Stability challenges in Microgrids (MGs) usually arise from low inertia of Distributed Generation (DG). In this paper, a voltage Stability improvement method is proposed in order to improve MG operation. Voltage Stability Index (VSI) is applied to evaluate and improve voltage Stability of MGs including different types of DGs. A new hybrid optimization is introduced to find the optimal operation of autonomous MG and to improve VSI. Operational optimization is performed by finding optimal droop parameters of DGs and sitting wind DGs to reduce energy generation cost. Optimization is defined as a multi-objective function and a hybrid HS-GA algorithm is applied to solve the optimization problem. A new power flow formulation is also proposed in which the steady state frequency, reference frequency, droop coefficients and, reference voltage of droop based DGs are considered as optimization variables. Results of proposed approach are compared with other methods for 33 and 69-bus IEEE systems using MATLAB software. Results prove the efficiency of proposed approach for operational improvement of MGs.

The central issue of the present paper is the study of the causes of the mother tongue decline in the Sahne city in Kermanshah province. The issue of language is beyond the dialect of people and is refer to the form of life and the matter of macroeconomic and political relations. From this perspective, the form of life and the language associated with it explain the praxis of the members of the society. The appropriate method of examining the problem should be able to analyze the justification of agents to their situation, that is why Grounded Theory is chosen as the method of this research and data analysis is taken from abduction reasoning. The results of the analysis introduced the categories of globalizing, modernizing, de-use of language, economic necessity and the government as the most important factors of Local language fading. In this essay the meaning of globalization is the globalizing of special type of economic relation that explain other categories role in the process of fading of languages.

Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of Local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of Local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

The improvement and use of high quality and productive cultivars are priority goals in alfalfa (Medicago sativa L.) farming and research in the country. This study was conducted using 32 alfalfa genotypes in a RCB design with three replications during 2012 and 2013 at Seed and Plant Research Institute (SPII), Karaj, Iran. In the first year of the experiment, two cuts and in the second year, four cuts (six cuts in total) were taken and recorded. The studied traits included fresh and dry forage yield, plant height at harvest time and stem number per square meter. The ANOVA results based on the split plot design in time showed a significant difference among cuts, genotypes, and genotype by cutting interaction. Mean comparisons of the cuts revealed that the second cut had the highest fresh and dry forage yield in both years. The genotypes of D203, D210, D207, D206 with average values of 3.34, 3.34, 3.33, and 3.31 (t ha-1 per cut), and Ranger with 2.21(t ha-1 per cut) had the highest and lowest means of forage dry yield, respectively. D207, D206, D203, D210 and LakLak genotypes had the lowest superiority index and D206, Galebani, D205, D207, Laklak, and D201 genotypes had the lowest Shukla’s Stability variance, respectively. According to the obtained results of dry forage yield and the Stability indices, the genotypes D206, D207 and LakLak were selected as the most stable and productive genotypes.

The aim of this study was to investigate the effect of using portulaca oleracea as a source of plant antioxidants for feeding fattening male lambs, on carcass traits, meat quality, and oxidative Stability. Twenty-one male lambs with an average weight of 24±1.5 kg and 150±15 days old were fed with one of the experimental rations included a diet without portulaca oleracea as control, a diet containing 7.5%, and a diet containing 15% portulaca oleracea, for 84 days. The muscle (Longissimus dorsi) samplewas used to determine pH, chemical composition, colorimetric properties, and oxidative Stability of meat. Meat lipid oxidation was determined after 1, 7, and 30 days of refrigerated storage using thiobarbituric acid (TBARS). The use of portulaca oleracea in diets improved meat color characteristics such as L, chroma, and Hue. The highest ash concentration and the lowest meat moisture were observed in the treatment of 15% portulaca oleracea (P<0.05). Meat pH was the lowest in the first hour after slaughter but did not differ at 24 h. Compared to the control, in both treatment groups feeding the lambs with portulaca significantly reduced the concentration of Malondialdehyde in the meat of the lambs and increased the meat oxidative Stability (P<0.05). The results of this experiment showed that feeding portulaca oleracea by improving properties such as color and increasing the meat oxidative Stability improves meat quality, which may have lead to an increase in its nutritional value in terms of human health.

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 non-autonomous Ginzburg-Landau equation with time-periodic coefficients is derived for two modulated double-diffusive stationary convection involving couple stress liquid. The heat and mass transports are quantified in terms of Nusselt and Sherwood numbers, which are obtained as functions of the slow time scale. Effects of Prandtl number, Lewis number, solute Rayleigh number and couple stress parameter have been discuused in detail.