Controlling parameter for wave types of long flexible riser undergoing vortex-induced vibration

Submerged floating tunnel (SFT) is a popular concept of crossing waterways. The failure of the cable may occur due to vortex-induced-vibration (VIV), and the stability of the cable is crucial to the safety of the entire tunnel. Investigation results in recent years show that the vortex-induced vibration of the flexible cables with large aspect ratio reveals some new phenomena, for example, the vortex-induced wave, multi-mode competition, wide band random vibration, which have brought new challenges to the study of vortex-induced vibration of long flexible cables. In this paper, the dimensionless parameter controlling the wave types of dynamic response of slender cables undergoing vortex-induced vibration is investigated by means of dimensional analysis and finite element numerical simulations. Our results indicate that there are three types of response for a slender cable, i.e. standing wave vibration, traveling wave vibration and intermediate state. Based on dimensional analysis the controlling parameter is found to be related to the system damping including fluid damping and structural damping, order number of the locked-in modes and the aspect ratio of cable. Furthermore through numerical simulations and parameter regression, the expression and the critical value of controlling parameter is presented. At last the physical meaning of the parameter is analyzed and discussed.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.