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.

Purpose: Managers are one of the important elements of an organization, for this reason, in order to draw the future of the organization, it is necessary for the planners to specify the conditions of their Selection and appointment. Therefore, the current research has been done with the aim of identifying and analyzing the components of selecting future principals.Method: In this research, comparative and benchmarking method is used as a prospective approach. This approach is based on the belief that today's advanced organizations/countries can be considered as a Model for the future of another organization/countries in their respective subjects. For this, first, the fields of comparison and benchmarking were determined using Brody's four-step comparison method; then the countries of Canada, Finland, Australia, South Africa, and Japan were selected according to the qualitative balance value in the international advanced TEAMS test, human development index, life quality index(health, instruction, and welfare), education quality index, and other scientific-scholarly indexes; finally, by extracting the criteria for the Selection and appointment of principals through content analysis and comparison with Iran, the proposed framework for Iran has been presented.Findings: A total of 61 components for the Selection of secondary school principals were identified from among the studies conducted in the selected countries in this article. By extracting the commonalities and differences of each of the components among the countries, it was found that the highest index of manager Selection and appointment belongs to Japan and the lowest one is related to Finland.Conclusion: There are similarities between the components of Selection of principals of secondary schools in Iran and selected countries. In Iran, special attention should be paid to important components such as adherence to religious principles, appropriate personality traits, creativity and innovation, motivation to develop capabilities, professional growth, power of supervision and accountability, social image, leader skills, and purposefulness and foresight.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron Model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed Model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature Selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the Model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the Model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented Model is validated in comparison with some state-of-the-art works.

In this paper, we developed robust optimization approach that departs from the randomness assumption used in other methods of optimization under uncertainty and describe uncertainty in parameters through uncertainty sets; for portfolio Selection problem. The Model can control the conservativeness of investor for portfolio Selection by a defined parameter. We used 50 active company of Tehran exchange stock in 3 first months of 1392 to study the performance of Model. The results of paired comparisons in out of sample experiments shows that Markowitz portfolio which has same expected return by robust portfolio, has lower Sharpe ratio.

This study introduces the Hill Model for Modelling sorption kinetics and illustrates its efficacy using formal Model Selection procedures. Although the coefficient of determination (R2) value can be used to ensure goodness-of-fit of a particular Model to a set of data, a measure that incorporates the number of Model parameters, such as the Bayesian information criterion, law of iterated logarithm criterion, Akaike information criterion, and the extra sum-of-squares F-test is necessary to undertake reliable Model Selection. This study undertook a formal Model Selection procedure based on these criteria, in combination with the coefficient of determination, for a range of candidate kinetics sorption Models [the pseudo-second order (PSO), the intra-particle diffusion (IPD), Power, Elovich, and the four and five parameter Hill Models]. It was found that the Model order that statistically best described the data was Hill 5 > Hill 4 > pseudo-second order > Elovich > Power > IPD as given by the evidence ratios, information criterion weights and Bayes factors. The extra sum-of-squares F-test, only applicable to nested Models, confirms the best fit order as Hill 5 > Hill 4 > PSO Models. Two new parameters (T and k Hill) have also been defined that give the Hill Models a mechanistic meaning directly comparable to the well-defined parameters of the pseudo-second-order Model, namely, the pseudo-second-order rate constant k pso and the instantaneous sorption coefficient h pso. In the field of geochemistry the use of the four and five parameter Hill Models for describing sorption kinetics has been overlooked with many studies favouring the PSO Model. It is strongly recommended that the Hill Model be considered more widely as a general predictive tool in geochemistry.