مطالب

Die Reihe der Religionsstifter beginnt mit Zarathustra, dem Begründer des antiken Zoroastrismus, der bis in die heutige Gegenwart noch gläubige An-hänger kennt, z.B. die Parsen. Der Aufsatz unternimmt den Versuch, Za-rathustra gleichberechtigt in die Reihe der Religionsstifter zu stellen, einige Gemeinsamkeiten mit anderen Religionsstiftern aufzuzeigen, aber auch seine Besonderheiten herauszuarbeiten. Um seine Person und sein Wirken ranken sich viele Legenden. In diesem Artikel soll der wahre Kern seiner Botschaft jenseits aller Mythen herausgearbeitet werden. Zarathustra gilt als der Be-gründer des Monotheismus, der das Judentum beeinflusste und im Christen-tum und Islam modifiziert weiterwirkte. Der Aufsatz ist zugleich eine me-thodische Prüfung, inwieweit Zarathustra als Prophet mit anderen Religions-stiftern verglichen und inwieweit der Zoroastrismus auf dieser Basis zu den Weltreligionen gerechnet werden kann.

This study aimed to investigate the role of managers' information interpretation on cost behavior. The locative domain of this research is the companies listed in the Tehran Stock Exchange during 2014-2020 and through systematic elimination method, 112 companies have been selected as statistical sample. Managers' information interpretation is considered as an independent variable and cost behavior is considered a dependent variable. The current research is applied research, and if the classification of types of re-searches be considered based on the nature and method, the method of the present study is essentially descriptive in terms of the nature, and in terms of the method is considered in correlation researches category. In this study, library method was used to collect data. In the research data section, data was collected through collecting data of sample companies by referring to financial statements, explanatory notes and stock exchange magazine. In order to describe and summarize the data collected, the descriptive and inferential statistics are used. In order to analyze the data, variance heterogeneity pre-test, F Leimer test, Hausman test and Jarque-Bera test and then multivariate regression test were used to confirm and reject the research hypotheses (EVIEWS software). The results showed that the extent of effectiveness of managers’ information interpretation factors, including changes in managers' consensus on profit, changes in public profit information, changes in private profit information, and changes in bias in profit forecasting on cost behavior in potentially competitive conditions are different from de facto competition. The results obtained in this research are consistent with the documents mentioned in the research theoretical framework and financial literature.

Purpose of this research is Predicting Stock Price Crash Risk with a Deep Learning Approach from Artificial Intelligence and Comparing its Efficiency with Classical Predicting Methods. This research is post-event correlation type and practical in terms of purpose. The research data were extracted from the website of the Stock Exchange Organization and Codal website. The risk variable of crashing stock prices was introduced as a predictor. 3200 obser-vations were obtained from 10-year data of 320 companies between 2012 and 2021. In the following, 29 variables were identified as variables that can affect the risk of crashing stock prices. Statistical methods such as unit root test, composite data, Hausman test and variance heterogeneity test were used. Next, the top 10 algorithms in the field of deep learning were selected and used to model the mentioned variables with the CNN method. Python, Eviews and Excel software were used in this research. Examining the performance of different deep learning algorithms shows that the convolutional neural network method performs better compared to other algorithms and can improve the prediction accuracy. Therefore, it is suggested to use this algorithm in reviewing econometric data and especially predicting the risk of crashing stock prices.

This research sought to assess the integrated impact of CLT (Communicative Language Teaching) and PLT (Pragmatic Language Teaching) on language competency, pragmatic awareness, and cultural competence. The study used a mixed-methods methodology, integrating both pre- and post-tests, questionnaires, interviews, and classroom observations, in addition to technological tools to enhance learning experiences in 150 advanced EFL students from five Iranian universities. The results obtained from repeated measures ANOVA and t-tests demonstrated that the integrated use of CLT and PLT approach leads to a considerable enhancement in language proficiency, pragmatic awareness, and cultural competence, when compared to the utilization of either method individually. Qualitative findings demonstrated a notable rise in student self-assurance, enhanced comprehension of different cultures, and heightened levels of involvement. The results highlight the efficacy of combining Communicative Language Teaching (CLT) and Pragmatic Language Teaching (PLT) through the use of technology to improve global language training. The study's findings emphasize the necessity of providing instructor training in both strategies, developing a curriculum that effectively combines communicative and pragmatic features, and doing additional research to understand the long-term effects of integrated language teaching approaches. The research enhances our understanding of how the integration of Communicative Language Teaching (CLT) and Pragmatic Language Teaching (PLT) might optimize language learning consequences and better equip students for real-life communication.