Islamic Azad University

About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Adsorption. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Bioavailability of Glucosinolates and Their Breakdown Products: Impact of Processing.

Abstract: Glucosinolates are a large group of plant secondary metabolites with nutritional effects, and are mainly found in cruciferous plants. After ingestion, glucosinolates could be partially absorbed in their intact form through the gastrointestinal mucosa. However, the largest fraction is metabolized in the gut lumen. When cruciferous are consumed without processing, myrosinase enzyme present in these plants hydrolyzes the glucosinolates in the proximal part of the gastrointestinal tract to various metabolites, such as isothiocyanates, nitriles, oxazolidine-2-thiones, and indole-3-carbinols. When cruciferous are cooked before consumption, myrosinase is inactivated and glucosinolates transit to the colon where they are hydrolyzed by the intestinal microbiota. Numerous factors, such as storage time, temperature, and atmosphere packaging, along with inactivation processes of myrosinase are influencing the bioavailability of glucosinolates and their breakdown products. This review paper summarizes the assimilation, absorption, and elimination of these molecules, as well as the impact of processing on their bioavailability.

Flood flow forecasting using ANN, ANFIS and regression models

Abstract: Flood prediction is an important for the design, planning and management of water resources systems. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR) and multiple nonlinear regression (MNLR) for forecasting maximum daily flow at the outlet of the Khosrow Shirin watershed, located in the Fars Province of Iran. Precipitation data from four meteorological stations were used to develop a multilayer perceptron topology model. Input vectors for simulations included the original precipitation data, an area-weighted average precipitation and antecedent flows with one- and two-day time lags. Performances of the models were evaluated with the RMSE and the R 2. The results showed that the area-weighted precipitation as an input to ANNs and MNLR and the spatially distributed precipitation input to ANFIS and MLR lead to more accurate predictions (e.g., in ANNs up to 2.0 m3 s−1 reduction in RMSE). Overall, the MNLR was shown to be superior (R 2 = 0.81 and RMSE = 0.145 m3 s−1) to ANNs, ANFIS and MLR for prediction of maximum daily flow. Furthermore, models including antecedent flow with one- and two-day time lags significantly improve flow prediction. We conclude that nonlinear regression can be applied as a simple method for predicting the maximum daily flow.