四烷基锡中性载体推动亚硝酸根离子迁移穿透大块液膜

通过大块液膜体系研究了四烷基锡的碳链长度、载体浓度.NO_2~-浓度及抗衡离子等因素对NO_2~_穿透含四烷基锡的液膜的传质速率的影响,讨论了它们与四烷基锡为载体的离子电极性能间的关系。NO_2~-通过四烷基锡为载体的液膜的迁移规律符合离子对传输模式。     

中性载体阳离子电极膜电化学的计算机数字模拟

对中性载体阳离子电极膜3种可能的传输模式进行了数字模拟,研究了淌度、浓度、配合物稳定常数以及阴离子亲脂性对电极电位响应的影响。模拟结果对于廓清这类电极响应机理,解释有关实验现象及指导新电极设计提供了有用的信息。     

A neuro-fuzzy system for X-ray spectra interpretation

A decision scheme for the interpretation of spectra from wavelength dispersive X-ray fluorescence spectrometry is described that encompasses elements from three areas of artificial intelligence: fuzzy logic, rule based expert systems and neural net technology.After transforming the recorded spectra to line spectra by appropriate background correction a reasoning scheme is applied that takes into account not only the observed spectra, but also the recording conditions and prior spectroscopic information regarding the relative emission probabilities and the usefulness of the different lines for the purpose of element identification. The latter is done on the basis of a previously described scheme to compute conditional a posteriori Bayes probabilities for a mean matrix. These different pieces of information are then assembled into a battery of fuzzy rules. The importance of the rules as well as the importance of the X-ray lines is determined in a training process, similar to the one in a feedforward back-propagation network.To further stabilize the results this network is pruned in a second training cycle. This, however, had little effect on the quality of interpretation.The advantages of this approach to the interpretation of X-ray spectra over older ones are numerous: the system adapts itself to better interpret spectra that are of greater importance to a laboratory as these are better represented in the training set; the fuzzy logic is capable of working with incomplete and uncertain knowledge, and the neural network results based on these fuzzy rules is readily interpretable by the X-ray spectroscopist as every rule can be expressed also in natural language as in any classical rule based system.On leave from Silesian University, Katowice, Poland     

Irreversible deformation of macromolecular networks

Based upon a thermodynamical approach, the generalized Onsager type of relaxation of van der Waals networks is presented. By linearly and identically coupling the set hidden variables to the network, the memory function of the system can be related to the equilibrium strain-energy function. The relaxation behavior of real networks on stretching can quantitatively be described by means of a distribution of relaxation times known from small strain experiments. Some new and interesting conclusions are discussed as to how the macroscopically non-linear visco-elastic response might be interpreted.     

Phenomenological and neural-network modeling of cephalosporin C production bioprocess

Cephalosporin C production process withCephalosporium acremonium ATCC 48272 in synthetic medium was investigated and the experimental results allowed the development of a mathematical model describing the process behavior. The model was able to explain fairly well the diauxic phenomenon, higher growth rate during the glucose-consumption phase, and the production occurring only in the sucrose-consumption phase. Moreover, the process was simulated utilizing the neural-networks technique. Two feed-forward neural-networks with one hidden layer were employed. Both models, phenomenological and neural-networks based, satisfactorily describe the bioprocess. The difficulties in determining kinetic parameters are avoided when neural networks are utilized.     

EQUILIBRIUM SWELLING AND MUTUAL ENTANGLEMENTS IN POLY(VINYL ACETATE)/POLY(METHYL ACRYLATE) IPNS

Based on Thiele-Cohen model, a swelling equation was derived by introducing the interaction parameters of a three-component system of polymer Ⅰ-polymer Ⅱ-solvent, which is a reasonable representation of the swelling behavior of general homogeneous IPNs. The degrees of equilibrium swelling of polyvinyl acetate/polymethyl actylate (PVAc/PMA) IPNs were compared between the theoretical and experimental values. The obvious effects of mutual entanglements were observed.     

ANN原子参数法预报合金相晶型及晶格常数

运用人工神经网络方法，以二元合金组成元素的电子结构为基本特征，对二元合金相晶型作了区分，进一步对其晶格常数作了预报，结果令人满意。     

若干中性配体对Mo—Fe—S簇合物自兜的影响

通过电子吸收光谱的变化研究了一些含N和含P中性配体对MoS_4~(2-)-nFeCl_2-DMF体系形成立方烷型Mo-Fe-S簇合物的可能影响。     

Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.