Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Search results for: Kalthoum Riahi

3 Effect of Changing Iron Content and Excitation Frequency on Magnetic Particle Imaging Signal: A Comparative Study of Synomag® Nanoparticles

Authors: Kalthoum Riahi, Max T. Rietberg, Javier Perez y Perez, Corné Dijkstra, Bennie ten Haken, Lejla Alic

Abstract:

Magnetic nanoparticles (MNPs) are widely used to facilitate magnetic particle imaging (MPI) which has the potential to become the leading diagnostic instrument for biomedical imaging. This comparative study assesses the effects of changing iron content and excitation frequency on point-spread function (PSF) representing the effect of magnetization reversal. PSF is quantified by features of interest for MPI: i.e., drive field amplitude and full-width-at-half-maximum (FWHM). A superparamagnetic quantifier (SPaQ) is used to assess differential magnetic susceptibility of two commercially available MNPs: Synomag®-D50 and Synomag®-D70. For both MNPs, the signal output depends on increase in drive field frequency and amount of iron-oxide, which might be hampering the sensitivity of MPI systems that perform on higher frequencies. Nevertheless, there is a clear potential of Synomag®-D for a stable MPI resolution, especially in case of 70 nm version, that is independent of either drive field frequency or amount of iron-oxide.

Keywords: Magnetic nanoparticles, MNPs, Differential magnetic susceptibility, DMS, Magnetic particle imaging, MPI, magnetic relaxation, Synomag®-D.

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2 Removal of Chromium from Aqueous Solution using Synthesized Polyaniline in Acetonitrile

Authors: Majid Riahi Samani, Seyed Mehdi Borghei

Abstract:

Absorptive characteristics of polyaniline synthesized in mixture of water and acetonitrile in 50/50 volume ratio was studied. Synthesized polyaniline in powder shape is used as an adsorbent to remove toxic hexavalent chromium from aqueous solutions. Experiments were conducted in batch mode with different variables such as agitation time, solution pH and initial concentration of hexavalent chromium. Removal mechanism is the combination of surface adsorption and reduction. The equilibrium time for removal of Cr(T) and Cr(VI) was about 2 and 10 minutes respectively. The optimum pH for total chromium removal occurred at pH 7 and maximum hexavalent chromium removal took place under acidic condition at pH 3. Investigating the isothermal characteristics showed that the equilibrium adsorption data fitted both Freundlich-s and Langmuir-s isotherms. The maximum adsorption of chromium was calculated 36.1 mg/g for polyaniline

Keywords: Polyaniline, Chromium, acetonitrile, Adsorption

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1 Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networks

Authors: B. Golchin, N. Riahi

Abstract:

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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