A study of macromolecular diffusion through native porcine mucus.

A diffusion chamber technique based on time-lag analysis for the estimation of effective diffusion coefficients of radiolabelled macromolecules of varying molecular weights through native mucus gel is reported. For all solutes studied, a reduction in effective diffusion coefficients was observed with a retardation of solute flux in both aqueous and mucus layers. Over the molecular weight range of solutes investigated (126-186,000 Daltons), a consistent effect of molecular weight was evident with regard to the retarding effect of mucus. No apparent or absolute molecular weight cut-off for macromolecular transfer was exhibited. However, at high molecular weights (greater than 30,000 Daltons) the retardation was greatly enhanced. The results confirm that mucus can be regarded as a gel with finite pores, but that it does not constitute an absolute barrier to even high molecular weight solutes.     

脉冲电磁场杀菌效应试验研究

利用自行研制的直流脉冲发生装置产生的脉冲电磁场分别对生活污水和纯种大肠埃希氏菌液进行电磁处理 ,研究其对细菌的杀菌效果及机理 .实验表明电磁脉冲对生活污水及纯种菌液中的细菌都有明显的致死作用 .实验结果表明 ,停留时间、水流速度、线圈绕组、输出功率、脉冲频率对脉冲磁场的杀菌效果具有影响作用 ,在停留时间 1 2h ,输出功率 4 6 8W ,脉冲频率 6 5kHz ,水流速度 0 .3m s时 ,生活污水的细菌总数从 3.9× 1 0 7个 mL下降到 1 .5× 1 0 2 个 mL ,存活率达 1 0 - 5;大肠杆菌数从 2 .4× 1 0 6 个 mL下降到 3.6× 1 0 2 个 mL ,存活率为 1 0 - 4.在此条件下 ,四种质量浓度的纯种大肠埃希氏菌液经电磁处理后 ,其细菌的存活率均在 1 0 - 3～ 1 0 - 5.细菌细胞的扫描电镜照片表明电磁作用使细胞表面产生凹陷 ,孔洞 ,细胞质溶出现象 ,从而证实了感应电流杀菌作用机制     

A latent class probit model for analyzing pick any/N data

A latent class probit model is developed in which it is assumed that the binary data of a particular subject follow a finite mixture of multivariate Bermoulli distributions. An EM algorithm for fitting the model is described and a Monte Carlo procedure for testing the number of latent classes that is required for adequately describing the data is discussed. In the final section, an application of the latent class probit model to some intended purchase data for residential telecommunication devices is reported. Geert De Soete is supported as “Bevoegdverklaard Navorser” of the Belgian “Nationaal Fonds voor Wetenschappelijk Onderzoek.”     

Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks

Abstract: After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks isintroduced, an intelligent PID controller is adopted to correct the errors including identified model errors and accumulatederrors produced in the recursive process. Characterized by predictive control, this method can achieve a good controlaccuracy and has good robustness. A simulation study shows that this control algorithm is very effective.     

Dynamic Characteristics of Inter-Satellite Links in LEO Networks

To establish an efficient inter-satellite link (ISL) in an LEO network, the effect of geometric characteristics of ISL on the ISLs and the devices on the LEO satellite should be examined. Because of the continuous movement of the LEO satellite, the time-varying behaviours of the ISL's geometric charactersistics continuously change with the changes of the satellite's position on the orbit. These dynamic geometric characteristics of the ISLs are important for ISL's performance analyzing and the design of the devices on the LEO satellite. This paper describes dynamic geometric characteristics of ISL, analyzes the impact of these regulations on the tracking system of the satellite's antenna and the power adjusting system of the satellite's transmitter, with the Iridium system as an example.     

Radar Imaging Based on Iterative Algorithms

It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.     

Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-