A Quantitative Structure-activity Relationships Model Based on Hybrid Artificial Intelligence Methods and its Application

Quantitative structure-activity relationship (QSAR) model is adopted to study the relationship between the chemical and physical properties of various substances and the structure. Through QSAR studies, the internal relationship between the invisible structure and the activity can be obtained. In this paper, a novel chaos-enhanced accelerate particle swarm algorithm (CAPSO) is proposed, which is used to molecular descriptors screening and optimization of the weights of back propagation artificial neural network (BP ANN). Then, the QSAR model based on CAPSO and BP ANN is put forward, hereinafter referred to as CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm is a reliable method for screening molecular descriptors and the five molecular descriptors obtained by CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed the CAPSO BP ANN model has a good performance in predicting the pKa values of various compounds, the absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the higher prediction accuracy and correlation. The proposed hybrid intelligent model can be applied in all kinds of engineering design, prediction of physical and chemical properties and intelligent calculation.

Some Advanced Air-Bearing Design Issues for Proximity Recording

In magnetic hard disk drives the minimum spacing between the air-bearing slider and disk has been reduced to under 50 nm, and some drives now employ so-called proximity sliders that are designed to operate at some level of interference between the slider and the peak asperities on the disk. This ultra-low flying condition brings into play some new interface phenomena and accentuates some of the well known ones as well. In this paper, we consider some air-bearing design issues related to proximity recording. First, we examine the effects of shear flow in the bearing, which is usually neglected, and we show that for high-pitch proximity slider designs the effect is not negligible. Next, we note that such low spacing also tends to accelerate particle accumulation at the trailing edges of the slider. In an effort to address this problem, a model is developed for calculating forces on particles in the air bearing. Including this in the CML air bearing design code we show that designs can be created that eject most of the particles from the sides rather than trapping them at the trailing edge. Finally, we investigate the performance of proximity sliders with regard to their sensitivity to altitude changes. We include altitude sensitivity as an objective in the design optimization scheme and demonstrate that it can yield air-bearing design with performance much less sensitive to changes in altitude.