Study of Adsorption/Desorption Effect of 2-Mercaptobenzothiazole as Sweet Corrosion Inhibitor on API-5L X60 Steel

Protection of Metals and Physical Chemistry of Surfaces - In this study, the inhibitive performance of 2-mercaptobenzothiazole (2MBT) and 2-aminobenzothiazole (2ABT) were investigated on API-5L X60...     

Improving the Reliability of Software-Defined Networks with Distributed Controllers Through Leader Election Algorithm and Colored Petri-Net

Wireless Personal Communications - Software-Defined Networks (SDNs) are developed to compensate the complicated function of the controlling parts of the given network elements and making the...     

Simulation of smoke from a burning vehicle and pollution levels caused by traffic jam in a road tunnel

Detailed analyses of smoke movement from a burning vehicle in a road tunnel have been carried out for the westbound Melbourne City Link tunnel. The time-averaged equations for velocity, pressure, temperature, and mass fraction of emissions were solved for transient condition using the CFD software FLUENT 6.0. For the analysis, a burning bus was assumed to release an equivalent energy of burning 500 l of diesel in 6 min, with vehicles upstream of the fire at a standstill. On the other hand, the vehicles downstream of the fire had enough time to escape from the tunnel through the exit portal. Due to the action of jet fans, most of the smoke was pushed downstream of the fire. The smoke had also dispersed about 55 m upstream of the fire, putting the passengers in this region at great risk. The emissions released from the vehicles in the jam, with their engines running, also posed a threat to human health. Within 8 min after the fire had started, the mass concentrations of O₂, CO₂ and CO were in the ranges of 0.12–0.15, 0.08–0.11 and 0.0006–0.0014, respectively. Therefore, quick evacuation of the passengers is essential in the event of a fire in the tunnel.     

Synthesis of poly(methyl methacrylate)/silica nanocomposite through emulsion polymerization using dimethylaminoethyl methacrylate

Polymer/Silica nanocomposite latex particles were prepared by emulsion polymerization of methyl methacrylate (MMA) with dimethylaminoethyl methacrylate (DM). The reaction was performed using a nonionic surfactant and in the presence of silica nanoparticles as the seed. The polymer‐coated silica nanoparticles with polymer content and number average particle sizes ranged from 32 to 93 wt % and 114–310 nm, respectively, were obtained depending on reaction conditions. Influences of some synthetic conditions such as MMA, DM, surfactant concentration, and the nature of initiator on the coating of the silica nanoparticles were studied. Electrostatic attraction between anionic surface of silica beads and cationic amino groups of DM is the main driving force for the formation of the nanocomposites. It was demonstrated that the ratio of DM/MMA is important factor in stability of the system. The particle size, polymer content, efficiency of the coating reaction, and morphology of resulted nanocomposite particles showed a dependence on the amount of the surfactant. Zeta potential measurements confirmed that the DM was located at the surface of the nanocomposites particles. Thermogravimeteric analysis indicated a relationship between the composition of polymer shell and polymer content of the nanocomposites. The nanocomposites were also characterized by FTIR and differential scanning calorimetry techniques. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

Uniform modeling of parameter dependent nonlinear systems

This paper addresses the problem of approximating parameter dependent nonlinear systems in a unified framework. This modeling has been presented for the first time in the form of parameter dependent piecewise affine systems. In this model, the matrices and vectors defining piecewise affine systems are affine functions of parameters. Modeling of the system is done based on distinct spaces of state and parameter, and the operating regions are partitioned into the sections that we call ’multiplied simplices’. It is proven that this method of partitioning leads to less complexity of the approximated model compared with the few existing methods for modeling of parameter dependent nonlinear systems. It is also proven that the approximation is continuous for continuous functions and can be arbitrarily close to the original one. Next, the approximation error is calculated for a special class of parameter dependent nonlinear systems. For this class of systems, by solving an optimization problem, the operating regions can be partitioned into the minimum number of hyper-rectangles such that the modeling error does not exceed a specified value. This modeling method can be the first step towards analyzing the parameter dependent nonlinear systems with a uniform method.     

Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model

Most of the commonly used hydrological models do not account for the actual evapotranspiration (ETa) as a key contributor to water loss in semi-arid/arid regions. In this study, the HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) model was calibrated, modified, and its performance in simulating runoff resulting from short-duration rainfall events was evaluated. The model modifications included integrating spatially distributed ETa, calculated using the surface energy balance system (SEBS), into the model. Evaluating the model’s performance in simulating runoff showed that the default HEC-HMS model underestimated the runoff with root mean squared error (RMSE) of 0.14 m³/s (R²?=?0.92) while incorporating SEBS ETa into the model reduced RMSE to 0.01 m³/s (R²?=?0.99). The integration of HECHMS and SEBS resulted in smaller and more realistic latent heat flux estimates translated into a lower water loss rate and a higher magnitude of runoff simulated by the HECHMS model. The difference between runoff simulations using the default and modified model translated into an average of 95,000 m³ runoff per rainfall event (equal to seasonal water requirement of ten-hectare winter wheat) that could be planned and triggered for agricultural purposes, flood harvesting, and groundwater recharge in the region. The effect of ETa on the simulated runoff volume is expected to be more pronounced during high evaporative demand periods, longer rainfall events, and larger catchments. The outcome of this study signifies the importance of implementing accurate estimates of evapotranspiration into a hydrological model.

     

Application of ANFIS for modeling of layer thickness of chromium carbonitride coating

In the present work, layer thickness of duplex coating made from thermo-reactive deposition and diffusion has been predicted by Adaptive network-based fuzzy inference systems (ANFIS). A duplex surface treatment on five steels has been developed involving nitrocarburizing and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700 °C for 1–30 h. The process formed a thickness up to 9.5 μm of chromium carbonitride coatings on a hardened diffusion zone. A model based on ANFIS for predicting the layer thickness of duplex coating of the specimens has been presented. To build the model, training and testing using experimental results from 84 specimens were conducted. The data used as inputs in ANFIS models are arranged in a format of twelve parameters that cover the chemical composition (C, Mn, Si, Cr, Mo, V, W), the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. According to these input parameters, in the Adaptive network-based fuzzy inference system models, the layer thickness of duplex coating of each specimen was predicted. The training and testing results in ANFIS models have shown a strong potential for predicting the layer thickness of duplex coating.     

Oil thermal annealed nano‐structured indium tin oxide thin films for display applications

Indium tin oxide‐coated thin films (200 nm) are deposited on glass substrates by using R.f. sputtering technique. Here, we investigate the influence of new technique of treatment, which is called as “oil thermal annealing” on the nano‐structured indium tin oxide thin films at fixed temperature (150 °C) which improves adhesion strength, electrical conductivity and optical properties (transmittance) of the films. Oil thermal annealing is used to reduce inherent defects that may be introduced during the prepared thin film and cooling processes. Proposed technique is highly suitable for liquid crystal displays, solar cells and organic light emitting diodes, and many other display‐related applications.