درخت حوزه‌های تخصصی

This study employs the framework of Influence Studies within Comparative Literature to explore the impact of Franz Kafka (1883-1924) on Gholam Hossein Saedi (1936-1985). The primary focus of this comparative study is to assess the quality of influence and elucidation regarding the overarching perspective on loneliness, isolation, identity crisis, and metamorphosis, ultimately aiming to comprehend the similarities and distinctions between Gholam Hossein Saedi's The Cow (1965) and Franz Kafka's The Metamorphosis (1915). The present research, conducted through a descriptive-analytical approach and grounded in the French school of comparative literature, concerned with influence studies, determines that the central theme of Kafka's novel revolves around human destiny, which is altered by societal issues. Both narratives illustrate the transformation of their main characters into non-human forms, emphasizing the alienation and isolation faced by individuals in contemporary society. Kafka examines the absurdity of human existence through Gregor's transformation and his subsequent ostracism by society, while Saedi sheds light on the struggles of marginalized communities through Mashd Hassan's change into a cow. The findings of this study demonstrate that Saedi, influenced by Kafka, has adapted and expanded the notion of metamorphosis in The Cow, thereby enriching the landscape of Persian literature.

In this paper, the European option pricing is done using neural networks in the Black-Scholes-Vasicek market. The general purpose of this research is to compare the accuracy of neural network and Black-Scholes-Vasicek models for the pricing of call options. In the sequel, the finite difference method is applied to find approximate solutions of partial differential equation related to option pricing in the considered market. In the design of the artificial neural network required for this research, the parameters of the Black-Scholes-Vasicek model have been used as network inputs, as well as 720 data from the daily price of stock options available in the Tehran Stock Exchange market (in 1400) as the network output. The approximate solutions obtained in this article, which were carried out by two methods of neural networks and finite differences on the Tehran stock exchange based on the daily price of stock options, are shown that neural networks are more accurate method comparing with finite difference. The comparison of pricing results using neural networks with real prices in the assumed market is presented and shown via diagram, as well. In this paper, the European option pricing is done using neural networks in the Black-Scholes-Vasicek market. The general purpose of this research is to compare the accuracy of neural network and Black-Scholes-Vasicek models for the pricing of call options. In the sequel, the finite difference method is applied to find approximate solutions of partial differential equation related to option pricing in the considered market. In the design of the artificial neural network required for this research, the parameters of the Black-Scholes-Vasicek model have been used as network inputs, as well as 720 data from the daily price of stock options available in the Tehran Stock Exchange market (in 1400) as the network output. The approximate solutions obtained in this article, which were carried out by two methods of neural networks and finite differences on the Tehran stock exchange based on the daily price of stock options, are shown that neural networks are more accurate method comparing with finite difference. The comparison of pricing results using neural networks with real prices in the assumed market is presented and shown via diagram, as well.

This study examines the employment of self-praise in online celebrity culture, particularly the self-presentation strategies adopted by male and female English and Iraqi celebrities on social media. Drawing on the theoretical frameworks of self-presentation, politeness theory, and self-praise, the study examines how male and female celebrities employ self-praise to negotiate their public images, engage with audiences, and enhance their market value. By examining posts on Instagram, Twitter, and Weibo, this study identifies prevalent self-praise strategies, such as explicit self-praise, adapted self-praise, and implicit self-praise, and draws attention to the affordances of platforms like hashtags, character limits, and multimodal content that influence these strategies. Further, this study investigates the degree to which cultural norms, primarily of individualism and collectivism, inform male and female celebrities' practice and perception of self-praise in different cultural settings. The findings suggest that as a global platform for celebrity branding and social validation, self-praise is grounded in the cultural expectations and affordances of given platforms. This study adds to the knowledge of self-presentation in the digital era and offers insights into the intersection of self-praise, gender, celebrity culture, and social media.

Aims: The present study aimed to validate the Persian Emoji Lexicon (PEL) and examine gender and cultural differences in the perception of the affective properties of selected emojis. Method: The study consisted of two phases: emoji selection and an online survey. First, we identified the 109 most frequently used emojis on Twitter. In the second phase, we assessed emotion and valence ratings from a sample of 200 X users (140 females; 27.3 ± 3.5 years). Results: Of the 109 selected emojis, 90 were perceived as having a positive valence. Gender comparisons revealed that women rated significantly higher emotional intensity across all eight emotion dimensions compared to men. We also compared our findings with a similar database validated among Canadians. The inter-rater reliability for emoji valence ratings between Iranian and Canadian participants showed a fair level of agreement (Kappa coefficient = 0.357), while also highlighting significant cultural differences in emotion perception. Conclusion: This database serves as a valuable resource for sentiment analysis of social media texts containing emojis, as well as for practical applications where emojis function as emotional cues to influence behavior (e.g., in marketing and education).