A theoretical study on different cluster configurations of the 9Be nucleus by using a simple cluster model

In this study,a comprehensive investigation on different cluster configurations of the ~9Be nucleus is performed with a simple cluster approach.With this goal,the elastic scattering angular distributions of ~9Be by ~(27)A1,~(28)Si,~(64)Zn,~(144)Sm,~(208)Pb,and ~(209)Bi target nuclei are reanalyzed for α + α + n,d + ~7Li,~3H + ~6Li,~3He + ~6He and n + ~8Be cluster configurations of the ~9Be projectile within the framework of the optical model.The theoretical results are compared with each other as well as the experimental data.The results provide an opportunity for a test of different cluster configurations in explaining the elastic scattering of~9Be nucleus.     

Gold‐supported activated NiZn coatings: hydrogen evolution and corrosion studies

Gold‐supported Raney‐type NiZn coatings were prepared on a thin Ni film‐modified copper substrate (Cu/Ni/NiZn‐Au). The hydrogen evolution activity, time stability and corrosion behaviour of the electrode was investigated in 1 M KOH solution by electrochemical, microscopic and spectroscopic technique. It was found that Au‐modified activated coatings exhibits good hydrogen evolution activity, electrochemical and physical stability as well as corrosion resistance. The enhanced hydrogen evolution activity of the Au‐modified electrode was related to the larger available surface area and/or a possible synergistic effect between the metals. Copyright © 2017 John Wiley & Sons, Ltd.     

Novel thermal barrier coatings that are resistant to high-temperature attack by glassy deposits

Airborne sand particles that deposit on thermal barrier coatings (TBCs) in gas-turbine engines melt and form calcium–magnesium–aluminosilicate (CMAS) glass, which attacks the TBCs. A new approach for mitigating CMAS attack on TBCs is presented, where up to 20 mol.% Al₂O₃ and 5 mol.% TiO₂ in the form of a solid solution is incorporated into Y₂O₃-stabilized ZrO₂ (YSZ) TBCs. The fabrication of such TBCs with engineered chemistries is made possible by the solution-precursor plasma spray (SPPS) process, which is uniquely suited for depositing coatings of metastable ceramics with extended solid-solubilities. Here, the TBC serves as a reservoir of Al and Ti solutes, which are incorporated into the molten CMAS glass that is in contact with the TBC. This results in the crystallization of the CMAS glass and the attendant arrest of the penetrating CMAS front. This approach could also be used to mitigate attack by other types of foreign deposits (salt, ash, and contaminants) on TBCs.     

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.     

Investigation of water gas-shift activity of Pt–MOx–CeO2/Al2O3 (M = K,Ni, Co) using modular artificial neural networks

The water gas shift activity of promoted Pt–CeO₂/Al₂O₃ catalysts were investigated in this work. The catalysts were prepared by incipient to wetness impregnation and tested using a microflow reaction system. It was found that K has beneficial effects under product-containing feed compositions while Co and Ni promoters worsen catalyst performance. The reaction temperature and feed H₂O/CO ratio positively affect the catalytic activity, whereas CO₂ and H₂ addition to the feed decreases CO conversion, as expected. The experimental results were also modeled using modular neural networks, at which the catalyst preparation and operational (reaction) variables were used together in the same network because they are interacting but processed differently because they are dissimilar in their form (i.e. categorical versus continuous) and their effects on catalytic activity. It was concluded that the effects of catalyst preparation and operational variables and their relative importance could be comprehended more accurately by using this approach, which may be also employed in other similar systems.     

Properties of pumice aggregate concretes at elevated temperatures and comparison with ANN models

The mechanical properties and thermal conductivity of concretes including pumice aggregate (PA) exposed to elevated temperature were analyzed by thermal conductivity, compressive strength, flexure strength, dynamic elasticity modulus (DEM) and dry unit weight tests. PA concrete specimens were cast by replacing a varying part of the normal aggregate (0–2 mm) with the PA. All concrete samples were prepared and cured at 23 ± 10C lime saturated water for 28 days. Compressive strength of concretes including PA decreased that reductions were 14, 19, 25 and 34% for 25, 50, 75 and 100% PA, respectively. The maximum thermal conductivity of 1.9382 W/mK was observed with the control samples containing normal aggregate. The tests were carried out by subjecting the samples to a temperature of 0, 100, 200, 300, 400 500, 600 and 700 °C for 3 h, then cooling by air cooling or in water method. The results indicated that all concretes exposed to a temperature of 500 and 700 °C occurred a significant decrease in thermal conductivity, compressive strength, flexure strength and DEM. An artificial neural network (ANN) approach was used to model the thermal and mechanical properties of PA concretes. The predicted values of the ANN were in accordance with the experimental data. The results indicate that the model can predict the concrete properties after elevated temperatures with adequate accuracy. Copyright © 2016 John Wiley & Sons, Ltd.     

The stability of hydrogen evolution activity and corrosion behavior of NiCu coatings with long-term electrolysis in alkaline solution

In this study, NiCu composite coating was electrochemically deposited on a copper electrode (Cu/NiCu) and tested for hydrogen evolution reaction (HER) in 1 M KOH solution for long-term electrolysis with the help of cathodic current–potential curves and electrochemical impedance spectroscopy (EIS) techniques. The bulk and surface composition of the coating was determined using atomic absorption spectroscopy (AAS) and energy dispersive X-ray (EDX) analysis. The surface morphology was investigated by scanning electron microscopy (SEM). The effect of electrolysis on the corrosion behavior of the Cu/NiCu electrode was also reported. It was found that the NiCu coating had a compact and porous structure with good time stability. The HER activity of the coating was stable over 120 h electrolysis and the HER mechanism was not modified during the operation. The corrosion tests showed that the corrosion resistance of the Cu/NiCu electrode changed when a cathodic current was applied to the electrolysis system.     

Detecting stock-price manipulation in an emerging market: The case of Turkey

This paper aims to develop methods that are capable of detecting manipulation in the Istanbul Stock Exchange. We take the difference between manipulated stock’s and index’s average daily return, average daily change in trading volume and average daily volatility and used these statistics as explanatory variables. The data in post-manipulation and pre-manipulation periods are used as non-manipulated instances while the data in the manipulation period are used as manipulated instances. Test performance of classification accuracy, sensitivity and specificity statistics for Artificial Neural Networks (ANN) and Support Vector Machine (SVM) are compared with the results of discriminant analysis and logistics regression (logit). We found that the data mining techniques (ANN and SVM) are better suited to detect stock-price manipulation than multivariate statistical techniques (discriminant analysis, logistics regression) as the performances of the data mining techniques in terms of total classification accuracy and sensitivity statistics are better than those of multivariate techniques. We also found that unit change in difference between average daily return of manipulated stock and the index has the largest effect while unit change in difference between average daily change in trading volume of manipulated stock and index has the least effect on multivariate classifiers’ decision functions.