Background: The fundamental problems with the personality disorders diagnostic system in DSM-IV led to the revision of the DSM approach and proposition of a dimensional model for DSM-5. The DSM-5 Personality and personality disorders workgroup developed the personality inventory for DSM-5 (PID-5) to assess the pathological personality traits within this new model. Objectives: The purpose of this study was to investigate the psychometric properties of PID-5 in psychiatric patients. Methods: In a cross-sectional study, the Persian translation of the PID-5 was administered to 400 psychiatric patients admitted to the Roozbeh Hospital. After data collection, the reliability of the inventory was investigated using internal consistency and test-retest methods. In addition, confirmatory factor analysis and convergent validity methods were used to evaluate the validity of the scale. Results: Adequate internal consistency coefficients were obtained for domains and facets. In addition, the test-retest coefficients (up to 0.70) suggested scale stability. Confirmatory factor analysis supported the original five-factor model of the inventory. The convergent validity of the inventory with the TCI-R scale was appropriate. Conclusions: The results of the study supported the psychometric properties of the Persian version of PID-5 in psychiatric populations.

A common strategy for the surface modification of nano TiO 2 and other metal oxide nanoparticles is based on anchor groups using chelating ligands that can carry additional functionalities. This would allow the exploration of further applications of these materials. In the present work, we report the modification of TiO 2 nanoparticles from nano TiO 2 (Degussa P-25) dispersion in distilled water in the presence of 2-aminoethyl dihydrogen phosphate, followed by removing the excess of the capping agent through washing with water. The surface functionalization and the kind of surface interaction were analyzed applying different characterization methods like CHN elemental analysis, thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), attenuated total reflectance (ATR-FTIR), X-ray powder diffraction (XRD) and 1 H, 13 C, 31 P magic angle spinning nuclear magnetic resonance spectroscopy (MAS NMR), confirming the presence of modifying agent on the surface. In the study of modified nano TiO 2 by MAS NMR spectroscopy, a distinct downfield shift for 31 P signal has been seen comparing to the pure cappping agent due to P-O-Ti bond formation. The phosphate groups interact with the surface via quite strong covalent interaction, while according the analyses results, the surface amine groups remained uncoordinated. ]]>

In this study TiO2 was photosensitized by graphene to improving the visible spectral absorption and photoactivity under visible light irradiation. TiO2 was added to different content of graphene oxide and with hydrothermal method and 1, 3 and 5 percentage of graphene-TiO2 composite (TG) was synthesized. The prepared samples were characterized by XRD, Raman, DRS, and SEM.Photoelectrochemical experiments exhibited that the photocurrent response of three percentages of TG nanocomposite was higher than others.

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In this paper, the quality characteristics of melon was estimated by using color parameters and neural networks for three fertilizing stages (no fertilizer, fertilizing 5 and 10 ton/ha). For this purpose, chemical parameters including fructose, glucose and sucrose, and color parameters such as L*, a*, b* were studied. Physical characteristics under study were specific weight, mean firmness and skin. Chemical compositions include Brix, moisture content, titratable acidity, pH and ash. Results showed that the best determination coefficient for approximated equation of Brix is R 2 = 0.96 and the least standard error was found to be SE = 0.52 related to glucose equation. According to the analyses done on the modeling data using neural network in Neuro Solutions software, the most appropriate network for fructose prediction was Feed for Multilayer perceptron with architecture of 4-12-12-1 and R 2 = 0.99, MSE = 0.000324. Therefore, for the estimation of quality characteristics for cantaloupe, neural network can be used successfully.