The effect of (Di-Ammonium Phosphate DAP) (P21%), as plant fertilizer on content of the Saliva officinalis. Total flavonoid as an important active constituent was investigated in the present study. Three samples (F1, F2, and F3) of Saliva officinalis were grown in soil treated with DAP (100, 200, and 300) kg/hector respectively. Another two samples were studied: F0 as a plant sample grown in soil untreated with fertilizer and F4 as wild type obtained from market. Total flavonoids were estimated Quantitatively by Rutin standard curve and qualitatively by TLC method in corresponding to standard flavonoids. Results showed that different concentrations of DAP treated soil affected flavonoid contained of the Iraqi cultivated Saliva officinali

     Predicting the maximum temperature is of great importance because it is related to various aspects of life, starting from people’s lives and their comfort, passing through the medical, industrial, agricultural and commercial fields, as well as concerning global warming and what can result from it. Thus, the historical observations of maximum and minimum air temperature, wind speed and relative humidity were analyzed in this work. In Baghdad, the climatic variables were recorded on clear sky days dawn at 0300 GMT for the period between (2005-2020). Using weather station's variables multiple linear regression equation, their correlation coefficients were calculated to predict the daily maximum air temperature for any day during

The multiple linear regression model is an important regression model that has attracted many researchers in different fields including applied mathematics, business, medicine, and social sciences , Linear regression models involving a large number of independent variables are poorly performing due to large variation and lead to inaccurate conclusions , One of the most important problems in the regression analysis is the multicollinearity Problem, which is considered one of the most important problems that has become known to many researchers  , As well as their effects on the multiple linear regression model, In addition to multicollinearity, the problem of outliers in data is one of the difficulties in constructing the reg

BackgroundCarcinoma of the larynx represent 10% of head & neck malignancies. The treatment of advanced carcinoma of larynx may include partial or total laryngectomy, with or without laser , radiotherapy,and or chemotherapy . Carcinoma of the larynx usually affect old age , heavy smoker with possible risk of pulmonary diseases & ischemic heart disease , which add risk to the general anaesthetic complication operative & postoperative - Objectives this study was designed to assess the feasibility of total laryngectomy under local anaesthesia in medically unfit patients for general anaesthesia & to re-establish practice doing total laryngectomy under local anaesthesia in those patients.
Methods a prospective study on 12 pa

BackgroundCarcinoma of the larynx represent 10% of head & neck malignancies. The treatment of advanced carcinoma of larynx may include partial or total laryngectomy, with or without laser , radiotherapy,and or chemotherapy . Carcinoma of the larynx usually affect old age , heavy smoker with possible risk of pulmonary diseases & ischemic heart disease , which add risk to the general anaesthetic complication operative & postoperative - Objectives this study was designed to assess the feasibility of total laryngectomy under local anaesthesia in medically unfit patients for general anaesthesia & to re-establish practice doing total laryngectomy under local anaesthesia in those patients.
Methods a prospective study on 12 pa

Metaheuristic is one of the most well-known fields of research used to find optimum solutions for non-deterministic polynomial hard (NP-hard) problems, for which it is difficult to find an optimal solution in a polynomial time. This paper introduces the metaheuristic-based algorithms and their classifications and non-deterministic polynomial hard problems. It also compares the performance of two metaheuristic-based algorithms (Elephant Herding Optimization algorithm and Tabu Search) to solve the Traveling Salesman Problem (TSP), which is one of the most known non-deterministic polynomial hard problems and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. The experimental results of Ele

 Exponential Distribution is probably the most important distribution in reliability work. In this paper, estimating the scale parameter of an exponential distribution was proposed through out employing maximum likelihood estimator and probability plot methods for different samples size. Mean square error was implemented as an indicator of performance for assumed several values of the parameter and computer simulation has been carried out to analysis the obtained results

Human beings are greatly inspired by nature. Nature has the ability to solve very complex problems in its own distinctive way. The problems around us are becoming more and more complex in the real time and at the same instance our mother nature is guiding us to solve these natural problems. Nature gives some of the logical and effective ways to find solutions to these problems. Nature acts as an optimized source for solving the complex problems.  Decomposition is a basic strategy in traditional multi-objective optimization. However, it has not yet been widely used in multi-objective evolutionary optimization.   

Although computational strategies for taking care of Multi-objective Optimization Problems (MOPs) h

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a