Evolution of the mosaic structure in InGaN layer grown on a thick GaN template and sapphire substrate

The In_xGa_1?xN epitaxial layers, with indium (x) concentration changes between 0.16 and 1.00 (InN), were grown on GaN template/(0001) Al₂O₃ substrate by metal organic chemical vapour deposition. The indium content (x), lattice parameters and strain values in the InGaN layers were calculated from the reciprocal lattice mapping around symmetric (0002) and asymmetric (10–15) reflection of the GaN and InGaN layers. The characteristics of mosaic structures, such as lateral and vertical coherence lengths, tilt and twist angle and heterogeneous strain and dislocation densities (edge and screw dislocations) of the InGaN epilayers and GaN template layers were investigated by using high-resolution X-ray diffraction (HR-XRD) measurements. With a combination of Williamson–Hall (W-H) measurements and the fitting of twist angles, it was found that the indium content in the InGaN epilayers did not strongly effect the mosaic structures’ parameters, lateral and vertical coherence lengths, tilt and twist angle, or heterogeneous strain of the InGaN epilayers.     

Performance study of a wireless mobile ad hoc network with orientation‐dependent internode communication scheme

A Petri‐net‐based simulation model of a wireless mobile ad hoc network is developed and studied. The model covers all the fundamental aspects of behaviour of such a network and uses a novel scheme of orientation‐dependent (or sector‐dependent) internode communication, with random states of links. The proposed scheme enables representation of reliability aspects of wireless communication, such as fading effects, interferences, presence of obstacles and weather conditions in a general and rather easy way. The simulation model was implemented in terms of a class of extended Petri nets to explicitly represent parallelism of events and processes in the WLAN as a distributed system. In the simulation, the behaviour of four fundamental performance metrics — packet delivery ratio, average number of hops, relative network traffic and end‐to‐end delay — were investigated with varying distance of transmission and different combinations of model parameters. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks

In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters that are protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight). A total of 79 flour samples of different wheat varieties grown in different regions of Turkey were chemically analyzed, and the results of both NIR spectrum (400–2,498 nm) and chemical analysis were used to train/test the network by applying various ANN architectures. Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and water absorption in particular gave a very good accuracy with coefficient of determination (R ²) of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively. The results indicate that NIR combined with the ANN can successfully be used to predict the quality parameters of wheat flour.     

RF plasma modified W5O14 and MoS2 hybrid nanostructures and photovoltaic properties

Modification of W₅O₁₄ and MoS₂ nanostructures was carried out using 3,4-ethylenedioxythiophene monomer in a capacitively coupled, RF rotating plasma reactor. Raman spectroscopy and X-ray diffraction (XRD) measurements were used for structural characterization. The surface morphologies of nanomaterials were investigated using transmission electron microscopy. Polymer coated (W₅O₁₄/PEDOT, MoS₂/PEDOT) and untreated (W₅O₁₄, MoS₂) nanostructures were used as the counter electrode for dye-sensitized solar cells. Photovoltaic performances of W₅O₁₄/PEDOT and MoS₂/PEDOT hybrid nanostructures were higher than those of W₅O₁₄ and MoS₂ inorganic nanostructures. Our results indicate that plasma polymer coated W₅O₁₄ and MoS₂ nanostructures of the device for cathode increase both its fill factor and its energy conversion efficiency.     

Big to Small: Ultrafine Mo2C Particles Derived from Giant Polyoxomolybdate Clusters for Hydrogen Evolution Reaction

Due to its electronic structure, similar to platinum, molybdenum carbides (Mo₂C) hold great promise as a cost‐effective catalyst platform. However, the realization of high‐performance Mo₂C catalysts is still limited because controlling their particle size and catalytic activity is challenging with current synthesis methods. Here, the synthesis of ultrafine β‐Mo₂C nanoparticles with narrow size distribution (2.5 ± 0.7 nm) and high mass loading (up to 27.5 wt%) on graphene substrate using a giant Mo‐based polyoxomolybdate cluster, Mo₁₃₂ ((NH₄)₄₂[Mo₁₃₂O₃₇₂(CH₃COO)₃₀(H₂O)₇₂]·10CH₃COONH₄·300H₂O) is demonstrated. Moreover, a nitrogen‐containing polymeric binder (polyethyleneimine) is used to create Mo? N bonds between Mo₂C nanoparticles and nitrogen‐doped graphene layers, which significantly enhance the catalytic activity of Mo₂C for the hydrogen evolution reaction, as is revealed by X‐ray photoelectron spectroscopy and density functional theory calculations. The optimal Mo₂C catalyst shows a large exchange current density of 1.19 mA cm^?2, a high turnover frequency of 0.70 s^?1 as well as excellent durability. The demonstrated new strategy opens up the possibility of developing practical platinum substitutes based on Mo₂C for various catalytic applications.     

Short term forecasting with support vector machines and application to stock price prediction

Forecasting a stock price movement is one of the most difficult problems in finance. The reason is that financial time series are complex, non stationary. Furthermore, it is also very difficult to predict this movement with parametric models. Instead of parametric models, we propose two techniques, which are data driven and non parametric. Based on the idea that excess returns would be possible with publicly available information, we developed two models in order to forecast the short term price movements by using technical indicators. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that support vector regression (SVR) out performs the multi layer perceptron (MLP) networks for a short term prediction in terms of the mean square error. If the risk premium is used as a comparison criterion, then the SVR technique is as good as the MLP method or better.     

A new hybrid recommendation algorithm with privacy

Providing accurate and dependable recommendations efficiently while preserving privacy is essential for e‐commerce sites to recruit new customers and keep the existing ones. Such sites might be able to increase their sales and profits while customers can obtain precise and trustworthy predictions if they use appropriate collaborative filtering (CF) algorithms without deeply jeopardizing users' privacy. We propose a new recommendation algorithm, which is a hybrid‐memory and model‐based algorithm to generate truthful referrals efficiently. Moreover, we use randomization techniques to preserve users' privacy while still offering CF services with decent accuracy. We perform real data‐based trials and analyse our proposed schemes in terms of privacy, accuracy, and performance.