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 & Catalysis. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Application of magnetic nanoparticles in smart enzyme immobilization.

Abstract: Immobilization of enzymes enhances their properties for efficient utilization in industrial processes. Magnetic nanoparticles, due to their high surface area, large surface-to-volume ratio and easy separation under external magnetic fields, are highly valued. Significant progress has been made to develop new catalytic systems that are immobilized onto magnetic nanocarriers. This review provides an overview of recent developments in enzyme immobilization and stabilization protocols using this technology. The current applications of immobilized enzymes based on magnetic nanoparticles are summarized and future growth prospects are discussed. Recommendations are also given for areas of future research.

A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques

Abstract: A new method is developed for recommender systemsThe recommender system is developed based on collaborative filteringScalability and sparsity issues in recommender systems are solvedMovieLens and Yahoo! Webscope R4 datasets are used for method evaluationThe method is effective in solving the sparsity and scalability problems in CF Improving the efficiency of methods has been a big challenge in recommender systems It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques Then, we use ontology to improve the accuracy of recommendations in CF part In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition(SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scalability of the recommendation method We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature The results showed that our method is effective in improving the sparsity and scalability problems in CF

Performance of photon reconstruction and identification with the CMS detector in proton-proton collisions at √s = 8 TeV

Abstract: A description is provided of the performance of the CMS detector for photon reconstruction and identification in proton-proton collisions at a centre-of-mass energy of 8 TeV at the CERN LHC. Details are given on the reconstruction of photons from energy deposits in the electromagnetic calorimeter (ECAL) and the extraction of photon energy estimates. The reconstruction of electron tracks from photons that convert to electrons in the CMS tracker is also described, as is the optimization of the photon energy reconstruction and its accurate modelling in simulation, in the analysis of the Higgs boson decay into two photons. In the barrel section of the ECAL, an energy resolution of about 1% is achieved for unconverted or late-converting photons from H→γγ decays. Different photon identification methods are discussed and their corresponding selection efficiencies in data are compared with those found in simulated events.