Sharif Digital Repository

Starch based scaffolds are considered as promising biomaterials for bone tissue engineering. In this study, a highly porous starch/polyvinyl alcohol (PVA) based nanocomposite scaffold with a gradient pore structure was made by incorporating different bio-additives, including citric acid, cellulose nanofibers, and hydroxyapatite (HA) nanoparticles. The scaffold was prepared by employing unidirectional and cryogenic freeze-casting and subsequently freeze-drying methods. Fourier transform infrared (FTIR) spectroscopy confirmed the cross-linking of starch and PVA molecules through multiple esterification phenomenon in the presence of citric acid as a cross-linking agent. Field emission scanning... 

The age-hardening behavior and microstructure of selected samples of a Ni-Co alloy were studied using hardness testing, optical microscopy, and scanning and transmission electron microscopy. The final microstructure of the material was found to be sensitive to the temperature of the solution treatment. This approach may be used to optimize the microstructure of the alloy for different applications. © 2007 Acta Materialia Inc  

Brain is the most complicated organ of body. It controls the activity of all other organs. Understanding its function and its language could give us a direct communication pathway for connecting with injured motor organ and it could be the core of functional repairing. Neurons are the vertices of a vast network that generates the brain signals. Neuronal recordings capture brain activity signatures. The processing of these signals can help to translate brain's language. Usually it follows three main stages: spike detection and extraction, spike sorting, and intention extraction from the encoded signal. In this work, we introduce an original idea based on Hidden Markov Models (HMM) which helps... 

Increasing the number of car accidents and other cerebral disease cause to progress in using Brain-Compute Interface (BCI) as a common subject for research and treatment. The aim of Brain-Computer Interface system is to establish a new communication system that translates human intentions, reflected by brain signals, into a control signal for an output device such as a computer. To this end, different processes must be done on brain signals and these signals must be classified by suitable methods. There are various methods to classify ECoG signals which are different in features and classifiers. Used features depend on extracted features, feature reduction methods and measures of feature... 

Feature extraction and classification are the most important parts of BCI systems. The new branch of BCI studies focuses on the design of a classifier that is trained to function properly for each individual. This problem is known as Transfer Learning. In between-subject classification, due to the differences in the neural signals' distribution of different individuals, using the common methods of feature extraction for training the classifier, does not lead to high accuracy for the test subject. As a result, in this study, we present a method for extracting features that perform well in between subjects classifications. The data that we used in this study are EEG signals recorded during... 

Classification is the most important part of brain-computer interface (BCI) systems. Because the neural activities of different individuals are not identical, using the ordinary methods of subject-dependent classification, does not lead to high accuracy in betweensubject classification problems. As a result, in this study, we propose a novel method for classification that performs well in between-subject classification. In the proposed method, at first, the subject-dependent classifiers obtained from the train subjects are applied to the test trials to obtain a set of scores and labels for the trials. Using these scores and the real labels of the labeled test trials, linear regression is... 

A pH-responsive drug delivery system based on core shell structure of mesoporous silica nanoparticle (MSN) and chitosan-PEG copolymer was prepared and characterized by Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), scanning electron microscope (SEM), and high-resolution transmission microscope (HR-TEM) techniques. In order to improve compatibility MSN and drug, mesoporous nanosilica was modified by 3-aminopropyl triethoxysilane. The release of erythromycin (a macrolide antibiotic) as a model drug was investigated in two pHs, 7.4 and 5.5  

Migraine is a common type of headache with neurovascular origin. In this paper, a quantitative analysis of spontaneous EEG patterns is used to examine the migraine patients with maximum and minimum pain levels. The analysis is based on alpha band phase synchronization algorithm. The efficiency of extracted features are examined through one-way ANOVA test. we reached the P-value of 0.0001, proving that the EEG patterns are statistically discriminant in maximum and minimum pain levels. We also used a Neural Network based approach in order to classify the EEG patterns, distinguishing between minimum and maximum pain levels. We achieved the total accuracy of 90.9 %