درخت حوزه‌های تخصصی

Foreign investment contracts are one of the major ways to absorb the technology in the oil and gas industry. Studies show that governments and investing companies prefer to gain more profit, control economic resources and political influence by investing financially and technically in developing countries unless buying foreign technology and intellectual property rights directly. On the other hand, for developing countries, the sum of foreign capital and technology within a contract is a great opportunity for advancement, provided that the choice of technology transfer is made with due regard to the country's needs, requirements and economic future. In this article, after examining the relationship between investment and technology transfer and its various methods in the oil and gas industry, we will analyze the constraints and barriers of technology transfer through foreign investment in developing countries (including Iranian law) and provide a solution. Keywords: Foreign Investment, Technology Transfer, Oil & Gas Industry, Mutual Trade, Intellectual Property Rights.

The liberalization of natural gas markets and the emergence of gas hubs in recent decades have shifted the natural gas trade from the regional to the global trade. The growth and maturity of these hubs have weakened the previously established relationship between the natural gas price and the prices of crude oil and petroleum products. Therefore, predicting the price of gas as a strategic commodity has become more important for different countries. Using the neural network method, this paper attempts to provide a model for the monthly prediction of natural gas price. Based on the time series data from 2012 to April 2019 as neural network input, this model predicts the prices in five hubs and natural gas exchange centers in Europe. Based on the R2 performance evaluation index of 98% in the neural network model fitted based on the aforementioned data series, the neural network model has an acceptable performance in predicting the natural gas price. The results of this study show that using the artificial neural network (ANN) method, the gas prices in the European gas hubs, which are located in European Country, can be predicted with a high accuracy.

The practice of ‘writing to learn’ has been propounded as fast-tracking the dynamic process of noticing problems in L2 writing. However, a marked melioration in learners’ attempted output requires a form of corrective feedback, among which modeling has proven to bear vigorous input enhancement effects. The present study attempted to inspect what EFL learners notice throughout their own output and exposure to model texts and how this noticing acts upon their short-term and long-term writing performance. In a repeated measure quasi-experimental design, the performance of 43 Iranian EFL learners on narrative writing tasks was collected upon pre-test, post-test, and delayed post-test. The participants indicated their noticing of linguistic problems through note-taking during the writing task followed by the exposure to two model texts and two revisions of the original writing, immediately, and two months later. The type of linguistic features noticed was studied through qualitative analysis. Paired-samples t-tests were conducted to compare students' accuracy performance before and after noticing. The results indicated that learners’ grammatical accuracy was improved by noticing. As to the nature of incorporated features, learners' noticing played a prompting role in the effectiveness of models since learners retained those lexical and grammatical features compatible with their own noticing. It is argued that the participants' engagement in output activity has increased their awareness of the required linguistic features, facilitating their noticing of those features through exposure to positive feedback. The findings provide further evidence for the constructive role of output in language learning.