Facile growth of novel morphology correlated Ag/Co-doped ZnO nanowire/flake-like composites for superior photoelectrochemical water splitting activity

In this paper, novel morphology correlation between silver nanowires (AgNWs) and cobalt (Co)-doped ZnO (Co-ZnO) flake-like thin films (nanowire/flake-like) has been proposed for enhanced photoelectrochemical (PEC) water splitting activity. Here in, high-quality AgNWs/Co-ZnO heterostructures enabled superior visible light water splitting activity compared to the pure ZnO and AgNWs/ZnO. To address the strategic effect of AgNWs coupling and transition metal (Co-2?at%) doping into the ZnO host lattice, we have carried out the X-ray diffraction, field emission scanning microscopy, X-ray photoelectron spectroscopy, UV–Vis transmittance, water contact angle and PEC analyses. In this way, PEC water splitting activity was mainly examined by linear sweep voltammetry (I-V), amperometric I-t and photoconversion efficiency (η) studies. The experimental results provide clear evidence of morphology correlation between AgNWs and Co-ZnO flake-like structures for strong visible light absorption. Specifically, AgNWs/Co-ZnO composites exhibited significant enhancement in the photocurrent density (7.0?×?10^?4 A/cm²) than AgNWs/ZnO (3.2?×?10^?4 A/cm²) and pure ZnO (1.5?×?10^?6 A/cm²). As a result, detailed AgNWs/Co-ZnO geometry has great potential for photoconversion efficiency (0.73%). In a word, the merits of controllable AgNWs/Co-ZnO heterostructure are proposed to improve the visible light harvesting and charge carrier generation for energy conversion devices.     

Alpha-tocopherol as a modulator of smooth muscle cell proliferation

The effects of alpha-tocopherol and beta-tocopherol have been studied in rat and human aortic smooth muscle cells. Alpha-tocopherol, but not beta-tocopherol, inhibited smooth muscle cell proliferation and protein kinase C in a dose-dependent manner, at concentrations ranging from 10 to 50 microM. Beta-tocopherol added simultaneously with alpha-tocopherol prevented both proliferation and protein kinase C inhibition. Protein kinase C inhibition was cell cycle-dependent and it was prevented by okadaic acid, a protein phosphatase inhibitor. Protein kinase C activity measured from aortas of cholesterol-fed rabbits was also inhibited by alpha-tocopherol. By using protein kinase C (PKC) isoform-specific inhibitors and immunoprecipitation reactions it was found that PKC-alpha was selectively inhibited by alpha-tocopherol. Further, an activation of protein phosphatase 2A by alpha-tocopherol was found, which caused PKC-alpha dephosphorylation and inhibition. Ultimately, this cascade of events at the level of cell signal transduction leads to the inhibition of smooth muscle cell proliferation.     

Multiwalled Nanotubes: Their Formation by Irradiating Graphite with High Doses of Electrons

We report the production of carbon nanotubes by high dose of electron irradiation. The irradiation was performed with a 2 MeV Van de Graaff accelerator, while the irradiation conditions were the following: voltage 1.3 MeV, current 5 µA, dose rate 25 kGy/min, and total dosage 1000 kGy. The samples were analyzed in a high-resolution transmission electron microscope. The main features observed on the samples, were huge nanotubes of several nanometer long and few nanometer wide, which are capped at one end. It is good to point out, that at this level of irradiation, we were not able to find either onion-like or particle structures throughout the material, as it is usual in similar hexagonal structures. This behavior could be attributed to the level of irradiation used to create the nanotubes under investigation.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

A case of split notochord syndrome: presence of dorsal enteric diverticulum adjacent to the dorsal enteric fistula

Like umblical enteric remnants (eg, umblical sinus and omphalomesenteric fistula), enteric remnants can be seen on the dorsal aspect of the body (dorsal enteric sinus, dorsal enteric fistula IDEF], dorsal enteric diverticulum) in conjunction with complete cleft of the vertebral column. Complete cleft of the vertebral column associated with gastrointestinal tract and central nervous system anomalies is known as "split notochord syndrome" (SNS). The authors present an unreported variant of SNS having dorsal enteric diverticulum adjacent to the DEF. The patient died 17 days after surgical repair.     

Derivation of empirical equations for neutronic performance in a thorium fusion breeder with various coolants using regression analysis

In this paper, regression analyses (RA) are presented for the neutronic calculation of ThO₂ mixed ²⁴⁴CmO₂ fuel with different neutronic parameters for various coolants, natural lithium, Li₂₀Sn₈₀ and Flinabe, respectively. The tritium breeding ratio (TBR), energy multiplication factor (M), total fission rate (Σ_f) and ²³²Th(n, γ) reaction is computed by XSDRNPM. In addition, this numerical results are estimated by RA depends on neutronic parameters and the empirical equations for neutronic performance are acquired. The results obtained by using XSDRNPM and the results of the RA, obtained empirical equations, are compared. The empirical equations indicate that RA can successfully be used for the prediction of the neutronic performance parameters in the hybrid reactor with a high degree of accuracy. In addition, correlation matrix is calculated to determined statistical relationships between variables TBR, M, Σ_f, and ²³²Th(n, γ).     

Variational sensitivity analysis and design optimization

Variational method (VM) is employed to derive the co-state equations, boundary (transversality) conditions, and functional sensitivity derivatives. The converged solutions of the state equations together with the steady state solution of the co-state equations are integrated along the domain boundary to uniquely determine the functional sensitivity derivatives with respect to the design function. The application of the variational method to aerodynamic shape optimization problems is demonstrated on internal flow problems at supersonic Mach number range of 1.5. Optimization results for flows with and without shock phenomena are presented. The study shows that while maintaining the accuracy of aerodynamical objective function and constraint within the reasonable range for engineering prediction purposes, variational method provides a substantial gain in computational efficiency, i.e., computer time and memory, when compared with the finite difference computations.     

A set of new Chebyshev kernel functions for support vector machine pattern classification

In this study, we introduce a set of new kernel functions derived from the generalized Chebyshev polynomials. The proposed generalized Chebyshev polynomials allow us to derive different kernel functions. By using these polynomial functions, we generalize recently introduced Chebyshev kernel function for vector inputs and, as a result, we obtain a robust set of kernel functions for Support Vector Machine (SVM) classification. Thus in this study, besides clarifying how to apply the Chebyshev kernel functions on vector inputs, we also increase the generalization capability of the previously proposed Chebyshev kernels and show how to derive new kernel functions by using the generalized Chebyshev polynomials. The proposed set of kernel functions provides competitive performance when compared to all other common kernel functions on average for the simulation datasets. The results indicate that they can be used as a good alternative to other common kernel functions for SVM classification in order to obtain better accuracy. Moreover, test results show that the generalized Chebyshev kernel approaches to the minimum support vector number for classification in general.     

A combination of Genetic Algorithm,Particle Swarm Optimization and Neural Network for palmprint recognition

In this study, a new approach to the palmprint recognition phase is presented. 2D Gabor filters are used for feature extraction of palmprints. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector. Genetic Algorithm-based feature selection is used to select the best feature subset from the palmprint feature set. An Artificial Neural Network (ANN) based on hybrid algorithm combining Particle Swarm Optimization (PSO) algorithm with back-propagation algorithms has been applied to the selected feature vectors for recognition of the persons. Network architecture and connection weights of ANN are evolved by a PSO method, and then, the appropriate network architecture and connection weights are fed into ANN. Recognition rate equal to 96% is obtained by using conjugate gradient descent algorithm.