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.     

Fully Automated Three-Dimensional Column-Switching SPE–FIA–HPLC System for the Characterization of Lipids by a Single Injection: Part I. Instrumental Design and Chemometric Approach to Assess the Effect of Experimental Settings on the Response of ELSD

This article presents the first application of fully automated three‐dimensional (3D) column‐switching SPE–FIA–HPLC system for the characterization of lipids by a single injection. The whole system was designed and set up by modifying Agilent 1200 Series HPLC system in our laboratory. By using this system, a complete separation profile of the oil samples was achieved in a very short time period by using single injections. This approach was applied on vegetable oils which contains a large number of relatively high‐class lipid components, such as TG, FFA, sterols, tocopherols, DG, ester and MG. In this part of the study, we focused on the optimization of evaporative light scattering detector (ELSD) by using an experimental design and RSM. Three experimental parameters were chosen as an independent variables which are the flow rate of mobile phase, nebulization temperature and evaporation temperature. A multivariate five level experimental design was used to establish a quadratic model as a functional relationship between the response values and independent variables. The optimal values of parameters were found to be a flow rate of 1.25 mL min^?1, nebulization temperature of 80 °C, and evaporation temperature of 40 °C. Regression analysis with an R² values indicated as a satisfactory correlation between the experimental and predicted values. ANOVA test results were also illustrate that the models can be successfully used to predict the optimum parameters of ELSD. Thus, the proposed system is suitable for a large number of applications including research and development of new quality control and characterization methods for vegetable oils.     

Private predictions on hidden Markov models

Hidden Markov models (HMMs) are widely used in practice to make predictions. They are becoming increasingly popular models as part of prediction systems in finance, marketing, bio-informatics, speech recognition, signal processing, and so on. However, traditional HMMs do not allow people and model owners to generate predictions without disclosing their private information to each other. To address the increasing needs for privacy, this work identifies and studies the private prediction problem; it is demonstrated with the following scenario: Bob has a private HMM, while Alice has a private input; and she wants to use Bob’s model to make a prediction based on her input. However, Alice does not want to disclose her private input to Bob, while Bob wants to prevent Alice from deriving information about his model. How can Alice and Bob perform HMMs-based predictions without violating their privacy? We propose privacy-preserving protocols to produce predictions on HMMs without greatly exposing Bob’s and Alice’s privacy. We then analyze our schemes in terms of accuracy, privacy, and performance. Since they are conflicting goals, due to privacy concerns, it is expected that accuracy or performance might degrade. However, our schemes make it possible for Bob and Alice to produce the same predictions efficiently while preserving their privacy.     

High‐performance filaments by melt spinning low viscosity nylon 6 using horizontal isothermal bath process

Several techniques and treatments have been developed for the production of high‐performance nylon‐6 fibers. The inherent problems of low productivity, high production cost, and high energy consumption, complexity of chemical reaction, mass transfer, and waste recovery systems make most of them inappropriate for industrial application. Horizontal isothermal bath (hIB) is an alternative ecofriendly simple treatment that can be used during melt spinning process for production of technical textile fibers. The efficacy of hIB in improving the mechanical properties of multifilament nylon‐6 yarn is studied in this research. The results showed that such treatment can increase the molecular orientation of the amorphous and crystalline functions up to 0.54 and 0.983, respectively, and raised both the amorphous isotropy and fiber birefringence by 67 and 45%, respectively. Hot drawing of the yarn at a very low draw ratio of 1.38, increased the tenacity and modulus up to 10 and 43.9 g/den, respectively, and decreased the elongation to 27%. POLYM. ENG. SCI., 55:2457–2464, 2015. © 2015 Society of Plastics Engineers     

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.