Quantitative genetic theories based on infinitesimal Model have been very successful in selecting the best animals in the last century. One of the methods based on IFM that has been used widely in quantitative genetics is best linear unbiased prediction (BLUP). Despite, because of the limited amount of genetic material and finite number of loci for each trait some infinitesimal Models assumptions can be violated. Since 1970, molecular genetics has opened this black box by mapping the single genes affecting the quantitative traits. Therefore in the past 15 years, the major effort in animal breeding has changed from quantitative to molecular genetics with emphasis on marker assisted Selection (MAS).However, results have been modest. In 2001, based on a computer simulation study, genomic Selection as markers covering the whole genome was proposed as a variant of MAS. Simulated and real results have been shown that the breeding values could be predicted with higher accuracy in genomic Selection than traditional Selection. According to expert assessments, genomic Selection makes it possible to save 92% of the funds spent on traditional Selection and it is twice as efficient as the latter. However, there is a long way for reaching to phenotype from genotype; nevertheless, new technologies such as genomics, transcriptomics, proteomics and metabolomics can be useful in this way.

in the era of information, data which are worthwhile asset of human, organizations and enterprises have become such sophisticated that the conventional approaches and methods are not usable anymore, or not efficient at least. Such complexity which is known as the Big Data problem is the affordable extraction of value from big data sets that we are encountered in many recent applications e.g., e-business, scientific research, monitoring, search engines, social networking, etc.. Big Data complexities are instantiated by three major dimensions, high Volume, high Variety, and high Velocity (a.k.a.3Vs). The first and most essential step in data management (also for Big Data management) is designing and employing a proper data Model, as the footstone of the other data management activities such as R& D of DB languages, DBMSs, tools, methods, algorithms, etc.. In this paper, a proper data Model for Big Data is designed and proposed in which the properties required for Big Data problem (i.e., to be integrated, complete, scalable, flexible, compatible, and efficient) are considered. As a data Model, data representation is designed and implicit integrity constraints are presented for the proposed HNG (Hyper Nested Graph) data Model. Experimental evaluation results show that the proposed data Model outperforms other currently used data Models such as the document-based, graph document-based, and graph- based data Models in terms of response time.

Road crashes and their consequences are one of the most important problems that affect people's lives. In order to reduce the fatalities and related costs of crashes, traffic safety researchers are continuously investigating approaches to reduce the occurrence and consequences of crashes. Crash-type Modeling is one of the most common tools for road safety goals in transportation facilities, and the purpose of crash-type Modeling is to establish a relationship between the frequency of crashes based on its type and other effective variables. One of the advantages of crash-type Models is that with the help of these Models, it is possible to identify the places where there is a possibility of a certain type of crashes and to examine the effect of different variables on different types of crashes. In this research, using the data of freeway crashes in Iran, the type of crash was identified with a new approach called the Nested logit Model. To this aim, crashes were initially divided into two categories of single-vehicle and multi-vehicle crashes, and then single-vehicle crashes were divided into three categories of collision with a fixed object, run-off road crashes, and overturning crashes, and multi-vehicle crashes were divided into two categories of collision with a vehicle and multi-vehicle collision crashes. Then the effect of different variables of environment, road, driver, and causes of crashes with different types of crashes were investigated and the effect of significant variables on each type of crash was explored with marginal effect.

In contrast to the classical discrete choice experiment, the respondent in a rank-order discrete choice experiment, is asked to rank a number of alternatives instead of the preferred one. In this paper, we study the information matrix of a rank order Nested multinomial logit Model (RO.NMNL) and introduce local D-optimality criterion, then we obtain Locally D-optimal design for RO.NMNL Models in the discrete choice experiment.