davood darvishi

In order to evaluate the grain yield and quality of four quinoa cultivars (Chenopodium quinoa willd.) as affected by planting date and plant density, an experiment was conducted in two years (2018 &2019) at the Agricultural Research Station located in Jupar region of Kerman, Iran. A randomized complete block design arranged in a split factorial was used with four replications. Experimental treatments consisted of planting dates in three levels (24 July, 8 August and 23 August) as main plots and two factors including cultivars at four levels (Giza1,Titicaca, Q12 and Q18) and plant density (16.6 and 33.3 plant m-2) were arranged in sub plots. Planting date of 8 August produced the highest grain yield compared to the other two dates. The average grain yield of Giza1, Titicaca, Q12 and Q18 cultivars were 2484, 2239, 2547, and 2881 kg ha-1, respectively. Lodging was observed in cultivar Q18 in all planting dates. Giza1 cultivar with average of 18.23% crude protein and 94.12 mg kg-1 iron had better grain quality than other cultivars. The highest and lowest harvest index (0.35 and 0.26) belonged to Q12 and Titicaca cultivars, respectively. Giza1 and Q12 cultivars were the earliest and most late cultivars with 82 and 99 days of the growing season respectively. Based on the results, cultivation of Q12 on 8 August and Giza1 on 23 August with plant density of 33.3 plant m-2 is recommended for Jupar region of Kerman.

The aim of this study was to investigate the role of problem-solving skills in predicting mathematical motivation and mathematical self-efficacy in children with Special Learning Disorders such as mathematics in Tehran. The research method was descriptive-correlational. The statistical population of the study consisted of all students with learning disabilities in the first year of elementary school in Tehran in the academic year 2020. For the research sample, 90 students with math learning disabilities were selected by convenience sampling method. Data were collected through Hepner Problem Solving Skills Questionnaires (1988), Usher & Pajares Mathematical Self-Efficacy (2009), Corter Mathematical Motivation (2005) and Iran Mathematical Test by K Matt Grenus, Naji Man and Pritchett (1976). The data obtained from the implementation of the questionnaires were analyzed by regression statistical method. Findings showed that there is a positive and significant relationship between the components of problem-solving confidence, attitude-avoidance style and personal control with mathematical motivation and mathematical self-efficacy. The results of regression analysis also showed that problem solving skills are strong predictors of mathematical motivation and mathematical self-efficacy. Considering the role of problem-solving skills in creating and expressing mathematical motivation and mathematical self-efficacy in students with special learning disabilities such as math, it is possible to prevent math-specific learning disabilities by identifying them in a timely manner.

Covid-19 virus is a major threat to the health and safety of people around the world. One of the key components in dealing with this global threat is rapid and timely decision-making to control the epidemic of the disease, so predicting the future trend of this disease in the world, including predicting deaths, can be useful for policy-making, management and control of its prevalence. Therefore, the mortality rate caused by this virus has been predicted with grey models in the world.

This study examines the process of predicting mortality rates in the world using the theory of grey systems models. Research data were collected from the World Health Organization website and predicted the number of deaths in the world on a monthly basis by five methods GM (1, 1), Verhulst Grey, DGM (1, 1), NGBM (1, 1) and FNGBM(1, 1). In order to evaluate the error of the models, the common error evaluation criteria MAE, RMSE and MAPE were used.

By evaluating the model error, the prediction of the F-NGBM model (1, 1) in the category of excellent models, the prediction values of the GreyVerhulst model are in the category of acceptable predictions and the rest of the models are in the category of good predictions. Also, the F-NGBM (1, 1) model with MAE, RMSE and MAPE error values of 26989.54, 21533.94 and 7.21, respectively, is the most suitable model compared to the other methods. An estimated 250,958 deaths are estimated by the F-NGBM (1.1) model by the end of 2021, which may be the most appropriate value among forecasting methods.

 Due to the lack of historical data and also a lot of uncertainty in the available data, it is necessary to use approaches to dealing with uncertainty such as the grey system theory in predicting the mortality rate of this disease. Various grey predictions estimate the mortality rate, which requires relatively less data than existing methods, and the model error is much lower. The study also looked at the worldwide mortality rate and will be more comprehensive on integrated global action.

All governments especially those having a high risk of natural disasters must have a comprehensive and implementable plan. In Iran, the placement of relief centers for the injured is usually done experimentally by relief organizations without considering the necessary standards. In this research, according to the crisis management standard, the criteria are classified and determined as layers of information in the Geographic Information System (GIS). Then, the selected relief centers in the study area were evaluated according to the standard criteria and using the obtained matrix, the research criteria were weighed by entropy method. Finally, using net flows, performance scores and research constraints, the optimal options were identified by the PROMETHEE 5 method. The strengths and weaknesses of each of these options were also assessed. The results showed that half of the relief centers considered in the region were not optimal and had poor performance in most of the effective and important criteria for locating relief centers. Criteria of population density and distance from worn tissue were the most important criteria in this study.

Ant colony optimization (ACOR) is a meta-heuristic algorithm for solving continuous optimization problems (MOPs). In the last decades, some improved versions of ACOR have been proposed. The UACOR is a unified version of ACOR that is designed for continuous domains. By adjusting some specified components of the UACOR, some new versions of ACOR can be deduced. By doing that, it becomes more practical for different types of MOPs. Based on the nature of meta-heuristic algorithms, the performance of meta-heuristic algorithms are depends on the exploitation and exploration, which are known as the two useful factors to generate solutions with different qualities. Since all the meta-heuristic algorithms with random parameters use the probability functions to generate the random numbers and as a result, there is no any control over the amount of diversity; hence in this paper, by using the best parameters of UACOR and making some other changes, we propose a new version of ACOR to increase the efficiency of UACOR. These changes include using chaotic sequences to generate various random sequences and also using a new local search to increase the quality of the solution. The proposed algorithm, the two standard versions of UACOR and the genetic algorithm are tested on the CEC05 benchmark functions, and then numerical results are reported. Furthermore, we apply these four algorithms to solve the utilization of complex multi-reservoir systems, the three-reservoir system of Karkheh dam, as a case study. The numerical results confirm the superiority of proposed algorithm over the three other algorithms.

Linear programming problems with interval grey numbers have recently attracted some interest. In this paper, we study linear programs in which right hand sides are interval grey numbers. This model is relevant when uncertain and inaccurate factors make difficult the assignment of a single value to each right hand side. Some methods have been developed for solving these problems. In this paper, we propose a new approach for solving interval grey number linear programming problems is introduced without converting them to classical linear programming problems. A numerical example is provided to illustrate the proposed approach.

To study the effect of different hormone combinations on breaking dormancy and germination of Black Zira, 27 treatments consisted of three hormone, gibberellic acid (80, 100, and 120 μm), Thidiazuron (4, 6 and 8 μm) and benzyl adenine (5, 10 and 15 μm). Factorial experimental with three replications in a randomized complete block design was carried out. Seedling length, seedling fresh weight, seedling dry weight and the total percentage of germination were measured and seedling vigor index, fresh weight index, and dry weight index were calculated. Analysis of variance showed the significant differences between the effect of ecotype × combination × Hormone level for fresh weight, total percentage of germination and vigor index. Comparison of means for ecotype × combination × level effect showed the total percentage of germination and vigor index were highest for sixth level of GA × BA combination in Hormozgan ecotype and fresh weight was highest for fifth level of TDZ × BA combination in Semnan ecotype. Mean Comparison of ecotype effects showed that the dry weight index was the highest in Semnan ecotype and the Mean Comparison of level effects showed that the dry weight index was highest in fourth levels (6 μm TDZ and 5 μm BA and 100 μm GA). The result showed that hormones levels and combinations were suitable for seed germination in this experiment. Hormozgan ecotype had the best germination at GA × BA combination.