The lifetime of CFC substitutes studied by a network trained with chaotic mapping modified genetic algorithm and DFT calculations

The hydrohaloalkanes have attracted much attention as potential substitutes of chlorofluorocarbons (CFCs) that deplete the ozone layer and lead to great high global warming. Having a short atmospheric lifetime is very important for the potential substitutes that may also induce ozone depletion and yield high global warming gases to be put in use. Quantitative structure–activity relationship (QSAR) studies were presented for their lifetimes aided by the quantum chemistry parameters including net charges, Mulliken overlaps, E _HOMO and E _LUMO based on the density functional theory (DFT) at B3PW91 level, and the C-H bond dissociation energy based on AM1 calculations. Outstanding features of the logistic mapping, a simple chaotic system, especially the inherent ability to search the space of interest exhaustively have been utilized. The chaotic mapping aided genetic algorithm artificial neural network training scheme (CGANN) showed better performance than the conventional genetic algorithm ANN training when the structure of the data set was not favorable. The lifetimes of HFCs and HCs appeared to be greatly dependent on their energies of the highest occupied molecular orbitals. The perference of the RMSRE comparing to RMSE as objective function of ANN training was better for the samples of interest with relatively short lifetimes. C₂H₆ and C₃H₈ as potential green substitutes of CFCs present relatively short lifetimes.     

Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO-LiPF6-EC-CNT)

In this study, the ionic conductivity of a nanocomposite polymer electrolyte system (PEO-LiPF₆-EC-CNT), which has been produced using solution cast technique, is obtained using artificial neural networks approach. Several results have been recorded from experiments in preparation for the training and testing of the network. In the experiments, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF₆), ethylene carbonate (EC) and carbon nanotubes (CNT) are mixed at various ratios to obtain the highest ionic conductivity. The effects of chemical composition and temperature on the ionic conductivity of the polymer electrolyte system are investigated. Electrical tests reveal that the ionic conductivity of the polymer electrolyte system varies with different chemical compositions and temperatures. In neural networks training, different chemical compositions and temperatures are used as inputs and the ionic conductivities of the resultant polymer electrolytes are used as outputs. The experimental data is used to check the system’s accuracy following the training process. The neural network is found to be successful for the prediction of ionic conductivity of nanocomposite polymer electrolyte system.     

Social networks’ unnoticed influence on body image in Spanish university students

The influence of advertising on social networks and its effect on young people’s self-image has been studied. This study first explored the frequency of published videos reposted on social networks using different Application Programming Interfaces as search engines. The study also implemented self-valuation surveys assessed by experts and distributed among mental health professionals who assessed the influence of these social networks. Finally, the survey was designed regarding social networks and body assessment and distributed among Córdoba university students (N = 328). The majority of health professionals concluded that there is an elevated influence of social networks on eating disorders, assessing advertising as a risk factor with a value of 4.60 out of 5. Young people’s perceptions of their body image indicate that women’s degree of satisfaction is highly correlated with the frequency of their connections to social networks, with the body image those women present and with the effect of advertising. Conversely, this high correlation and influence does not exist in men.     

人工神经网络概述

近年来,随着人工神经网络在信息、自动化、医学、经济等领域的广泛应用和杰出表现,使得人工神经网络又开始得到广泛关注和重新重视。基于需求,本文对人工神经网络做了简单概述。     

Coordination Polymers. 9. Chelate Polymers Derived from Bisphenolic Complexes and Propylenediamine

Chelate polymers derived from bis(2,4-dihydroxybenzaldehyde)propyl-enediimine M and bis(2,4-dihydroxyacetophenone)propylenediimine M (M = Fe²⁺, Co²⁺, Ni²⁺, Cu²⁺, Zn²⁺) with aromatic acid chlorides were prepared by interfacial polycondensation. Also, chelate polysiloxanes were obtained from the same monomers and α,ω-dichloropolydimethyl-siloxane. The spectral, thermal, magnetic, and electrical properties of the polychelates were studied.     

Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network

An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol–water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back‐propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol–water partition coefficients logP, acid dissociation constant pK_a, water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters.     

Automatic Electronic Tongue for On-Line Detection and Quantification of Organophosphorus and Carbamate Pesticides Using Enzymatic Screen Printed Biosensors

This work presents an automatic system, based on an electronic tongue, for resolution of mixtures of three pesticides. Inhibition detections were performed during the steady state of biosensors response. Three biosensors were built using two enzymes, electric eel (EE), genetically-modified Drosophila melanogaster (B131), and electric eel co-immobilized with drosophila melanogaster (BH). Calibrations curves for paraoxon, dichorlvos, and carbofuran were performed in the ranges 0.4–50.4 µM, 0.01–1.01 µM, 0.01–0.41 µM with LOD of 3.91 × 10^?8, 6.30 × 10^?11, and 5.84 × 10^?10, respectively. An artificial neural network (ANN) was used to model the combined response of three pesticides. A set of 19 mixtures were prepared in order to train the artificial neural network, the modeling was validated with a set of 6 spiked samples of river water. The error and recovery yields were found in consistent with expected values.     

CATALASE MIMIC WITH Fe (II) METALLOMICELLE

The effect of Fe (II) metallomicelle as a model of catalase, which was formed by adding surfactants (CTAB, SDS, LSS, Brij35) in Fe (II) -trien complex of molar ratios 1: 500 on the decomposition of hydrogen peroxide was investigated at 20°C and 30°C in pH 10 using KI-color and UV Spectrophotometry. A kinetic model for metallomicellar catalysis was proposed. The association constant of the ternary complex K and the rate constant of the decomposition of hydrogen peroxide k3 were obtained. The results indicate that the metallomicelles making up of Fe (II) metal complex and cationic or nonionic surfactants have obvious catalysis on the decomposition of hydrogen peroxide, but the metallomicelles making up of Fe (II) metal complex and anionic or zwitterionic surfactants have inhibition on this reaction.