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Jalaloddin Davani who has written many books, treatises, and letters, is one of the great scholars of the Shiraz school of thought during the Timurid period. Considering his influential attitude in the dynamics and excellence of Persian mystical literature, it is important to know about his Persian works which often include mysticism, philosophy, and theology. Obviously, before judging his opinions and beliefs, one should investigate the authenticity of his works. Therefore, the present research aims at the critical codicology of Persian works attributed to Davani to determine their authenticity. Reviewing the catalogue of manuscripts in the libraries shows that, in most catalogues, there are many mistakes in the title of Davani's Persian works. These bibliographic mistakes have been repeated in the works of many subsequent authors. To write the paper First, based on the Union Catalogue of Iran Manuscripts (Fankha), the titles of all Persian works attributed to Davani were identified. Then, using his Persian manuscripts and other sources, the authenticity of his Persian works in the catalogue of manuscripts was examined. In this study, through the comparative investigation of Davani's Persian manuscripts in Iran, some of our codicological and bibliographic information about Davani's Persian works have been corrected, especially the title and number of his authentic and non-authentic works. The result of the current critical codicology showed that Davani has written at least twenty original Persian works. In addition, according to the entries of Davani's Persian works in Fankha, at least nine nonoriginal Persian works have been attributed to him. Thus, at least nine entries of Davani's Persian works in Iran seem incorrect. [ABSTRACT FROM AUTHOR]

Simultaneous use of an efficient routing protocol and mobile sink not only prevents rapid sensor energy depletion but also effectively improves the energy balance in wireless sensor networks. In this paper, a new data aggregation method is proposed called "Ant colony-based Routing and Petal-shaped Data Aggregation (ARPDA)", which includes clustering, Cluster Head (CH) selection, intra-cluster routing, determining polling points, and designing the sink node trajectory. In the proposed method, the network is divided by several virtual concentric circles that are equidistant from each other. The intersection point of these circles with the virtual lines passing through the origin of the network determines the probable polling points for the sink node. First, by efficiently clustering the sensor nodes and forming an intra-cluster routing tree based on the ant colony optimization algorithm, the sensor data is aggregated in CHs; then, by selecting the appropriate polling points and designing a petal-shaped path, the sink node periodically collects the aggregated data in CHs based on two linear and circular movements. Considering the sink movement in the CH selection process is another advantage of the proposed algorithm. Numerical results confirm the better performance of the proposed algorithm compared to the EDT algorithm. [ABSTRACT FROM AUTHOR]

In this paper, using a deep learning technique, the sensing characteristic of a refractive index (RI) sensor based on a multimode optical fiber speckle-gram has been extracted. The investigated sensor structure consists of a tapered optical fiber or microfiber (MF) induced on a standard multimode optical fiber (MMF). Due to the presence of evanescent waves around the MF, the guiding status of the end MMF is dependent on the external RI around the taper. Thereby, the propagating modes and resulting interference pattern or speckle-gram become sensitive to the changes in the taper environment. Herein, by analyzing and quantifying the speckle-gram modulation, the RI changes around the taper are characterized. To extract the sensing characteristic and improve its measurement accuracy, deep learning algorithms and specifically the ResNet deep neural network are used in the processing of the output speckle-grams. Using this algorithm, while the tapered fiber is immersed in different refractive index solutions in the range of 1.33 to 1.368, a set of output speckle images is captured for learning and then a number of them are used for measurement, and finally the accuracy of detecting the refractive index of the solutions RI has been achieved up to 95%. [ABSTRACT FROM AUTHOR]