Review: the latest advances in biomedical applications of chitosan hydrogel as a powerful natural structure with eye-catching biological properties

Journal of Materials Science - Chitosan is one of the natural cationic polymers with unique properties such as non-toxicity, biodegradability, biocompatibility, environmentally friendly that has...     

Rate of Methane Hydrate Formation in Presence of Tetra-n-Butylammonium Chloride and Tween 80: Application in Air Conditioning Systems

Theoretical Foundations of Chemical Engineering - Moderate hydrate forming conditions of tetra-n-butylammonium salts have made these compounds as attractive materials to store the cold in air...     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

An accurate analysis of the nonlinear power amplifier effects on SC-FDMA signals

Wireless Networks - Single carrier-frequency division multiple access (SC-FDMA) is a multiple access technique in broadband wireless networks which has been adapted by 3GPP for uplink transmission...     

Polyacrylonitrile/α-Fe2O3 Hybrid Photocatalytic Composite Adsorbents for Enhanced Dye Removal

The potential of a novel α-Fe₂O₃/polyacrylonitrile (PAN) hybrid composite adsorbent to eliminate methylene blue (MB) from aqueous solution was evaluated. PAN was selected as the base composite. The presence of α-Fe₂O₃ as nanophotocatalyst on the surface of PAN introduced an efficient photocatalytic hybrid composite adsorbent for degrading MB. Effects of α-Fe₂O₃ nanopowder loading, pH, temperature, MB initial concentration, solar light, and contact time were investigated. Langmuir, Freundlich, and Temkin isotherms were applied to analyze the adsorption behavior. The Freundlich equation provided the best correlation with experimental data. Pseudo-first-order, pseudo-second-order, and intraparticle models were employed. Thermodynamic studies indicated an endotherm and spontaneous adsorption process in a defined temperature range.     

Copper-alumina nanocomposite coating on copper substrate through solution combustion

The main objective of the present research is to investigate the production of Cu-Al₂O₃ nanocomposite coating on a copper substrate using solution combustion synthesis. Solution combustion synthesis is mainly used to produce nanocomposite powders; however, in this study it is applied to produce nanocomposite coat. For this purpose, both copper and aluminum nitrates (Cu (NO₃)₂·3H₂O and Al (NO₃)₃·9H₂O) are used as oxidizers. Also, urea and graphite are respectively used as fuel to synthesize the Cu-Al₂O₃ nanocomposite and as inhibitor to prevent the oxidation of the synthesized copper. The microstructure and morphology of the nanocomposite coating, which includes 25 wt% alumina as the reinforcing phase, was studied using X-ray diffraction, scanning electron microscopy, and transmission electron microscopy at different fuel/oxidizer ratios ranging from 0.9 to 2. The temperature variation during the process was measured as a function of time using a precise thermocouple. Finally, micro-hardness and wear tests were conducted on the nanocomposite coating. The results verified the formation of Cu-Al₂O₃ nanocomposite coating. Time-temperature curve illustrated that the highest temperature was achieved at the fuel/oxidizer ratio of 1.25. The results of the microhardness and wear resistance test showed that these properties depend heavily on the fuel/oxidizer ratio, with the best condition attained at the ratio of 1.25.