In order to investigate the effect of genetic markers in genetic improvement of domestic fowl, seven breeding programs were simulated. In first breeding program called base program, phenotypic information of traits was only used. In this program, aggregative genotype equation consisted of body weight at 8 weeks of age, egg weight and egg number and selection index consisted of traits in aggregative genotype together with two other tarits including body weight at 12 weeks of age and age at sexual maturity. In other programs called marker assisted programs, QTL information that described 5, 10, 20, 30, 40 and 50% of genetic variation of egg number were also included in selection index. Based on obtained results, in program that QTL described 5% of genetic variance, economic response was improved by 3.37% compared to base program. Economic response in program that QTL described 50% of genetic variance was also improved by 14.28% compared to base program. Rate of Inbreeding was decreased from 0.69% in base program to 0.50% in program that QTL described 50% of genetic variance in each generation. The result of present study showed that QTL information, even with 5% description of genetic variance, can effectively improve economic traits in native fowl.

The effectiveness of schools is considerably depends on the teachers ‘voluntary behaviors which are named as "Organizational Citizenship Behaviors (OCB)". If teachers are forced to show these behaviors, they are called as "Compulsory Organizational Citizenship Behaviors (COCB)". The present knowledge about these behaviors is very scarce. Through acknowledging this gap, this mixed study has identified the contextual indicators and dimensions of this newly emerged variable. First, at qualitative stage, educational experts were selected through purposeful and snowball sampling and then the sample was interviewed. The results of interview coding identified 32 indicators. These indicators were classified into 5 dimensions: 1) top- level managers, 2) principals and assistants, 3) external organizations and institutions, 4) colleague teachers, and 5) students and parents. At quantitative stage, according to the identified indicators and dimensions, a questionnaire was provided and then filled out by 357 teachers in Babol and Babolsar. Data of the quantitative stage were analyzed using Confirmatory Factor Analysis (CFA) and one-sample t-test. The results of CFA showed that the measurement model of COCB fits well with the data. Also, the results of the t-test showed that the present situation of COCBs among the studied teachers, are not so worrying. This study, finally discusses its findings, suggests the variable and the questionnaire of COCB to the interested researchers.

in the primary education system of Iran in 2025 and prioritize it, using qualitative research to develop criteria and indicators and quantitative research to prioritize criteria and indicators with the statistical population in Document analysis section includes contents in the field of environment, bio-education, Supreme Leader communiqué on general environmental policies, Article 50 of the Constitution of the Islamic Republic of Iran, 20-year vision document of the Islamic Republic of Iran 2025, National Document on Fundamental Transformation of Education (Objectives And macro strategies), the national curriculum document that was selected based on the theoretical saturation rule and all professors and specialists in the field of education and environmental experts who were selected using the saturation rule of 30 people by snowball sampling method and method Analysis in the study of documents, the method of content analysis and in interviews with professors and experts, was done by the method of theme analysis. Based on the results obtained; Underlying philosophy of bioeducation in Iranian education in the form of 9 axes, macro goals of bioeducation in the form of 14 macro goals, essential criteria and indicators of bioeducation in the fields of knowledge (21 criteria), understanding (5 criteria), individual responsibilities and Civil (5 criteria) and questioning, analysis and interpretation (7 criteria) were developed in the elementary course

The higher number of markers (p) to the number of observations (n) is the first challenge (i.e., course of dimensionality; p>>n) in genomic selection. Therefore with aim to reduce the course of dimensionality, the effect of reduce markers density on the accuracy of genomic prediction breeding values of parametric methods investigated. In this order a genome consisted of 10000 bi-allelic single nucleotide polymorphism (SNP) over 10 chromosomes, with 100 cM length each, was simulated. In this research, the different gene distributions (i.e., uniform, Gaussian and gamma), different levels of heritability (0.05, 0.25, 0.45 or 0.65) and number of quantitative trait loci (QTL; 100 or 500) were considered as assumption of simulation. Then in a selection scenario only 10% of the markers and in the other scenario 20% of the markers were selected (randomly), so that for each of the population three different marker matrices with different dimension (all, 10% and 20% of markers) were defined. According to the finding of this research, in order of preference the accuracy of genomic breeding values is influenced by the trait inheritance, the distance with the reference population, the marker density, the distribution and the number of QTLs and statistical models (p <0.001). So that in all three genetic effects distribution and with each form of the marker structure (selection or without selection of markers) it was observed that the accuracy of predictions declined significantly (p <0.05) as a result of reduction of heritability or with increasing distance from the reference population. The effect of reduce markers density on the genomic prediction accuracy of traits with different genetic architecture showed that except for traits with low heritability (0.05) and high number of QTL (500) and allele effects drown from a gamma distribution, in general reduction of marker density resulted to decrease accuracy of genomic predictions (p <0.05).

In recent decades، along with the developments of the molecular approaches، many researches were done to detect genes or chromosome segments that affect the economical traits of domestic animals. Notwithstanding extensive researches towards marker assisted selection (MAS) method، there has been limited implementation of MAS and minor increases in genetic gain due to its several limitations. The successful completion of the human genome sequence and huge developments regarding high-throughput assays for genotyping single nucleotide polymorphisms (SNP) across the genome has made the possibility to apply MAS in genomic scale which is called genomic selection. Genomic selection predicts the total genetic values using genome-wide dense marker maps so that all QTLs are in linkage disequilibrium with at least one marker. Calculation of genomic breeding values requires estimating SNP effects from a subset of animals in the population with information on both phenotypes and markers genotype called the reference population. Estimated markers effects in the reference population is then used to calculate genomic breeding values of selection candidate individuals in evaluation population which has only markers information. According to the literature، factors affecting accuracy of genomic evaluations was the heritability of the trait، the size of the reference population، extent of relationships between selection candidates and the reference population، the method used to estimate marker effects، linkage disequilibrium among markers and QTLs and the distribution of QTLs effects. In a nutshell، genomic selection have many advantages over traditional methods including an increase in accuracies of breeding value prediction، shortening the generation interval for many traits and thus improving the rate of genetic gain and decrease evaluation costs. Furthermore، genomic selection by using genetic markers better predict Mendelian sampling term of breeding values and decrease inbreeding rate compared to traditional selection.

Identification of molecular markers associated with quantitative and qualitative traits can facilitate selection for these traits in breeding programs. This study was designed to identify AFLP markers associated with agronomic and fiber traits using an F4 population derived from interspecific cross between Gossypium hirsutum and G. barbadense. Seeds were grown in the field and plants were scored for eleven agronomic and fiber quality traits. Correlation analysis between AFLP markers and traits indicated that 19 and 14 markers were significantly associated positively or negatively with fiber and agronomic traits. Only markers which have been correlated with a specific traits significantly were used for stepwise multiple regression analysis. Associated markers explained from 6. 2% (for lint weight، seed weight and micronaire) to 43% (for 2. 5% span length) of the total variation available for different individual traits. Results suggest that these AFLP markers could be used for predicting and selecting desirable cotton germplasm to improve these traits.

Genomic selection is a method to predict the breeding values of individuals using a large number of single nucleotide polymorphism markers. The cost of the high-density marker panel genotyping is very high, which prevents the widely application of genomic selection. The present simulation study was carried out to evaluate the use of low to medium marker density panels to predict direct genomic values. In this study, a trait with heritability of 0.10, 0.20, and 0.40 was simulated. The simulated genome was consisted of 25 outosomes with the same distance (1morgan). Different marker density (12.5, 27.5 and 50 k) and 125, 250, 500 and 1000 random distributed QTL were simulated. The least square means of genomic accuracy varied between the different levels of heritability and the different marker density (P<0.05)­. For low heritable trait (10℅), increasing the markers density had no effect on genomic accuracy with low (125) or high (1000) number of QTL, but did with the moderate number of QTLs (250 and 500 QTLs). The results showed that along with increasing in the number of markers, genomic accuracy was not changed in traits with medium heritability (20℅). For high heritable trait (40℅), increasing the marker density had no effect on genomic accuracy whit the moderate to high QTL density (250, 500 or 1000 QTLs), but but it increased the accuracy with low number of QTLs (125 QTLs).

In camera-based motion tracking, the target structure has a significant impact on the tracking accuracy. In the present research, a new method is proposed for the optimization of the location of five markers on a visible target for the camera. The proposed objective function for positioning of markers is equal to the total distance of each marker from all of the planes formed from the combination of triads made of other markers. To avoid the symmetry of the target structure, which makes the labeling of markers on the image impossible, a constraint is applied that guarantees a minimum difference in the distance between the pairs of markers. The genetic algorithm is exploited for maximizing the proposed objective function with the mentioned constraint. The experimental results obtained from the positioning of markers on the target using the proposed method (with or without the application of the constraint) and other methods are evaluated. These methods are compared in terms of error in estimating 3D pose and tracking speed in the presence of noise. It is notable that the results confirm the applicability of the proposed method in the positioning of markers.

Technology leadership is an emergent field in school leadership literature and suggests that school principals play a proactive role in implementing technology in their schools. Current Iranian knowledge about this role is very scarce. The aim of current exploratory mixed research (qualitative-quantitative) was to investigate principals’ role in integrating technology at schools. Initially in qualitative phase of the study, educational technology experts were interviewed. The participants were selected using purposive and snow-ball sampling. The results of content analysis and coding of interviews revealed 58 indicators and 9 dimensions including: principals'''' belief to employ technology, illustrating technology objectives and expectations, technology planning, establishing and reinforcing the culture of utilizing technology, teachers'''' empowerment for utilizing technology; encouraging and stimulating teachers to employ technology; appraisal of teachers'''' technology performance, strengthening the sharing of technology knowledge, and technology support. Using the identified indicators and dimensions, a Technology Leadership (TL) questionnaire was designed and developed by the researcher to gather quantitative data. 335 teachers were, then, selected from smart schools of Tehran and were requested to complete the questionnaires. The collected data were analyzed using Confirmatory Factor Analysis (CFA). The result of CFA revealed that the measurement model of TL has fit suitably to the observed data. This study highlights the role of principals in technology integration by teachers and recommends TL variable and questionnaire to researchers. The results are discussed in the final section of the paper.

The present study was conducted in order to select effective markers in breed discrimination and compare the performance of SNP marker selection methods with the data of 304 animals from 14 different breeds that were genotyped using the Illumina SNP50K marker panel. Knowledge of genetic structure are very important for better understanding of genetic changes in genomic studies. The information content of each marker is used as an index for selecting markers in reducing the size of marker panels. To estimate the information content of each marker, the following selection methods were used: Fst (pairwise & global), Theta, Delta, D, Gst, G'st, G"st and Principal Component Analysis. In this study, the logarithm of the likelihood ratio was used to select markers. According to the results, all selection methods for identifying markers had similar performance. The number of common markers between the methods was at least 42 markers and at most 499 SNP markers. In general, the F_ST statistical method required a smaller number of markers to achieve a successful assignment. G'st and G"st statistics showed poor performance with more than 350 markers to achieve 95% correct assignment. It should be noted that with only the top 60 selected markers, it is possible to achieve a success rate of more than 70%. According to the results, Wright's paired Fst had better performance than other SNP selection methods. The obtained results lead to the creation of exclusive panels to identify various breeds, which have great economic importance.