Background: Bladder cancer (BC) is the most common malignant tumor in the urinary tract and the tenth most common malignancy worldwide. Exosomes are 40–100 nm-diameter nanovesicles that are either released straight from the plasma membrane during budding or merged with the plasma membrane by multivesicular bodies. Objectives: To assess the proportion of serum and urinary Exosome levels in urinary bladder cancer patients, as well as their impact on the disease. Methods: From January 2023 to June 2023, a total of 45 samples of blood and urine were collected from individuals diagnosed with bladder cancer at the Ghazi Hariri Hospital for Specialized Surgery. They included 45 male and female patients, varying in age, as well as 45 heal

Al-Chibayish Marsh (CM)  is considered as the major part of Central Marshes area of this marsh is 1050 Km². The water quality of these marshes is suffering from salt accumulation due to intensive dam construction, limited supply of water from sources,  climate change impacts, and the absence of outlet flow from these marshes, specifically at low flow periods. So, the current research aims to assess and improve these marshes' hydraulic behavior and water quality and define the best location for outlet drains.  Field measurements and laboratory tests were conducted for two periods (November 2020 and February 2021) to define the (TDS) concentrations at nine different locations. Samples were also examined for water's phy

     Kp index correlates with the many magnetosphere properties, which are used to measure the level of magnetic activity. In the solar system, the two different planets, Mercury with weak magnetic field and Jupiter with strong magnetic field, are selected for this study to calculate the planet's magnetosphere radius (R_MP) which represents the size of magnetosphere compared with solar activity through Kp index,  through two types of geomagnetic conditions; quiet and strong for the period (2016-2018). From the results, we found that there are reversible relations between them during strong geomagnetic storms, while there are direct relations during quiet geomagnetic conditions. Also it is found that the

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

In this paper was discussed the process of compounding two distributions using new compounding procedure which is connect a number of life time distributions ( continuous distribution ) where is the number of these distributions represent random variable distributed according to one of the discrete random distributions . Based on this procedure have been compounding zero – truncated poisson distribution with weibell distribution to produce new life time distribution having three parameter , Advantage of that failure rate function having many cases ( increasing , dicreasing , unimodal , bathtube) , and study the resulting distribution properties such as : expectation , variance , comulative function , reliability function and fa

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of

Lagrange series and the Bessel function are two classical methods that were created by series expanding from Taylor series. In this paper, the purpose of those two methods was to find the values of the eccentric anomaly for one period (0–360)°. The Matlab program is used to apply the results, the input parameters were eccentricity (0–1), mean anomaly (0–360)°, and finally the parameter W (1–13). The program does not need a tolerance to obtain a precise value for eccentric anomaly like other iterative and non-iterative methods to stop the program; it will stop after completing the required period from 0° to 360° for a body that is determined by the solver. The output will be the final value of the eccentric anomaly. Furthermore,

In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chro