Modelling of steady motion of solid specimens conveyed by travelling wave ultrasonic feeding

On the basis of the research on the morphology of the contact surfaces, a contact model is proposed, which regards the rough contact surfaces as the collections of elastic micro peaks. These micro peaks generate elastic contact force due to elastic deformation and the contact force has relationship to the morphology of the contact surfaces, the motion of the vibrator as well as the materials of the specimen and the vibrator. And using Newton’s second law to the specimen’s motion, the normal dynamical equation for the specimen with the actuation of the ultrasonic vibrator is established. Solving the dynamical equation, the normal kinetic function of specimen and the normal elastic contact force are obtained. Furthermore, the normalized contact time could be analyzed theoretically, which is defined as the ratio of contact time to period. The calculated results indicate that the normalized contact time decreases with the vibrator’s normal amplitude increasing, and increases with the standard deviation of the height of micro peaks on the contact surfaces. Finally, the average tangential velocity of the specimen, conveyed by a travelling wave ultrasonic feeding device, is discussed on the basis of the research on the normalized contact time. The formula of the specimen’s average tangential velocity is derived and it is the function of the normalized contact time, the tangential amplitude, the angular frequency and the phase difference of the tangential and the normal vibrations. Using this formula, the tangential velocity of the specimen is theoretically analyzed; in addition, the theoretical conclusions are compared with the experimental data

1,3-Dimethyl-1H-indole-2-carbonitrile

The title compound, C11H10N2, crystallizes with two mol­ecules in the asymmetric unit, both of which are essentially planar (r.m.s. deviations = 0.014 and 0.016 Å). In the crystal, aromatic π–π stacking inter­actions occur [shortest centroid–centroid separation = 3.5569 (11) Å]

Cortical Actin Turnover during Cytokinesis Requires Myosin II

SummaryThe involvement of myosin II in cytokinesis has been demonstrated with microinjection, genetic, and pharmacological approaches; however, the exact role of myosin II in cell division remains poorly understood. To address this question, we treated dividing normal rat kidney (NRK) cells with blebbistatin, a potent inhibitor of the nonmuscle myosin II ATPase. Blebbistatin caused a strong inhibition of cytokinesis but no detectable effect on the equatorial localization of actin or myosin. However, whereas these filaments dissociated from the equator in control cells during late cytokinesis, they persisted in blebbistatin-treated cells over an extended period of time. The accumulation of equatorial actin was caused by the inhibition of actin filament turnover, as suggested by a 2-fold increase in recovery half-time after fluorescence photobleaching. Local release of blebbistatin at the equator caused localized accumulation of equatorial actin and inhibition of cytokinesis, consistent with the function of myosin II along the furrow. However, treatment of the polar region also caused a high frequency of abnormal cytokinesis, suggesting that myosin II may play a second, global role. Our observations indicate that myosin II ATPase is not required for the assembly of equatorial cortex during cytokinesis but is essential for its subsequent turnover and remodeling

Robust Optimization and Sensitivity Analysis with Multi-Objective Genetic Algorithms: Single- and Multi-Disciplinary Applications

Uncertainty is inevitable in engineering design optimization and can significantly degrade the performance of an optimized design solution and/or even change feasibility by making a feasible solution infeasible. The problem with uncertainty can be exacerbated in multi-disciplinary optimization whereby the models for several disciplines are coupled and the propagation of uncertainty has to be accounted for within and across disciplines. It is important to determine which ranges of parameter uncertainty are most important or how to best allocate investments to partially or fully reduce uncertainty under a limited budget. To address these issues, this dissertation concentrates on a new robust optimization approach and a new sensitivity analysis approach for multi-objective and multi-disciplinary design optimization problems that have parameters with interval uncertainty. The dissertation presents models and approaches under four research thrusts. In the first thrust, an approach is presented to obtain robustly optimal solutions which are as best as possible, in a multi-objective sense, and at the same time their sensitivity of objective and/or constraint functions is within an acceptable range. In the second thrust, the robust optimization approach in the first thrust is extended to design optimization problems which are decomposed into multiple subproblems, each with multiple objectives and constraints. In the third thrust, a new approach for multi-objective sensitivity analysis and uncertainty reduction is presented. And in the final research thrust, a metamodel embedded Multi-Objective Genetic Algorithm (MOGA) for solution of design optimization problems is presented. Numerous numerical and engineering examples are used to explore and demonstrate the applicability and performance of the robust optimization, sensitivity analysis and MOGA techniques developed in this dissertation. It is shown that the obtained robust optimal solutions for the test examples are conservative compared to their corresponding optimal solutions in the deterministic case. For the sensitivity analysis, it is demonstrated that the proposed method identifies parameters whose uncertainty reduction or elimination produces the largest payoffs for any given investment. Finally, it is shown that the new MOGA requires a significantly fewer number of simulation calls, when used to solve multi-objective design optimization problems, compared to previously developed MOGA methods while obtaining comparable solutions

Estimating Mixture of Gaussian Processes by Kernel Smoothing

When functional data are not homogenous, for example, when there are multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this article, we propose a new estimation procedure for the mixture of Gaussian processes, to incorporate both functional and inhomogenous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from expectation-maximization (EM) algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset