Finding Contents or People

A. Sadeghi
Reviewing Activities
 Evaluation of aflatoxin B1 binding by toxin binder and its effect on performance, gastrointestinal health, and blood parameters in broilers (Sponsor: Fartac Company, Iran).
6- Feasibility Study of Synthesis of Long-Chain Alcohols Via Air Oxidation of Linear Long Chain Parafins, Funded by Isfahan LAB Company, Iran
An evolutionary game-theoretic approach for investigating the long-term behavior of the industry sector for purchasing renewable and non-renewable energy: A case study of Iran
Critical metals distribution in the late Triassic-early Jurassic Nasr-Abad bauxite deposit, Irano-Himalayan karst bauxite belt, NW Iran
2015-2016 Charachterisation and Processing of Kaolinite ore of Isfahan Province, Funded by the Organization of Mining and Industry of Isfahan province, Isfahan, Iran (Co-worker).
Investigation of intact rock geomechanical parameters effects on commercial blocks productivity within stone reserves A case history of some quarries in Isfahan Iran
Population genetic structure of four regional populations of the barley pathogen Pyrenophora teres f. maculata in Iran is characterized by high genetic diversity and sexual recombination
Space Partitioning among Two Sympatric Species Sand Cat (Felis margarita) and Rppells Fox (Vulpes rueppellii) in a Desert Landscape in Central Iran
First Laureate on 21stIranian Seminar of Organic Chemistry(21st ISOC), Ilam university, Ilam, Iran, 13-15 March 2014
Using Statistical Models to Estimate Economic Optimum Nitrogen Fertilizer Rate for Wheat, Safflower, and Canola before and after Fertilizer Subsidy Programs in Iran
The potential application of sea cucumber (Holothuria leucospilota) as a bioindicator of heavy metals contamination: a case study from Chabahar Bay, Oman Sea, Iran
The potential application of sea cucumber (Holothuria leucospilota) as a bioindicator of heavy metals contamination: a case study from Chabahar Bay, Oman Sea, Iran
Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran