The interaction of charged particles with the chemical elements involved in the synthesis of human tissues is one of the modern techniques in radiation therapy. One of these charged particles are alpha particles, where recent studies have confirmed their ability to generate radiation in a highly toxic localized manner because of its high ionization and short its range. In this work, We focused our study on the interaction of alpha particles with liquid water; since the water represents over 80% of the most-soft tissues, as well as, hydrogen, oxygen, and nitrogen ,because they are key chemical elements involved in the synthesis of most human tissues. The mass stopping powers of alpha particle with HଶO , COଶ, Oଶ, Hଶ and Nଶhave been calculated in energy range (0.001-1000) MeV, using four methods (Beth-Bloch equation, Zeigler formula and SRIM2013 software, ASTAR program).We have produced semi-empirical formulas for calculating the mass stopping power of alpha particles, for such targets by knowing alpha particle energy. Comparing our results of the calculated mass stopping power with ICRU- Report 49 we find very good agreement between them, this confirms the ability of our result to be used in such cancer treatment and other fields where this quantity is used.
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
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This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.
Chemical compounds, characteristics, and molecular structures are inevitably connected. Topological indices are numerical values connected with chemical molecular graphs that contribute to understanding a chemical compounds physical qualities, chemical reactivity, and biological activity. In this study, we have obtained some topological properties of the first dominating David derived (DDD) networks and computed several K-Banhatti polynomials of the first type of DDD.
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Was appointed acid steady disintegration of organic EkandThe results proved that organic Allicand acting and Konnh solid baseBy Tgrav Pearson has possible account Maamat hardness and softness of organic Ekand
The purpose of this article is to improve and minimize noise from the signal by studying wavelet transforms and showing how to use the most effective ones for processing and analysis. As both the Discrete Wavelet Transformation method was used, we will outline some transformation techniques along with the methodology for applying them to remove noise from the signal. Proceeds based on the threshold value and the threshold functions Lifting Transformation, Wavelet Transformation, and Packet Discrete Wavelet Transformation. Using AMSE, A comparison was made between them , and the best was selected. When the aforementioned techniques were applied to actual data that was represented by each of the prices, it became evident that the lift
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Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.
In this paper we have been focus for the comparison between three forms for classification data belongs
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