Brachytherapy treatment is primarily used for the certain handling kinds of cancerous tumors. Using radionuclides for the study of tumors has been studied for a very long time, but the introduction of mathematical models or radiobiological models has made treatment planning easy. Using mathematical models helps to compute the survival probabilities of irradiated tissues and cancer cells. With the expansion of using HDR-High dose rate Brachytherapy and LDR-low dose rate Brachytherapy for the treatment of cancer, it requires fractionated does treatment plan to irradiate the tumor. In this paper, authors have discussed dose calculation algorithms that are used in Brachytherapy treatment planning. Precise and less time-consuming calculations

Class consciousness represents its highest stage in the contemporary Marxist thought of Rosa Luxemburg and Antonio Gramsci, not just a reflection of reality, but rather a dialectical form through the reflection of consciousness on reality and its reproduction. The superiority over the infrastructure (historical materialism) promised the revolutionary class consciousness to be achieved spontaneously, and the seeds of a breakthrough are mass strikes, denying the role of the party to organize this awareness, limiting its role to the interconnection between classes, emphasizing the role of socialist democracy as a conscious vanguard for organizing spontaneity, struggle, and developing awareness during the revolutionary process and the leader

Any software application can be divided into four distinct interconnected domains namely, problem domain, usage domain, development domain and system domain. A methodology for assistive technology software development is presented here that seeks to provide a framework for requirements elicitation studies together with their subsequent mapping implementing use-case driven object-oriented analysis for component based software architectures. Early feedback on user interface components effectiveness is adopted through process usability evaluation. A model is suggested that consists of the three environments; problem, conceptual, and representational environments or worlds. This model aims to emphasize on the relationship between the objects

In this article, we propose a Bayesian Adaptive bridge regression for ordinal model. We developed a new hierarchical model for ordinal regression in the Bayesian adaptive bridge. We consider a fully Bayesian approach that yields a new algorithm with tractable full conditional posteriors. All of the results in real data and simulation application indicate that our method is effective and performs very good compared to other methods. We can also observe that the estimator parameters in our proposed method, compared with other methods, are very close to the true parameter values.

The marketing logistic chain, as an integrated system aimed to balance the achievement of its main opposite objectives which represented in the access to the best service presented to the customer with lowest possible logistic costs especially the transportation costs, where encourages the researcher to choose the second objective as a field of this study in order to reduce the transportation costs in the final link of marketing logistic chain which related to delivering of fuel oil to the customer that falls within organizational responsibilities of the company under consideration (Oil Marketing Company) and also known in a brief name by (SOMO) through two methods, the first is by functioning quantative techniques by using trans

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the