Imaging adiabatic control of charge transfer in molecules

Charge transfer is a subfemtosecond process in molecules that creates chemical and electronic structure changes. At the quantum level the process can be coherently controlled by ultrashort light pulses. We show how the charge transfer process can be manipulated using a combination of dynamic and static fields and predict how this can be observed experimentally by imaging with photoionization.

How position, velocity, and temporal information combine in the prospective control of catching:data and model

The cerebral cortex contains circuitry for continuously computing properties of the environment and one's body, as well as relations among those properties. The success of complex perceptuomotor performances requires integrated, simultaneous use of such relational information. Ball catching is a good example as it involves reaching and grasping of visually pursued objects that move relative to the catcher. Although integrated neural control of catching has received sparse attention in the neuroscience literature, behavioral observations have led to the identification of control principles that may be embodied in the involved neural circuits. Here, we report a catching experiment that refines those principles via a novel manipulation. Visual field motion was used to perturb velocity information about balls traveling on various trajectories relative to a seated catcher, with various initial hand positions. The experiment produced evidence for a continuous, prospective catching strategy, in which hand movements are planned based on gaze-centered ball velocity and ball position information. Such a strategy was implemented in a new neural model, which suggests how position, velocity, and temporal information streams combine to shape catching movements. The model accurately reproduces the main and interaction effects found in the behavioral experiment and provides an interpretation of recently observed target motion-related activity in the motor cortex during interceptive reaching by monkeys. It functionally interprets a broad range of neurobiological and behavioral data, and thus contributes to a unified theory of the neural control of reaching to stationary and moving targets.

Prospective control of manual interceptive actions:comparative simulations of extant and new model constructs

Two prospective controllers of hand movements in catching-both based on required velocity control-were simulated. Under certain conditions, this required velocity control led to overshoots of the future interception point. These overshoots were absent in pertinent experiments. To remedy this shortcoming, the required velocity model was reformulated in terms of a neural network, the Vector Integration To Endpoint model, to create a Required Velocity Integration To Endpoint model. Addition of a parallel relative velocity channel, resulting in the Relative and Required Velocity Integration To Endpoint model, provided a better account for the experimentally observed kinematics than the existing, purely behavioral models. Simulations of reaching to intercept decelerating and accelerating objects in the presence of background motion were performed to make distinct predictions for future experiments.

NO3- transport across the plasma membrane of Arabidopsis thaliana root hairs:Kinetic control by pH and membrane voltage

High-affinity nitrate transport was examined in intact root hair cells of Arabidopsis thaliana using electrophysiological recordings to characterise the response of the plasma membrane to NO₃^-challenge and to quantify transport activity. The NO₃^--associated membrane current was determined using a three-electrode voltage clamp to bring membrane voltage under experimental control and to compensate for current dissipation along the longitudinal cell axis. Nitrate transport was evident in the roots of seedlings grown in the absence of a nitrogen source, but only 4-6 days postgermination. In 6-day-old seedlings, additions of 5-100 μm NO₃^-to the bathing medium resulted in membrane depolarizations of 8-43 mV, and membrane voltage (V_m) recovered on washing NO₃^-from the bath. Voltage clamp measurements carried out immediately before and following NO₃^-additions showed that the NO₃^--evoked depolarizations were the consequence of an inward-directed current that appeared across the entire range of accessible voltages (-300 to +50 mV). Both membrane depolarizations and NO₃^--evoked currents recorded at the free-running voltage displayed quasi-Michaelian kinetics, with apparent values for K_m of 23 ± 6 and 44 ± 11 μm, respectively and, for the current, a maximum of 5.1 ± 0.9 μA cm^-2. The NO₃^-current showed a pronounced voltage sensitivity within the normal physiological range between -250 and -100 mV, as could be demonstrated under voltage clamp, and increasing the bathing pH from 6.1 to 7.4-8.0 reduced the current and the associated membrane depolarizations 3- to 8-fold. Analyses showed a well-defined interaction between the kinetic variables of membrane voltage, pH_o and [NO₃^-]_o. At a constant pH_o of 6.1, depolarization from -250 to -150 mV resulted in an approximate 3-fold reduction in the maximum current but a 10% rise in the apparent affinity for NO₃^-. By contrast, the same depolarization effected an approximate 20% fall in the K_m for transport as a function in [H⁺]_o. These, and additional characteristics of the transport current implicate a carrier cycle in which NO₃^-binding is kinetically isolated from the rate-limiting step of membrane charge transit, and they indicate a charge-coupling stoichiometry of 2(H⁺) per NO₃^-anion transported across the membrane. The results concur with previous studies showing a high-affinity NO₃^-transport system in Arabidopsis that is inducible following a period of nitrogen-limiting growth, but they underline the importance of voltage as a kinetic factor controlling NO₃^-transport at the plant plasma membrane. © 1995 Springer-Verlag New York Inc.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

High-affinity NO3 --H+ cotransport in the fungus Neurospora:Induction and control by pH and membrane voltate

High-affinity nitrate transport was examined in intact hyphae of Neurospora crassa using electrophysiological recordings to characterize the response of the plasma membrane to NO₃ ^- challenge and to quantify transport activity. The NO₃ ^--associated membrane current was determined using a three electrode voltage clamp to bring membrane voltage under experimental control and to compensate for current dissipation along the longitudinal cell axis. Nitrate transport was evident in hyphae transferred to NO₃ ^--free, N-limited medium for 15 hr, and in hyphae grown in the absence of a nitrogen source after a single 2-min exposure to 100 μM NO₃ ^-. In the latter, induction showed a latency of 40-80 min and rose in scalar fashion with full transport activity mensurable approx. 100 min after first exposure to NO₃ ^-; it was marked by the appearance of a pronounced sensitivity of membrane voltage to extracellular NO₃ ^- additions which, after induction, resulted in reversible membrane depolarizations of (+)54-85 mV in the presence of 50 μM NO₃ ^-; and it was suppressed when NH₄ ⁺, was present during the first, inductive exposure to NO₃ ^-. Voltage clamp measurements carried out immediately before and following NO₃ ^- additions showed that the NO₃ ^--evoked depolarizations were the consequence of an inward-directed current that appeared in parallel with the depolarizations across the entire range of accessible voltages -400 to +100 mV). Measurements of NO₃ ^- uptake using NO₃ ^--selective macroelectrodes indicated a charge stoichiometry for NO₃ ^- transport of 1(+):1(NO₃ ^-) with common K(m) and J(max) values around 25 μM and 75 pmol NO₃ ^- cm^-2sec^-1, respectively, and combined measurements of pH(o) and [NO₃ ^-](o) showed a net uptake of approx. 1 H⁺ with each NO₃ ^- anion. Analysis of the NO₃ ^- current demonstrated a pronounced voltage sensitivity within the normal physiological range between -300 and -100 mV as well as interactions between the kinetic parameters of membrane voltage, pH(o) and [NO₃ ^-](o). Increasing the bathing pH from 5.5 to 8.0 reduced the current and the associated membrane depolarizations 2- to 4-fold. At a constant pH(o) of 6.1, driving the membrane voltage from -350 to -150 mV resulted in an approx. 3-fold reduction in the maximum current and a 5-fold rise in the apparent affinity for NO₃ ^-. By contrast, the same depolarization effected an approx. 20% fall in the K(m) for transport as a function in [H⁺](o). These, and additional results are consistent with a charge-coupling stoichiometry of 2(H⁺) per NO anion transported across the membrane, and implicate a carrier cycle in which NO binding is kinetically adjacent to the rate-limiting step of membrane charge transit. The data concur with previous studies demonstrating a pronounced voltage-dependence to high-affinity NO₃ ^- transport system in Arabidopsis, and underline the importance of voltage as a kinetic factor controlling NO₃ ^- transport; finally, they distinguish metabolite repression of NO₃ ^- transport induction from its sensitivity to metabolic blockade and competition with the uptake of other substrates that draw on membrane voltage as a kinetic substrate.

Feasibility Assessment of Plug and Play Model Predictive Control for use in DC Grids

Distributed control techniques can allow Transmission System Operators (TSOs) to coordinate their responses via TSO-TSO communication, providing a level of control that lies between that of centralised control and communication free decentralised control of interconnected power systems. Recently the Plug and Play Model Predictive Control (PnPMPC) toolbox has been developed in order to allow practitioners to design distributed controllers based on tube-MPC techniques. In this paper, some initial results using the PnPMPC toolbox for the design of distributed controllers to enhance AGC in AC areas connected to Multi-Terminal HVDC (MTDC) grids, are illustrated, in order to evaluate the feasibility of applying PnPMPC for this purpose.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.