The objective of this paper is to study the stability of SIS epidemic model involving treatment. Two types of such eco-epidemiological models are introduced and analyzed. Boundedness of the system is established. The local and global dynamical behaviors are performed. The conditions of persistence of the models are derived.

Abstract:

      In this research we discussed the parameter estimation and variable selection in Tobit quantile regression model in present of multicollinearity problem. We used elastic net technique as an important technique for dealing with both multicollinearity and variable selection. Depending on the data we proposed Bayesian Tobit hierarchical model with four level prior distributions . We assumed both tuning parameter are random variable and estimated them with the other unknown parameter in the model .Simulation study was used for explain the efficiency of the proposed method and then we compared our approach with (Alhamzwi 2014 & standard QR) .The result illustrated that our approach

The nature of interstellar matter has been explained by presenting a model consists of organic and inorganic materials mixture in the wavelength range (9-12) μm.
Laboratory samples of different concentrations of these materials were prepared and examined by using FTIR spectrometer device. Results of spectra for these samples are compared with observation of Trapezium nebula in the same wavelengths range. The best mixture model found to fit with observation is a sample consists of:11.96 % (Diatom silica (98% pure silica)) + 14.35 % (Carbon (C)) +27.63 % (Tryptophan amino acid) + 46.06 % (Tyrosine amino acid), this are done using convolution technique. Interstellar matter could be explained biologically, as the contribution of organic

Seismic instantaneous phase attribute was applied for conventional seismic interpretation (structural interpretation) on 3D seismic cube of 1914.72km² of Samawa-Diwan area, located in the south part of Iraq within Muthna governorate. Instantaneous phase section is very important to detect structural and stratigraphic features. Six reflectors represent Upper Jurassic and Cretaceous formations were defined from synthetic seismogram of wells in study area, then picked over seismic cube. Fault boundaries maps for each horizon were drawn depending on horizon contacts then fault planes were constructed. Finally, a 3D structural model was constructed in time domain, then converted to depth domain by using 3D average velocity model. Structurall

            Semi-parametric regression models have been studied in a variety of applications and scientific fields due to their high flexibility in dealing with data that has problems, as they are characterized by the ease of interpretation of the parameter part while retaining the flexibility of the non-parametric part. The response variable or explanatory variables can have outliers, and the OLS approach have the sensitivity to outliers. To address this issue, robust (resistance) methods were used, which are less sensitive in the presence of outlier values in the data. This study aims to estimate the partial regression model using the robust estimation method with the wavel

Chemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients’ bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and hypotheses subject to parametric uncertainties and exo

   In this work , we applied the nuclear shell model by using Modified Surface Delta Interaction ( MSDI ) to study the nuclear structure for Ti42-44 nuclei from the calculation of the energy level values and its total angular momentum .      After comperation with the experiment values which found to be rather in good agreement and determined the total angular momentum values of energy levels which are not assigned experimently , as soon as , we certify some values that were not certained experimently .  

This article aims to estimate the partially linear model by using two methods, which are the Wavelet and Kernel Smoothers. Simulation experiments are used to study the small sample behavior depending on different functions, sample sizes, and variances. Results explained that the wavelet smoother is the best depending on the mean average squares error criterion for all cases that used.

    In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne