درخت حوزه‌های تخصصی

< p>The present study models the risk of investment in the petrochemical industry considering the impacts of exchange rate (US dollar to Iran''''s Rial) movements using the time series data from November 2008 to March 2019 and ARFIMA-FIGARCH framework. The empirical results prove the existence of the Fractal Market Hypothesis, FMH, and the Long Memory property in both the risk and return of the petrochemical stock index. These findings can be culminated in reaching a reliable and significant model to evaluate the investment risk in the petrochemical industry. In line with this, to analyze the idea whether considering the exchange rate movements matter for assessing the risk management in the petrochemical industry, the effects of exchange rate movements as a crucial source of systematic risk in Iran has been taken into consideration in the process of modelling the risk of investment in that industry. Our results demonstrate that the exchange rate movements have had a direct and significant effect on the investment risk of that industry so that if, on average, one percent change occurs in the exchange rate, the investment risk in this industry changes by 57% in the same direction.

ChatGPT-assisted language instruction is gathering irresistible momentum in today’s fast-moving, AI-governed world of educational enterprises. The current scoping review article sought to analyze and synthesize the existing literature on the complementary use of ChatGPT in second/foreign language (L2/FL) education and pedagogy. Aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA‑ScR), the study adopted a systematic approach to evidential data search and analysis. A total of 28 peer-reviewed articles retrieved from well-established databases, such as Scopus and Web of Science (WoS), were reviewed to map the scope and consequences of the growing body of literature on using ChatGPT as an L2/FL learning/teaching tool. Centering around five predetermined variables, including setting, design, focus area, objective, and concluding remarks, the content or thematic analysis results helped to shape a clear picture of the literature under review. The literature-derived evidential data also referred to the fruits of ChatGPT-assisted instruction under three distinct headings: learning outcomes, pedagogical outcomes, and cognitive-affective impetus for language learning and teaching. The review results also revealed the repercussions of the instruction mainly concerned with ethical and technical concerns. Given the immature but fast-paced nature of the literature on the instructional model, this early attempt, though provided a brief sketch of the research scope and chief outcomes of the model, needs to be complemented with future systematic reviews to fully unlock the potential of language pedagogy well suited to the demands of today’s technology-governed education.

Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.