Different mechanisms involved in adaptation to stable and unstable dynamics.

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a "velocity-dependent force field" (VF), which interacted with the arm in a stable manner, and learning in a "divergent force field" (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.

Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

Air stable complementary polymer circuits fabricated in ambient condition by inkjet printing

Air stable complementary polymer inverters were demonstrated by inkjet printing of both top-gate electrodes and the semiconductors in ambient conditions. The p-type and n-type polymer semiconductors were also thermally annealed in ambient conditions after printing. The good performance of circuits in ambient condition shows that the transistors are not only air-stable in term of ambient humidity and oxygen, but also inert to ion migration through dielectrics from the printed gate. The result obtained here has further confirmed the feasibility of fabrication of low-cost polymer complementary circuits in a practical environment. © 2011 Elsevier B.V. All rights reserved.

Stable operation of the Brushless Doubly-Fed Machine (BDFM)

A simple method of controlling the Brushless Doubly-Fed Machine (BDFM) is presented. The controller comprises two Proportional-Integral (PI) modules and requires only the rotor speed feedback. The machine model and the control system are developed in MATLAB. Both simulation and experimental results are presented. The performance of the system is presented in the motoring and generating operations. The experimental tests included in this paper were carried out on a 180 frame size BDFM with a nested-loop rotor. © 2007 IEEE.

Stable RNA hairpins in 88 coding regions of human mRNA

RNA hairpins containing UNCG, GNRA, CUUG (N = A, U, C or G, R = G or A) loops are unusually thermodynamic stable and conserved structures. The structural features of these hairpin loops are very special, and they play very important roles in vivo. They are prevalent in rRNA, catalytic RNA and non-coding mRNA. However, the 5' C(UUCG)G 3' hairpin is not found in the folding structure of 88 human mRNA coding regions. It is also different from rRNA in that there is no preference for certain sequences among tetraloops in these 88 mRNA folding structures.

The identification and quantification of highly stable 'common hairpin' in the dynamic process of co-transcriptional mRNA folding

In our studies, 88 human mRNA samples were collected from the Integrated Sequence-Structure database and then the dynamic process in co-transcriptional mRNA folding was simulated using the RNAstructure version 4.1 program. Through statistical analyses of the frequencies of occurrence of hairpins, a group of special folding structures-the 'common hairpins'-were identified. These 'common hairpins' have lower energies and occur in all the subsequent folding units that formed in the dynamic folding process. By applying the formulas (1)-(4) of the 'common hairpins' statistical model, 163 'common hairpins' were found, to make up about 7% of the total of 2286 hairpins. Classified studies further show that the 'common hairpins' that were studied may oscillate in the dynamic folding process. However, the hairpin loops of the 'common hairpins' and stems proximal to those 'common hairpins' loops maintain topologically stable structures, while other loops and stems distal to the 'common hairpins' loops are shown to be alterable structures. Strikingly, further studies indicate that the stable structures of these 'common hairpins' may have unbeknown effects on controlling the formation of protein structures in the translation process (unpublished results). (c) 2005 Elsevier B.V. All rights reserved.

Energy stable and momentum conserving hybrid finite element method for the incompressible Navier-Stokes equations

A hybrid method for the incompressible Navier-Stokes equations is presented. The method inherits the attractive stabilizing mechanism of upwinded discontinuous Galerkin methods when momentum advection becomes significant, equal-order interpolations can be used for the velocity and pressure fields, and mass can be conserved locally. Using continuous Lagrange multiplier spaces to enforce flux continuity across cell facets, the number of global degrees of freedom is the same as for a continuous Galerkin method on the same mesh. Different from our earlier investigations on the approach for the Navier-Stokes equations, the pressure field in this work is discontinuous across cell boundaries. It is shown that this leads to very good local mass conservation and, for an appropriate choice of finite element spaces, momentum conservation. Also, a new form of the momentum transport terms for the method is constructed such that global energy stability is guaranteed, even in the absence of a pointwise solenoidal velocity field. Mass conservation, momentum conservation, and global energy stability are proved for the time-continuous case and for a fully discrete scheme. The presented analysis results are supported by a range of numerical simulations. © 2012 Society for Industrial and Applied Mathematics.

On the stability of a rod adhering to a rigid surface: Shear-induced stable adhesion and the instability of peeling

Using variational methods, we establish conditions for the nonlinear stability of adhesive states between an elastica and a rigid halfspace. The treatment produces coupled criteria for adhesion and buckling instabilities by exploiting classical techniques from Legendre and Jacobi. Three examples that arise in a broad range of engineered systems, from microelectronics to biologically inspired fiber array adhesion, are used to illuminate the stability criteria. The first example illustrates buckling instabilities in adhered rods, while the second shows the instability of a peeling process and the third illustrates the stability of a shear-induced adhesion. The latter examples can also be used to explain how microfiber array adhesives can be activated by shearing and deactivated by peeling. The nonlinear stability criteria developed in this paper are also compared to other treatments. © 2012 Elsevier Ltd. All rights reserved.