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.

مطالعه حاضر، تأثیر آموزش واژگان موضوعی را همراه و بدون انجام کارهای آموزشی مبتنی بر هوش چندگانه بر یادسپاری واژگان زبان آموزان پیشرفته در بافت زبان خارجی ایران بررسی کرده و تفاوت های احتمالی را در میان زبان آموزان فردی با هوش های مختلف مورد بررسی دقیق قرار داده است. با این هدف، یک نمونه همگن هدفمند 80 نفری از 118 زبان آموز پیشرفته در موسسه زبان انگلیسی در شهر تبریز، ایران انتخاب شد و به طور تصادفی در چهار گروه قرار گرفتند. گروههای تحقیق اول و دوم به ترتیب کارهای آموزشی سازگار و ناسازگار با هوش غالب خود دریافت کردند. سومین گروه تحقیق روی تمرینات کتاب درسی تمرکز کرد و گروه کنترل واژگان غیرموضوعی را براساس تمرینات کتاب درسی تمرین کرد. محتوای دوره شامل 60 کلمه پیشرفته بود که به مدت 10 جلسه تدریس شد. پس از سه هفته آزمون یادآوری واژگان داده شد که شرکت کنندگان را ملزم به استفاده از کلمات در پنج پاراگراف بر اساس موضوع داده شده می کرد. نتایج با استفاده ازفراوانی و آزمون آماری تحلیل واریانس نشان داد که گروه تحقیق اول که آموزش واژگان موضوعی با کارهای آموزشی مبتنی بر هوش برترشان قرار گرفته بود عملکرد بهتری از دیگر گروهها داشتند. به طور خاص، فراگیران با هوش غالب کلامی بالاترین وزبان اموزان با هوش غالب درون فردی ضغیف ترین عملکرد را نشان دادند. یافته ها اهمیت در نظر گرفتن تفاوت های فردی را مورد تاکید قرار می دهد و کاربردهای عملی برای معلمان و دست اندرکاران امور آموزشی در بر دارد.

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.