Thermodynamic state of reinforced interpenetrating polymer networks

The free energies of mixing of two networks in the interpenetrating polymer network based on crosslinked polyurethane and poly(ester acrylate) have been determined by the vapour sorption method. It was established that the constituent networks in the IPN are not miscible. The introduction of fillers of different chemical nature increases the compatibility. The thermodynamic affinity of the fillers to the individual networks and IPN was estimated. It was established that when the free energy of interaction of one or both components of the IPN with the filler is negative, reinforcement leads to the formation of a compatible and equilibrium system. For fillers having no affinity to the polymers, compatibilization is observed, which is connected with slowing down of phase separation in the system in the presence of filler.     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

Evolving Fault-Tolerant Neural Networks

In this paper, genetic algorithm is used to help improve the tolerance of feedforward neural networks against an open fault. The proposed method does not explicitly add any redundancy to the network, nor does it modify the training algorithm. Experiments show that it may profit the fault tolerance as well as the generalisation ability of neural networks.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Influence of polymer modulus on the percolation threshold of latex-based composites

Monodispersed copolymer emulsions with different glass transition temperatures were synthesized to investigate the effect of room temperature polymer matrix modulus on the electrical properties of carbon black (CB) filled segregated network composites. The emulsion with the highest modulus at room temperature produced composites with the lowest percolation threshold. The threshold for a composite made from a copolymer latex containing an equal ratio of butyl acrylate and methyl methacrylate (BA5) is 1.5 vol%, while the percolation threshold for the much lower modulus BA7 (7:3 BA/MMA ratio) is 4.93 vol%. The microstructure of each composite shows significant differences in the level of CB dispersion within the polymer matrix. Higher modulus polymer particles push the CB more efficiently into the interstitial space between them, resulting in a lower percolation threshold. This modulus effect was confirmed by increasing the drying temperature, where the moduli of latexes (BA5, BA5.5, and BA6) were more similar and the percolation thresholds for three composites also become closer to one another.     

Memory of Distributed Computer Systems: Distinctive Features of Their Architectural-Structural Organization and Use

Distinctive features of architectural-structural organization and use of memory networks are considered with the aim of choosing an optimal configuration in designing distributed computer systems.     

永州电力通信交换网集中操作和维护的实现

分析实现永州电力通信交换网集中操作和维护的必要性，介绍了实现程控交换机远程操作和维护的两种方式，总结在实践过程中所得到的经验和教训。     

广播电视发射台技术指标集中自动测试系统

本文介绍了利用计算机技术、网络技术、三遥技术对广播电视发射技术指标进行实时自动测试的系统，以便进一步为安全优质播出提供技术保证。     

Artificial Neural Network Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits

Artificial neural network (ANN) models were developed for the prediction of transient moisture loss (ML) and solid gain (SG) in osmotic dehydration of fruits using process kinetics data from the literature. ANN models for ML and SG were developed based on data over a broad range of operating conditions and ten common processing variables: temperature and concentration of osmotic solution, immersion time, initial water and solid content of the fruit, porosity, surface area, characteristic length, solution-to-fruit mass ratio, and agitation level. The trained models were able to accurately predict the outputs with associated regression coefficients (r) of 0.96 and 0.93, respectively, for ML and SG. These ANN models performed much better than those obtained from linear multivariate regression analysis. The large number of process variables and their wide ranges considered along with their easy implementation in a spreadsheet make them very useful and practical for process design and control.     

Study on phase behavior–impact strength relationship of unsaturated polyester/polyurethane hybrid polymer network

The phase behavior of a hybrid polymer network (HPN) composed of poly[(propylene glycol maleate)-co-(propylene glycol phthalate)] crosslinked with styrene and polyester–urethane crosslinked with methylene-bis-ortho-chloroaniline was examined. The correlation between phase separation and impact strength of the HPNs is discussed. The composition of HPNs has an effect on their properties.