مطالب

This research applies an interdisciplinary method of discussion to read George Martin’s A Game of Thrones, an epic fantasy, under the light of Nancy Katherine Hayles’ theories concerning modern laws of physics and literature. The plot of the novel has prominent intricacy with vulnerability and flightiness of its storyline and characters which remains open to abundant literary interpretations. This article uses thermodynamics to refer to a type of physics concerned with temperature and heat, and their link to energy, entropy and the physical qualities of radiation and matter. The conduct of these quantities is ruled by thermodynamic laws that transfer a perceptible description utilizing quantifiable macroscopic physical features. This research traces them within the system of Westeros, a fictitious continent enduring countless tensions and wars, in which entropy is manifested metaphorically. Martin shows how thermodynamics and entropy function and bring chaos, and bases his fantasy on the violation of scientific laws to epitomize the ontology of physics and give meaning to his imaginary world. Regarding this, the researchers show and conclude how Martin interplays physics and literature to decipher the reality of this chaotic world and propose a realistic framework to see the mechanism of universe in fantasy and reality.

The bulk of what we know as violence is myth. Since myths have been told and retold over the years, they have been taken as truth. This paper explores racial myths as representations of verbal violence in Toni Morrison's The Bluest Eye, Sula, and Beloved. Toni Morrison as an African-American identity explorer dwells on the slogans and myths as manifestations of Žižekian symbolic violence. Methodologically, this study uses political discourse analysis, black gendered feminism and psychoanalysis in the light of Slavoj Žižek's commentary on violence to be the approaches of analyzing the drawn data. The fusion of psychoanalytic terms with political ones sheds a fresh light on the concepts of ideology and violence. This paper aims at exploring the mechanisms of the black violence myth that spread the racial beliefs that Africans are more violence prone and have higher pain tolerance than Europeans. The conclusion extracted from this study confirms that the discourse of the myths functions as pseudo-ideologies to normalize the violence against non-European races and stop the African subalterns' resistance in the form of fighting back against the raids of the Europeans.

This study aims to design a local model of social responsibility in digital marketing in small online retail businesses in Iran. The present research is  applied  in terms of purpose and a qualitative research  in terms of data collection method. The statistical population in the qualitative section included 11 experts. The main tools for data collection in the field included semi-structured interviews. The grounded theory approach using MAXQDA software was adopted to analyze qualitative data and present a paradigm model. Based on the results of qualitative analysis, six categories of factors were identified  including causal conditions, underlying factors, strategies, pivotal phenomena, intervening factors and consequences . Targeted marketing was identified as the main category in relation to the context. In relation to causal conditions, two main categories were identified, including moral responsibility and environmental responsibility. In relation to digital marketing as a central phenomenon of research, the indicators of giving customers the right to choose, retaining customers, providing services in accordance with customer demands, establishing appropriate relationships and relationships with customers, and creating value for customers were identified. Commercialization strategies were identified as the main category of strategies and actions. Competitiveness of companies has been identified as the most important intervening factors; and economic development and service quality improvement were identified as the main categories of outcomes.

The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 are used as target variables and 31 technical analysis indicators as feature variables for the three models in the first stage. 70% of the data is regarded as learning data, and the remaining 30% is considered for the second and third stages. In the second phase, 50% of the data is considered for learning the output of the previous stage and 50% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.