The existing theoretically derived order SELECTION criteria for autoregressive (AR) processes have poor performance in the finite SAMPLE case. In this paper, the least-squares-forward (LSF) is considered as the AR parameter estimation method, and new theoretical approximations are derived for the expectations of residual variance and prediction error. These approximations are especially useful in the finite SAMPLE case and are derived for AR processes with arbitrary statistical distributions. New order SELECTION criteria for AR processes are derived using these approximations. In a simulation study, the performance of the proposed criteria relative to other criteria is examined in the finite SAMPLE case. Simulation results show that the performance of the proposed criteria is much better than the other theoretically derived criteria.

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.

Feature SELECTION can significantly be decisive when analyzing high dimensional data, especially with a small number of SAMPLEs. Feature extraction methods do not have decent performance in these conditions. With small SAMPLE sets and high dimensional data, exploring a large search space and learning from insufficient SAMPLEs becomes extremely hard. As a result, neural networks and clustering algorithms perform poorly on this kind of data. In this paper, a novel hybrid feature SELECTION technique is proposed, which can reduce drastically the number of features with an acceptable loss of prediction accuracy. The proposed approach operates in multiple stages, starting by removing irrelevant features with a low discrimination power, and then eliminating the ones with low variation range. Afterward, among each set of features with high cross-correlation, a single feature that is strongly correlated with the output is kept. Finally, a Genetic Algorithm with a customized cost function is provided to select a small subset of the remainder of features. To show the effectiveness of the proposed approach, we investigated two challenging case studies with SAMPLE set sizes of about 100 and the number of features larger than 1000. The experimental results look promising as they showed a percentage decrease of more than 99% in the number of features, with a prediction accuracy of more than 92%.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature SELECTION is a common approach to tackle this issue by choosing prominent features. Multi-label feature SELECTION is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

The aim of this study was a strain-improvement program for Trichoderma reesei PTCC 5142 by using a combination of UV light and NTG (N-methyl-N'-nitro-N-nitrosoguanidine) for enhanced cellulase production. Following mutagenesis after several rounds, mutant A6: 2 was selected from a total of 6500 colonies. Results obtained after 4 days were: Enzyme activity 1.26 U/ml and 0.82 U/ml for exoglucanase and endoglucanase, respectively. The comparative results showed increased production exoglucanase and endoglucanase by mutant A6: 2 than Trichoderma reesei PTCC 5142 to amount 130% for exoglucanase and 156% for endoglucanase.

We extend the method of adaptive two-stage sequential sampling to include designs where there is more than one criteria used in deciding on the allocation of additional sampling e ort.These criteria, or conditions, can be a measure of the target population, or a measure of some related population. We develop Murthy estimator for the design that is unbiased estimators for the population mean, and propose another, more efficient, estimator. We investigate asymptotic properties of this estimator. We use a simulation study to investigate design properties of the multi-criteria adaptive stratified sequential sampling scheme and also some estimator properties under the design.

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.

Mate SELECTION is one of the most important decisions made by individuals during their lives. The purpose of this study was to examine the validity, reliability and factor structure of Schwarz and Hassebrauck Mate SELECTION Criteria Inventory (2012) in Iranian culture. The study SAMPLE was consisted of 486 individuals who were selected among university students through a partial sampling. The participants completed the Mate SELECTION Criteria Inventory (MSCI) and the Mate SELECTION Criteria Priority Questionnaire. The results of the confirmatory factor model showed that the 9-factor model of Mate SELECTION Criteria Inventory (MSCI) had an appropriate fit in Iranian SAMPLE. The value of Cronbach’ s Alpha of the factors ranged between 0. 51 and 0. 91, and it was between 0. 53 and 0. 72 using retest method with an interval of 1 month. The correlation among the factors of MSCI and the factors of Mate SELECTION Priority Questionnaire was positive and significant. The results of the study show that the most important factors of mate SELECTION in Iranian culture are trustfulness, kindness, and understanding respectively.