Kharazmi University

About: Kharazmi University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Membrane & Supply chain. The organization has 3395 authors who have published 5321 publications receiving 45645 citations. The organization is also known as: Tarbiat Moallem University of Tehran & Teacher Training University.

Comparative evaluation of impacts of climate change and droughts on river flow vulnerability in Iran

Abstract: Rivers in arid and semi-arid regions are threatened by droughts and climate change. This study focused on a comparative evaluation of the impacts of climate change and droughts on the vulnerability of river flows in three basins with diverse climates in Iran. The standardized precipitation-evapotranspiration index (SPEI) and precipitation effectiveness variables (PEVs) extracted from the conjunctive precipitation effectiveness index (CPEI) were used to analyze the drought severity. To investigate hydrological droughts in the basins, the normalized difference water index (NDWI) and the streamflow drought index (SDI) were calculated and compared. The effects of droughts were assessed under various representative concentration pathway (RCP) scenarios. Changes in the number of wet days and precipitation depth restricted hydrological droughts, whereas an increasing number of dry days amplified their severity. The projected increases in dry days and precipitation over short durations throughout a year under future climate scenarios would produce changes in drought and flood periods and ultimately impact the frequency and severity of hydrological droughts. Under RCP 4.5, an increase in the frequencies of moderate and severe meteorological/hydrological droughts would further affect the Central Desert Basin. Under RCPs 2.6 and 8.5, the frequencies of severe and extreme droughts would increase, but the drought area would be smaller than that under RCP 4.5, demonstrating less severe drought conditions. Due to the shallow depths of most rivers, SDI was found to be more feasible than NDWI in detecting hydrological droughts.

Geochemistry and geochronology of the volcano-plutonic rocks associated with the Glojeh epithermal gold mineralization, NW Iran

Abstract: Abstract Eocene to Oligocene volcano-plutonic rocks arewidespread throughout NW Iran. The Tarom-Hashtjinmetallogenic province is one of the most promisingepithermal-porphyry ore mineralized districts in NW Iran.The Glojeh gold deposit, located in the center of thisprovince, is a typical high to intermediate sulfidation epithermalsystem, spatially and temporally associated witha granite intrusion and associated high-K calc-alkalineto shoshonitic volcano-plutonic rocks. The intrusive complexesof the Glojeh district are characterized by: SiO2contents of 60.9 to 70.7 wt.%, K2O+Na2O of 7.60 to 8.92wt.%, and K2O/Na2O ratios of 0.9 to 1.8. They are enrichedin light rare earth elements (LREEs), and large ionlithophile elements (LILEs), depleted in high field strengthelements (HFSEs), and have weak negative Eu anomalies(Eu/Eu*= 0.5 to 0.9). 40Ar/39Ar geochronology appliedto biotite and feldspar, separated from two intrusives(Goljin and Varmarziar), and two feldspar aliquotsseparated from hydrothermal veins at North Glojeh andSouth Glojeh, was carried out to constrain magmatic andhydrothermal events. Plagioclase (± sericite), from NorthGlojeh and South Glojeh produced ages (42.20±0.34 Ma,and 42.56±1.47 Ma respectively) that overlap with the ageof the Goljin intrusion (41.87±1.58 Ma).Geochemical data for the volcano-plutonic rocks in theGlojeh district, that have87Sr/86Sr isotopic compositionsthat range from 0.706344 to 0.708331, suggest an origin involvingpartial melting of a depleted mantle source duringNeo-Tethyan subduction.

Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach

Abstract: In this paper, a supervised learning approach is introduced for detecting both damage and deterioration in two building models under ambient and forced vibrations. The coefficients and residuals of autoregressive (AR) time-series models are utilized for extracting features through some statistical indices. Moreover, a novel algorithm called best-uncorrelated features selection (BUFS) is proposed and utilized in order to select the most sensitive and uncorrelated features, which are used as predictors. Accordingly, a common set of predictors capable of detecting both damage and deterioration is established and used in order to form a general pattern of the structural condition. Besides, the BUFS algorithm can also be utilized with other features as well as different types of structures and depicts the most sensitive predictors. The results indicate that the proposed method is capable of detecting damage and deterioration in both models precisely, even in a noisy environment, and the appropriate features are introduced.