fractional parameter. To analyze and determine the ARFIMA model, the fractal parameter must be estimated. There are many methods for fractional parameter estimation. In this research, the estimation methods were divided into indirect methods, where the Hurst parameter is estimated fir

    In this paper further properties of the fuzzy complete a-fuzzy normed algebra have been introduced. Then we found the relation between the maximal ideals of fuzzy complete a-fuzzy normed algebra and the associated multiplicative linear function space. In this direction we proved that if  is character on Z then ker is a maximal ideal in Z. After this we introduce the notion structure of the a-fuzzy normed algebra then we prove that the structure, st(Z) is -fuzzy closed subset of fb(Z, ) when  (Z,  , , ) is a commutative fuzzy complete a-fuzzy normed algebra with identity e.

Back ground: Colorectal cancer is one of the leading causes of death allover the world especially in the developed countries. Each year approximately one million veterans aged 50 and older will develop colorectal cancer over the remainder of their lives and nearly 433,000 will die from it. Because most cancers are diagnosed after local or regional spread, nearly half of all patients diagnosed with colorectal cancer will die lWarly detection and management definitely decrease mortality.
Patient and methods: This is a prospective study on fifty patients with colorectal cancer admitted at the first surgical unit in Baghdad teaching hospital since March 2000 till March 2003 The data collected from those fifty patients were analyzed accor

Background: Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality among women in Iraq forming 23% of cancer related deaths. The low survival from the disease is a direct consequence to the advanced stages at diagnoses. Aim: To document the composite stage of breast cancer among Iraqi patients at the time of diagnosis; correlating the observed findings with other clinical and pathological parameters at presentation. Patients and Methods: A retrospective study enrolling the clinical and pathological characteristics of 603 Iraqi female patients diagnosed with breast cancer. The composite stage of breast cancer was determined according to UICC TNM Classification System of Breast Cancer and the Ameri

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function i

Asthma is a chronic respiratory disease highly prevalent worldwide. Recent studies have suggested a role for microbiome-associated gut–lung axis in asthma development. In the current study, we investigated if Resveratrol (RES), a plant-based polyphenol, can attenuate ovalbumin (OVA)-induced murine allergic asthma, and if so, the role of microbiome in the gut–lung axis in this process. We found that RES attenuated allergic asthma with significant improvements in pulmonary functions in OVA-exposed mice when tested using plethysmography for frequency (F), mean volume (MV), specific airway resistance (sRaw), and delay time(dT). RES treatment also suppressed inflammatory cytokines in the lungs. RES modulated lung microbiota and cause

Transforming the common normal distribution through the generated Kummer Beta model to the Kummer Beta Generalized Normal Distribution (KBGND) had been achieved. Then, estimating the distribution parameters and hazard function using the MLE method, and improving these estimations by employing the genetic algorithm. Simulation is used by assuming a number of models and different sample sizes. The main finding was that the common maximum likelihood (MLE) method is the best in estimating the parameters of the Kummer Beta Generalized Normal Distribution (KBGND) compared to the common maximum likelihood according to Mean Squares Error (MSE) and Mean squares Error Integral (IMSE) criteria in estimating the hazard function. While the pr

              The current paper proposes a new estimator for the linear regression model parameters under Big Data circumstances.  From the diversity of Big Data variables comes many challenges that  can be interesting to the  researchers who try their best to find new and novel methods to estimate the parameters of linear regression model. Data has been collected by Central Statistical Organization IRAQ, and the child labor in Iraq has been chosen as data. Child labor is the most vital phenomena that both society and education are suffering from and it affects the future of our next generation. Two methods have been selected to estimate the parameter