A model to describe the hysteresis damping characteristic of rubber material was presented.It consists of a parallel spring and damper,whose coefficients change with the vibration amplitude and frequency.In order to a...A model to describe the hysteresis damping characteristic of rubber material was presented.It consists of a parallel spring and damper,whose coefficients change with the vibration amplitude and frequency.In order to acquire these relations,force decomposition was carried out according to some sine vibration measurement data of nonlinear forces changing with the deformation of the rubber material.The nonlinear force is decomposed into a spring force and a damper force,which are represented by the amplitude-and frequency-dependent spring and damper coefficients,respectively.Repeating this step for different measurements gives different coefficients corresponding to different amplitudes and frequencies.Then,the application of a parameter identification method provides the requested approximation functions over amplitude and frequency.Using those formulae,as an example,the dynamic characteristic of a hollow shaft system supported by rubber rings was analyzed and the acceleration response curve in the centroid position was calculated.Comparisons with the sine vibration experiments of the real system show a maximal inaccuracy of 8.5%.Application of this model and procedure can simplify the modeling and analysis of mechanical systems including rubber materials.展开更多
Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian n...Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian networks to develop a decision support system that is based on the modeling of relationships between key signaling proteins and clinical and pathological characteristics of breast tumors and patients. Motivated by the lack of prior information on the parameters of the problem, we use the Implicit inference for the structure and parameter learning. A dataset of 84 Tunisian breast cancer patients was used and new prognosis factors were identified. The system predicts a metastasis risk for different patients by computing a score that is the joint probability of the Bayesian network using parameters estimated on the learning database. Based on the results of the developed system we identified that overexpression of ErbB2, ErbB3, bcl2 as well as of oestrogen and progesterone receptors associated with a low level of ErbB4 was the predominant profile associated with high risk of metastasis.展开更多
基金Project(50675042) supported by the National Natural Science Foundation of China
文摘A model to describe the hysteresis damping characteristic of rubber material was presented.It consists of a parallel spring and damper,whose coefficients change with the vibration amplitude and frequency.In order to acquire these relations,force decomposition was carried out according to some sine vibration measurement data of nonlinear forces changing with the deformation of the rubber material.The nonlinear force is decomposed into a spring force and a damper force,which are represented by the amplitude-and frequency-dependent spring and damper coefficients,respectively.Repeating this step for different measurements gives different coefficients corresponding to different amplitudes and frequencies.Then,the application of a parameter identification method provides the requested approximation functions over amplitude and frequency.Using those formulae,as an example,the dynamic characteristic of a hollow shaft system supported by rubber rings was analyzed and the acceleration response curve in the centroid position was calculated.Comparisons with the sine vibration experiments of the real system show a maximal inaccuracy of 8.5%.Application of this model and procedure can simplify the modeling and analysis of mechanical systems including rubber materials.
文摘Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian networks to develop a decision support system that is based on the modeling of relationships between key signaling proteins and clinical and pathological characteristics of breast tumors and patients. Motivated by the lack of prior information on the parameters of the problem, we use the Implicit inference for the structure and parameter learning. A dataset of 84 Tunisian breast cancer patients was used and new prognosis factors were identified. The system predicts a metastasis risk for different patients by computing a score that is the joint probability of the Bayesian network using parameters estimated on the learning database. Based on the results of the developed system we identified that overexpression of ErbB2, ErbB3, bcl2 as well as of oestrogen and progesterone receptors associated with a low level of ErbB4 was the predominant profile associated with high risk of metastasis.
