Abstract: The Mihr ü Mâh Mathnawi of the 16th Century Ottoman field, by poet, Jabri, is an allegorical and symbolic work written in accordance with Ptolemy's cosmogony. The work has nested layers of meaning; the first layer consists of a love story between Mihr ü Mâh. The other layer of meaning in work is based on a number of symbols that are open to mystical interpretation. In this study, the concepts of holy-light, holy-number and holy-space, directing the story construction, will be evaluated in the context of mysticism symbolism.

Abstract: In Buddhist scriptures about women, the Suj?ta Sutra and the ?r?m?l? Sutra are unique until now. Previous studies have mainly explored them in the religious and social perspectives. However, the speech acts in the text of the two sutras, which are written in the style of a spoken language, have been overlooked. The purpose of this research is to examine the image of women portrayed through speech acts. The sample of the research focuses on utterances of speech acts in the Suj?ta Sutra and the ?r?m?l? Sutra. A literature review and a descriptive approach are utilized to analyse the sample. The research scope is limited to the following two categories: utterances of speech acts; and contents of Five Good Deeds, Three Evils, Ten Evil Deeds in the Suj?ta Sutra, and Ten Great Vows in the ?r?m?l? Sutra. The findings indicate that illocutionary acts of directives and representatives are present in the dialogues in the Suj?ta Sutra, where Suj?ta as a wife and a woman is treated as inferior to her husband; and commissive acts are contained in the utterances in the ?r?m?l? Sutra, where Queen ?r?m?l? swears to Buddha to become Tathagata, thus achieving equal status with men. These speech acts ultimately connote that women had a passive, obedient social image in ancient times and that Buddhism pursues gender equality in a religious dimension. Thus, this research suggests that speech acts can be a useful tool in examining the image of women in the sutras.

Abstract: These scripts were written in Hebrew and mainly belong to the Karaims, who are a Turkic community with a population that currently is decreasing. The scripts, which are found in various libraries around the world and in private collections, are mostly composed of religious translations and mejumas which have been compiled from folk literature (e.g., stories, folk songs, proverbs, legends, and riddles etc. which have been passed down orally from story-tellers). During a visit to The National Library of Russia and The Institute of Oriental Studies of the Russian Academy of Sciences, analysis was conducted on some of the scripts that are located in the Eastern Manuscripts Section of this library. The purpose of this study is to share the information collected about the scripts during this visit with the science world in the hope that this will be useful for researchers who are interested in this subject.

Abstract: The forecasting concept can be expressed as predicting a variable’s future values using various methods under certain assumptions. In the estimations made using the time series, the value of a variable in the current period and the past period values are used. In the time series analysis, past time values of the variable to be estimated constitute the model’s independent variables. Learning from data, generalizing, working with very large samples, etc. are essential Artificial Neural Networks (ANN) methodology features. ANN methodology, which provides significant advantages thanks to these features, has vast usage possibilities in prediction modeling as in other fields. ANN has become one of the time series estimations’ methods since the late 1980s. This study handles an artificial neural network learning paradigm to estimate the exchange rate for economic targets. This paradigm has not prior conditions such as normal distribution or stationary. Therefore it has been frequently preferred in time-series analyses. The present study predicts the Turkish Lira’s value against the U.S. Dollar by using an ANN methodology. Our study is built upon purchasing power parity and the different researches dealing with exchange rate predictions. Our primary purpose is to minimize the error between the expected output of the network and the output it produces. This study uses an Artificial Neural Network model that builds upon the MLP to estimate the exchange rates. We first define the MLP with one hidden layer for this model, using monthly data from 2000 to 2019. In contrast, the independent variables are interest rates, Gross Domestic Product, and Consumer Price Index data for Turkey and the United States of America. We prefer batch training to train the ANN in SPSS Neural Networks 17.0 software. Sixty percent of the data were used in training, 25 percent in the testing, and 10 percent in the holdout, of the artificial neural networks, respectively. According to research results, the performance of ANN is relatively higher. While the relative error in the training set is 1 percent, the test set’s relative error is below 1 percent (0,007).