Implementation issues of vehicular ad hoc network applications: selected case studies in Malaysia

This paper looks into the implementation issues on Vehicular Network applications. In order to have better insights two scenarios have been chosen and simulated. A toll booth system shows the issues on a hybrid VANET application while City Taxi system provides the studies of highly mobile applications

Prediction of the antibacterial activity of garlic extract on E. coli, S. aureus and B. subtilis by determining the diameter of the inhibition zones using artificial neural networks

The aim of this study was to devise a model that predicts the inhibition zone diameter using artificial neural networks. The concentration, temperature and the exposure time of our extract were taken as input variables. The neural architecture model 3-13-3 and a learning algorithm Quasi-Newton (BFGS) revealed a positive correlation between the experimental results and those artificially predicted, which were measured according to a mean squared error (RMSE) and an R2 coefficient of E. coli (RMSE = 1.28; R2 = 0,96), S. aureus (RMSE = 1.46; R2 = 0,97) and B. subtilis (RMSE = 1.88; R2 = 0,96) respectively. Based on these results, an external and an internal model validation were attained. A neuronal mathematical equation was created to predict the inhibition diameters for experimental data not included in the basic learning. Consequently, a good correlation was observed between the values predicted by the equation and those obtained experimentally, as demonstrated by the R2 and RMSE values. The results regarding the sensitivity analysis showed that the concentration was the most determinant parameter compared to Temperature and Time variables. Ultimately, the model developed in this study will be used reliably to predict the variation of garlic extract's inhibition diameter

Estimation of Properties of Liquid-Vapor Mixture of Some Refrigerants at High Pressure for Solar-Photovoltaic Refrigeration

Abstract. In this work, a hybrid method based on neural network and particle swarm optimization is applied to literature data to develop and validate a model that can predict with precision vapor-liquid equilibrium data for the binary systems (hexafluoroethane (R116(1)), 1,1,1,2-tetrafluoroethane (R134a) and R1234ze) . ANN was used for modelling the non-linear process. The PSO was used for two purposes: replacing the standard back propagation in training the ANN and optimizing the process. The training and validation strategy has been focused on the use of a validation agreement vector, determined from linear regression analysis of the predicted versus experimental outputs, as an indication of the predictive ability of the neural network model. Statistical analysis of the predictability of the optimized neural network model shows excellent agreement with experimental data (coefficient of correlation equal to 0.998). Furthermore, the comparison in terms of average relative deviation (AARD%) between, the predicted results for the whole temperature and pressure range shows that the ANN-PSO model can predict far better the mixture properties than cubic equations of state

Synaptic and extrasynaptic NMDA receptors are gated by different endogenous coagonists.

N-methyl-D-aspartate receptors (NMDARs) are located in neuronal cell membranes at synaptic and extrasynaptic locations, where they are believed to mediate distinct physiological and pathological processes. Activation of NMDARs requires glutamate and a coagonist whose nature and impact on NMDAR physiology remain elusive. We report that synaptic and extrasynaptic NMDARs are gated by different endogenous coagonists, D-serine and glycine, respectively. The regionalized availability of the coagonists matches the preferential affinity of synaptic NMDARs for D-serine and extrasynaptic NMDARs for glycine. Furthermore, glycine and D-serine inhibit NMDAR surface trafficking in a subunit-dependent manner, which is likely to influence NMDARs subcellular location. Taking advantage of this coagonist segregation, we demonstrate that long-term potentiation and NMDA-induced neurotoxicity rely on synaptic NMDARs only. Conversely, long-term depression requires both synaptic and extrasynaptic receptors. Our observations provide key insights into the operating mode of NMDARs, emphasizing functional distinctions between synaptic and extrasynaptic NMDARs in brain physiology

Removal of antibiotics from aqueous solution by bioadsorbant

In this study, dehydrated wheat bran, which is a natural product grows in the north of Algeria has been used as bioadsorbant for antibiotics removal from aqueous solution. Experimental data showed that the sorption of Tylosin increased with increasing the amount of adsorbent and decreased at high temperature. ∆H◦ and ∆S◦ were calculated from the slope and intercept of plots of ln(kd) versus 1/T, the adsorption process was found to be exothermic and more favourable at low temperature

Prediction of therapeutic potency of tacrine derivatives as BuChE inhibitors from quantitative structure–activity relationship modelling

<p>Numerous studies show that tacrine derivatives exhibit increased inhibitory activity against butyrylcholinesterase (BuChE) and acetylcholinesterase (AChE). However, the screening assays for currently available BuChE inhibitors are expensive, time consuming and dependent on the inhibitory compound. It is therefore desirable to develop alternative methods to facilitate the screening of these derivatives in the early phase of drug discovery. In order to develop robust predictive models, three regression methods were chosen in this study: multiple linear regression (MLR), support vector regression (SVR) and multilayer perceptron network (MLP). Eight relevant descriptors were selected on a dataset of 151 molecules using a method based on genetic algorithms. Internal and external validation strategies play an important role. Also, to check the robustness of the selected models, all available validation strategies were used, and all criteria used to validate these models revealed the superiority of the SVR model. The statistical parameters obtained with the SVR model were RMSE = 0.197, <i>r</i>² = 0.969 and <i>Q</i>² = 0.964 for the training set, and <i>r</i>² = 0.906 and <i>Q</i>² = 0.891 for the test set. Therefore, the model developed in this study provides an excellent prediction of the inhibitory concentration of tacrine derivatives.</p

Application of multilayer perceptron for prediction of the rat acute toxicity of insecticides

International audienceWith the growing number of insecticides that can potentially contaminate the environment, the determination of their acute mammalian toxicity is of prime importance in risk assessment. Chemoinformatics presents an alternative to animal testing because laboratory tests are costly in time and money and actively opposed by animal rights activists. In this work, the Quantitative Structure-Toxicity Relationship (QSTR) model established by using the artificial neural network (ANN) has been used for estimating the acute oral toxicity (LD50) of these insecticides to male rats. The 123 insecticides of the training set and the sixteen insecticides of external testing set have been described by means of using molecular descriptors. The QSTR model was validated internally and externally. A good results (Q2 =0.96 and Q2ext =0.95) were obtained. The prediction results are in good agreement with the experimental values of LD50. © 2017 The Authors. Published by Elsevier Ltd

New approach of the fouling process modeling in tangential filtration on cake

International audienceThis work developed a new theoretical approach for modeling the membrane cross-flow filtration and described the importance of surface energy in the fouling process. In the case of zeta potential membranes, the cake formation results from surface phenomena caused by various interactions at the molecular and the macroscopic level, and is characterized by the surface energy resulting from the action of both gravitational and mechanical forces. The results of the experimental application of the new model revealed that the surface phenomena and the surface forces are the major factors in the fouling process. The new model had undergone various statistical tests to determine its performances and for its comparison with existing models used in wastewater or in drinking water treatment. The considered statistical parameters were the absolute relative error, squared correlation coefficient, the error probability of the experimental points, the distribution function and the correlation coefficient. In this study, two important parameters namely the fouling power Ψ and the overall surface energy γ have been developed; they constituted the contribution of this study in the understanding of the mechanism of membrane fouling. The obtained results showed the impact of the surface interactions, especially at particle wall of the filtering membrane. The level forming the deposit fouling demonstrated that the fouling process from zeta potential membrane is relatively intrinsic from the quantity of surface energy, the characteristic of membrane and the conditions of filtration. © 2017 Desalination Publications. All rights reserved

Prediction of thermal conductivity of liquid and vapor refrigerants for pure and their binary, ternary mixtures using artificial neural network

International audienceThe determination of thermophysical properties of hydrofluorocarbons (HFCS) is very important, especially the thermal conductivity. The present work investigated the potential of an artificial neural network (ANN) model to correlate the thermal conductivity of (HFCS) at (169.87-533.02) K, (0.047-68.201) MPa, and (0.0089-0.1984) W/(m·K) temperature, pressure, and thermal conductivity ranges, respectively, of 11 systems from 3 different categories including five pure systems (R32, R125, R134a, R152a, R143a), four binary mixtures systems (R32 + R125, R32 + R134a, R125 + R134a, R125 + R143a), and two ternary mixtures systems (R32 + R125 + R134a, R125 + R134a + R143a). Each one received 1817, 794 and 616 data points, respectively. The application of this model for these 3227 data points of liquid and vapor at several temperatures and pressures allowed to train, validate and test the model. This study showed that ANN models represent a good alternative to estimate the thermal conductivity of different refrigerant systems with a good accuracy. The squared correlation coefficients of thermal conductivity predicted by ANN were R2 = 0.998 with an acceptable level of accuracy of RMSE = 0.0035 and AAD = 0.002 %. The results of applying the trained neural network model to the test data indicate that the method has a highly significant prediction capability