A study in Beijing, China of 112 pathologically confirmed epithelial ovarian cancer cases and 224 age-matched community controls enabled evaluation of risk in relation to reproductive, medical, familial, and selected lifestyle factors. An inverse relationship was observed between the number of full-term pregnancies and ovarian cancer risk. Compared to nulliparous women, subjects with one, two, or three full-term pregnancies were at 50%, 70%, or 90% reduced risks, respectively (P for trend less than 0.01). A positive correlation was found between the number of ovulatory years and risk, with a 2.6-fold increased risk for women with 30 or more compared to less than 10 ovulatory years (P for trend less than 0.01). Infertility, as estimated in various ways, was also found to be an important risk factor. When parity was taken into account, age at first pregnancy was not related to ovarian cancer risk. No protective effect was associated with mumps virus infection. In contrast, risk increased significantly as serum mumps virus antibody titres increased (P for trend less than 0.01). An elevated risk was found in women with a history of long-term (greater than 3 months) application of talc-containing dusting powder to the lower abdomen and perineum (Relative risk 3.9, 95% confidence interval: 0.9-10.63). These findings suggest that Chinese women have risk factors similar to those of occidental women.
In this paper, a model identification and adaptive control design is performed on a humanoid robot based Devanit-Hartenberg (D-H) model. We focus on the modelling of the 6 degree-of-freedom (DOF) upper limb using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation of the robot for modelling, the particle swarm optimization (PSO) method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values of the RNE based adaptive control design to achieve improved tracking performance. Simulation studies are carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control system.
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