Comparison of mechanical properties of heat‐polymerized acrylic resin with silver nanoparticles added at different concentrations and sizes

The aim of this study was to investigate the effect of silver nanoparticles (AgNPs) incorporation on the flexural strength (FS) of poly(methyl methacrylate) (PMMA). PMMA specimens (65 mm × 10 mm × 3.3 mm for flexural test, 50 mm × 6 mm × 4 mm for impact test) containing different sizes (40, 50, 60 nm) and concentrations (0.05%, 0.2%) of AgNPs were prepared, along with a control group with no AgNP. Impact strength (IS) and FS of all specimens were measured, and one‐way ANOVA and Tukey–Kramer post hoc multiple comparisons tests were used to identify any statistical differences between groups. The addition of AgNPs has no effect on IS of PMMA. The incorporation of AgNPs affected the FS of PMMA depending on the concentration of nanoparticles. The addition of large‐sized nanoparticles to PMMA increases its FS. Accordingly, adding AgNP with proper concentrations to PMMA may enhance the mechanical properties of denture bases used in clinical practice. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45807.     

Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems

Water Resources Management - Reservoirs’ optimal operation is a critical issue in the management of surface water resources. In the present study, after combining the whale optimization...     

Task sensitivity in EEG biometric recognition

This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who performed four different motor movement/imagery tasks while their data were recorded. Training and test of the system was performed using a number of experimental protocols to establish if training with one type of task and tested with another would significantly affect the recognition performance. Also, experiments were conducted to evaluate the performance when a mixture of data from different tasks was used for training. The results suggest that performance is not significantly affected when there is a mismatch between training and test tasks. Furthermore, as the amount of data used for training is increased using a combination of data from several tasks, the performance can be improved. These results indicate that a more flexible approach may be incorporated in data collection for EEG-based biometric systems which could facilitate their deployment and improved performance.     

Improving image inpainting quality by a new SVD-based decomposition

Multimedia Tools and Applications - In this paper, we present a new algorithm for image inpainting using structure and texture information. Our image decomposition to texture and structure is...     

Ensemble-based clustering of large probabilistic graphs using neighborhood and distance metric learning

Graphs are commonly used to express the communication of various data. Faced with uncertain data, we have probabilistic graphs. As a fundamental problem of such graphs, clustering has many applications in analyzing uncertain data. In this paper, we propose a novel method based on ensemble clustering for large probabilistic graphs. To generate ensemble clusters, we develop a set of probable possible worlds of the initial probabilistic graph. Then, we present a probabilistic co-association matrix as a consensus function to integrate base clustering results. It relies on co-occurrences of node pairs based on the probability of the corresponding common cluster graphs. Also, we apply two improvements in the steps before and after of ensembles generation. In the before step, we append neighborhood information based on node features to the initial graph to achieve a more accurate estimation of the probability between the nodes. In the after step, we use supervised metric learning-based Mahalanobis distance to automatically learn a metric from ensemble clusters. It aims to gain crucial features of the base clustering results. We evaluate our work using five real-world datasets and three clustering evaluation metrics, namely the Dunn index, Davies–Bouldin index, and Silhouette coefficient. The results show the impressive performance of clustering large probabilistic graphs.

     

ZnO nanoparticles and polyaniline blend as an active layer for bulk heterojunction solar cell applications

In this work, a facile and straightforward procedure was introduced to prepare a blend as an active layer for hybrid solar cell applications. The active layer consisting of a blend of ZnO nanoparticles (NPs) and polyaniline (PANI) dispersions was deposited by spin coating on ITO covered glasses. The current density–voltage characteristics were studied under AM1.5G standard illumination, without any encapsulation process. Also, the samples were studied using UV–Vis spectroscopy, energy dispersive X-ray spectroscopy (EDS) and field emission-scanning electron microscopy. The investigation is limited just to the active layer, so the cells were fabricated without any interlayer. The effect of various volume ratios of ZnO–NPs:PANI solutions, thickness and the annealing temperature of the active layer on the open circuit voltage and the short circuit current density of the cells were investigated. Moreover, the blending time of ZnO–NPs:PANI dispersions as a significant factor for achieving the optimum results were studied.     

Optimization of energy management and conversion in the water systems based on evolutionary algorithms

Neural Computing and Applications - In this article, an application of weed optimization algorithm (WOA) for reservoir operation was proposed. In addition, genetic algorithm (GA) and particle swarm...     

Sustained drug release using supramolecular hydrogels composed of cyclodextrin inclusion complexes with PCL/PEG multiple block copolymers

Methadone hydrochloride is a narcotic drug used to relieve chronic pain and control withdrawal symptoms in people undergoing detoxification for opiate addiction, but poses some limits. To reduce the limits and increase the drug’s effectiveness, supramolecular hydrogels composed of α-CD (α-cyclodextrin) and PCL/PEG (polycaprolactone/polyethylene glycol) copolymers, which gained attention due to their advantages, were chosen in this study as controlled-release formulations. PCL/PEG/PCL and PCL/PEG copolymers were synthesized by a microwave-assisted method and then supramolecular hydrogels were prepared by mixing the solutions of copolymers/drug and α-CD to make a methadone hydrochloride sustained-release system. Effects of the drug, copolymer, and α-CD concentrations and copolymer structure on gelation time were also investigated. The systems had rheological properties that depended on copolymer construction and component concentrations. The microwave-assisted method provides an accelerated synthesis of the copolymers with yield of 57 % and low level of impurities. The supramolecular hydrogels demonstrated shear thinning and thixotropic behavior and regained their networks quickly after passing through a fine needle. Due to the porous construction of supramolecular hydrogels, they swell in the presence of fluids and absorb large quantities of water, thereby making the system comprehensively biocompatible. In this study, supramolecular hydrogels composed of α-CD with tri- and di-block copolymers were investigated and found to have rheological properties that depended on copolymer construction and component concentrations. Changing one parameter alone like copolymer or α-CD concentration or the length of blocks could not significantly affect on the drug release, but combination of these factors was efficient. Results of the present study indicated that supramolecular hydrogels composed of α-CD and PCL/PEG copolymers are appropriate drug delivery systems that can release methadone hydrochloride in a sustained manner.     

Multibiometrics for face recognition

Fusion is a popular practice to combine multiple sources of biometric information to achieve systems with greater performance and flexibility. In this paper various approaches to fusion within a multibiometrics context are considered and an application to the fusion of 2D and 3D face information is discussed. An optimal method for fusing the accept/reject decisions of individual biometric sources by means of simple logical rules is presented. Experimental results on the FRGC 2D and 3D face data show that the proposed technique performs effectively without the need for score normalization.