In this paper, the oscillation of a Hematopoiesis model in both cases delay and non-delay are discussed. The place and are continuous pstive -rdic functions. In the nn-dlay cse, we will exhibit that a nonlinear differential equation of hematopoiesis model has a global attractor  for all different pstive solutions. Also, in the delay case, the sufficient conditions for the oscillation of all pstive solutions of it aboutare presented and we establish sufficient cnditions for the global attractive of. To illustrate the obtained results some examples are given.

The present work was done in an attempt to build systematic procedures for treating warts by 810 nm diode laser regarding dose parameters, application parameters and laser safety. The study was done in Al- Kindy Teaching Hospital in Baghdad, Iraq during the period from 1st October 2003 till 1st April 2004. Fifteen patients completed the treatment and they were followed for the period of 3 months. Recalcitrant and extensive warts were selected for the study. Patients were randomly divided into 3 groups to be treated by different laser powers 9, 12 and 15 W, power density of 286 W/cm2, 381W/cm2, 477 W/cm2 pulse duration of 0.2 s, interval of 0.2 s and repeated pulses were used. The mode of application was either circular or radial. Pain oc

The software-defined network (SDN) is a new technology that separates the control plane from data ‎plane for the network devices. One of the most significant issues in the video ‎surveillance system is the link failure. When the path failure occurs, the monitoring center cannot receive the ‎video from the cameras. In this paper, two methods are proposed to solve this problem.  The first ‎method uses the Dijkstra algorithm to re-find the path at the source node switch. The second method ‎uses the Dijkstra algorithm to re-find the path at the ingress node switch (or failed link).

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

To obtain the approximate solution to Riccati matrix differential equations, a new variational iteration approach was ‎proposed, which is suggested to improve the accuracy and increase the convergence rate of the approximate solutons to the ‎exact solution. This technique was found to give very accurate results in a few number of iterations. In this paper, the ‎modified approaches were derived to give modified solutions of proposed and used and the convergence analysis to the exact ‎solution of the derived sequence of approximate solutions is also stated and proved. Two examples were also solved, which ‎shows the reliability and applicability of the proposed approach. ‎

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

     Data mining is a data analysis process using software to find certain patterns or rules in a large amount of data, which is expected to provide knowledge to support decisions. However, missing value in data mining often leads to a loss of information. The purpose of this study is to improve the performance of data classification with missing values, ​​precisely and accurately. The test method is carried out using the Car Evaluation dataset from the UCI Machine Learning Repository. RStudio and RapidMiner tools were used for testing the algorithm. This study will result in a data analysis of the tested parameters to measure the performance of the algorithm. Using test variations: performance at C5.0, C4.5, and k-NN at 0% missi

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

     Groundwater is necessary to ensure the safety of Mandali's water supply. Groundwater samples were collected from various parts of the city for this study. Two aquifers have been identified: the first is a free bed made up of Quaternary deposits. The second is the primary bed that conveys water,  belonging to the Bai Hassan Formation. Nineteen groundwater samples were collected from wells and examined for physicochemical parameters in the current study during October 2021. Samples were analyzed for main cations Ca²⁺, Mg²⁺, Na⁺, and K⁺;  anions HCO^3- , SO₄^2- , Cl^- and NO₃^-,  pH, Electrical Conductivity, and Total Dissol