Effectiveness of different surface cleaning methods on the shear bond strength of resin cement to contaminated zirconia: an in vitro study

The purpose of this in vitro study was to evaluate and compare the effectiveness of different surface cleaning methods on the shear bond strength (SBS) of zirconia ceramic surfaces. Seventy polished and cleaned zirconia disk specimens of 8 mm in diameter and 3.4 mm in thickness were immersed in fresh saliva. They were then pressed into a freshly mixed silicone disclosing medium. Six different cleaning methods were applied to the tested groups; they were airborne-particle abraded (AA), covered with a cleaning paste (Ivoclean®) (IV), etched with orthophosphoric acid (PA), immersed in alcohol (AL), rinsed with tap water only (WA), or cleaned with steam (SC). No surface cleaning was done after saliva immersion and silicone disclosing medium contamination to the control group (CC). The specimens were then bonded to an adhesive resin cement using polyethylene tubes. SBS was determined using a universal testing machine at a crosshead speed of 1 mm/min. The specimens were also examined with a scanning electron microscope and a stereomicroscope. Group AA yielded the highest SBS value (7.01 ± 1.4 MPa) among the groups, while Group WA had the lowest SBS value (3.03 ± 0.8 MPa). The SBS values of Group AA (7.01 ± 1.4 MPa) and IV (6.2 ± 1.7 MPa) were also significantly higher than those of the remaining four groups (p < 0.05). Within the limitations of this in vitro study, it was concluded that among the various cleaning methods tested, airborne-particle abrasion and Ivoclean® paste were effective in cleaning the zirconia surface.     

Guanine oxidation signal enhancement in single strand DNA with polyacrylonitrile/polyaniline (PAN/PAni) hybrid nanofibers

Pure polyacrylonitrile (PAN) and polyacrylonitrile/polyaniline (PAN/PAni) hybrid nanofibers (NFs) were produced via electrospinning and used to monitor guanine oxidation in single strand DNA (ssDNA) by electrochemical methods. Two different methodologies were conducted. First, pre‐synthesized PAni was added into electrospinning PAN solution and electrospun into composite PAN/PAni nanofibrous structure on cylindrical pencil graphite (PGE) surface. In the second route, PAN NFs were electrospun on a PGE surfaces and polymerization of PAni was conducted on the surfaces of the as‐spun PAN NFs. NFs were kept at ?18 °C in a refrigerator for several days. ssDNA was immobilized on the prepared NFs and guanine oxidation signals were observed for each system. The results revealed that use of PAN NFs enhanced signal intensity from 0.92 µA (PGE) to 1.04 µA (PAN NFs). Addition of PAni to PAN increased signal intensity to 1.23 µA. When the PAN NF surfaces were coated with PAni, signal enhancement continued to increase up to 4.19 µA for fourth day and decreased again when PAni‐coated NFs were kept at ?18 °C in the refrigerator. Since the prepared system is fast and cheap, it is promising for application in DNA biosensor devices. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45567.     

Performance of acrylic monomer based terpolymer/montmorillonite nanocomposite hydrogels for U(VI) removal from aqueous solutions

Acrylic monomer based terpolymer/montmorillonite nanocomposite hydrogels (NH-MMTs) synthesized using 2-(N,N-dimethylamino)ethyl methacrylate (DMAEMA), 2-acrylamido-2-methlypropane sulfonic acid (AMPS) and 2-hydroxyethyl methacrylate (HEMA) in the aqueous montmorillonite (MMT) suspension were employed as adsorbents for U(VI) removal from aqueous solutions. Adsorption efficiency of the NH-MMTs was strongly enhanced by increasing pH in the range of 3–6. Adsorption capacity of the NHs increased with the MMT weight ratio up to 1% and the complete removal of U(VI) from 1 mmol/L aqueous solutions was achieved by 2 g/L polymer but further increase of MMT up to 6% caused a gradual decrease in adsorption percentage up to 57%. Nearly 98% of U(VI) loaded on the adsorbents could be recovered by 0.1 M HNO₃. Consecutive adsorption/desorption cycles showed that the NH-MMTs are re-usable. Kinetic results were analyzed using Paterson's and Nernst Planck approximation's based on homogeneous solid phase diffusion (HSPD). Experimental data were fitted to equilibrium isotherm models, Langmuir, Freundlich, Dubinin–Radushkevich and Temkin. SEM, and FTIR analysis of bare and U(VI) loaded adsorbents were used to elucidate adsorption mechanisms. The results showed that the NH-MMTs tested in this study are very promising for the recovery of U(VI) from water.     

A new Schiff base: Synthesis,characterization and optimization of metal ions-binding properties

In this study, a new Schiff base (H₄TSTE) was synthesized and characterized by elemental analysis, FT-IR, NMR and MS spectral data. Liquid–liquid extraction process was performed for removal of Cu(II), Mn(II), Ni(II), Pb(II) and Zn(II) from aqueous solutions by means of H₄TSTE. The extractions were investigated depending on the concentration of picric acid, metal ion and H₄TSTE ligand. Response surface methodology (RSM) was first applied to optimize metal ion-binding properties of H₄TSTE. The extraction efficiency was estimated to be >98% for all metals by models. Under the same conditions, the extraction efficiency was experimentally found to be >97% with a relative standard deviation within ±0.10 (N = 4), indicating the suitability of the models.     

Combined neural network model to compute wavelet coefficients

Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients.     

Modified mixture of experts employing eigenvector methods and Lyapunov exponents for analysis of electroencephalogram signals

Abstract: The use of diverse features in detecting variability of electroencephalogram (EEG) signals is presented. The classification accuracies of the modified mixture of experts (MME), which was trained on diverse features, were obtained. Eigenvector methods (Pisarenko, multiple signal classification – MUSIC, and minimum-norm) were selected to generate the power spectral density estimates. The features from the power spectral density estimates and Lyapunov exponents of the EEG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the EEG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on the diverse features achieved high accuracy rates (total classification accuracy of the MME is 98.33%).     

Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes

The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means “cessation of breath” during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. Decision making was performed in two stages: feature extraction by computation of wavelet coefficients and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the three ANFIS classifiers. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on detecting any possible changes in the human EEG activity due to hypopnoea (mild case of cessation of breath) occurrences were drawn through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting changes in the human EEG activity due to hypopnoea episodes.     

Combining eigenvector methods and support vector machines for detecting variability of Doppler ultrasound signals

In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for detecting variabilities of the multiclass Doppler ultrasound signals. The ophthalmic arterial (OA) Doppler signals were recorded from healthy subjects, subjects suffering from OA stenosis, subjects suffering from ocular Behcet disease. The internal carotid arterial (ICA) Doppler signals were recorded from healthy subjects, subjects suffering from ICA stenosis, subjects suffering from ICA occlusion. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, better classification procedures for Doppler ultrasound signals are searched. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the SVMs trained on the extracted features. The research demonstrated that the multiclass SVMs trained on extracted features achieved high accuracy rates.     

Recurrent neural networks employing Lyapunov exponents for analysis of doppler ultrasound signals

The implementation of recurrent neural networks (RNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Decision making was performed in two stages: computation of Lyapunov exponents as representative features of the Doppler ultrasound signals and classification using the RNNs trained on the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the Doppler ultrasound signals and the RNNs trained on these features achieved high classification accuracies.     

The determinants of agglomeration for the manufacturing sector in the Istanbul metropolitan area

This paper examines the determinants of agglomeration by seeking the patterns of urbanization economies and localization economies in the Istanbul metropolitan area (IMA). The research is developed in two steps. The first step is the measurement of concentration levels for the IMA; the Ellison–Glaeser localization index (EGI) is applied to the 22 manufacturing sector (2-digit level) at three different geographical levels. The second step is to determine the structural pattern of agglomeration. By regressing the Ellison–Glaeser localization index values on proxies for urbanization and localization economies, the determinants of agglomeration are demonstrated. The determinants of agglomeration are estimated by 12 different two-stage OLS regressions. While three of these regressions represent the agglomeration factors at each geographical level, the other nine equations represent the agglomeration factors at the industry-specific level. The results suggest that urbanization economies have a strong effect on agglomeration both at the geographical level and industry-specific level. It is noticed that density, market area potential, and labor market potential are the most effective proxies for urbanization economies on agglomeration. The effects of localization economies are consistent with Marshall for labor pooling and manufactured input. However, the results do not provide any evidence that knowledge spillovers have an influence on agglomeration in this case.