Experimental Study on Characteristics of NiMnGa Magnetically Controlled Shape Memory Alloy

The static and dynamic magnetic controlling characteristics of NiMnGa magnetically controlled shape memory alloy （MSMA） were experimentally studied. The results show that the characteristics of induced strain with respect to the magnetic field are nonlinear with saturation nature, and dependent on the temperature as well as the load applied to the MSMA. The magnetic shape memory effect can be observed only in complete martensite phase at room temperature. The magnetic permeability of MSMA is not constant and reduces with the increment of magnetic field. The relative saturation magnetic permeability of MSMA is about 1.5.

Controlling Entropic Uncertainty in the Presence of Quantum Memory by Non-Markovian Effects and Atom-Cavity Couplings

Based on the time-convolutionless master-equation approach, the entropic uncertainty in the presence of quantum memory is investigated for a two-atom system in two dissipative cavities. We find that the entropic uncertainty can be controlled by the non-Markovian effect and the atom-cavity coupling. The results show that increasing the atom-cavity coupling can enlarge the oscillating frequencies of the entropic uncertainty and can decrease the minimal value of the entropic uncertainty. Enhancing the non-Markovian effect can reduce the minimal value of the entropic uncertainty. In particular, if the atom-cavity coupling or the non-Markovian effect is very strong, the entropic uncertainty will be very dose to zero at certain time points, thus Bob can minimize his uncertainty about Alice＇s measurement outcomes,

Reliable Memory Feedback Design for a Class of Nonlinear Fuzzy Systems with Time-varying Delay

This paper is concerned with the robust reliable memory controller design for a class of fuzzy uncertain systems with timevarying delay. The system under consideration is more general than those in other existent works. The controller, which is dependent on the magnitudes and derivative of the delay, is proposed in terms of linear matrix inequality （LMI）. The closed-loop system is asymptotically stable for all admissible uncertainties as well as actuator faults. A numerical example is presented for illustration.

Non-fragile memory feedback control for uncertain time-varying delay switched fuzzy systems with unknown nonlinear disturbance

Currently,the feedback control rate of most nonlinear systems is realised by the memoryless state feedback controller which cannot affect the impact of time delay on the systems,and the general processing method of the Lyapunov–Krasovskii functional for the time-varying delay switched fuzzy systems(SFS)is more conservative.Therefore,this paper addresses the problem of nonfragile robust and memory state feedback control for switched fuzzy systems with unknown nonlinear disturbance.Non-fragile memory state feedback robust controller which has two controller gains different from each other,and switching law are designed to keep the proposed systems asymptotically stable for all admissible parameter uncertainties.Delay-dependent less conservative sufficient conditions are obtained through using the Lyapunov–Krasovskii functional method and free-weighting matrices depending on Leibniz–Newton,guaranteeing that the SFS can be asymptotically stable.A numerical example is given to illustrate the proposed controller performs better than the classic memoryless state feedback controller.

Shifts in intrinsic neural timescale of hippocampus support the maturation of inhibitory control and working memory in youth

The intrinsic neural timescale(INT)provides temporal windows in brain activity that process information of different durations,crucial for the integration and segregation of external inputs and ultimately shaping cognition and behavior.Recent research has uncovered a pronounced INT hierarchy along the adult hippocampus's longaxis.Yet,the development of INT organization within the hippocampus—particularly the pattern of its hierarchical structure and its impact on cognitive development—has not been thoroughly investigated in youth.Here,we discovered that the INT distribution in youth presents a distinct hierarchical structure along both posterioranterior and proximal-distal axes of the hippocampus.Strikingly,this hierarchical structure correlates signifi-cantly with the first principal gradient of the hippocampal-cortical functional connectome and the thickness of hippocampal grey matter.Furthermore,we observed notable changes in the hippocampal INT landscape during youth,characterized by a general narrowing of timescales,alongside dedifferentiation along the hippocampal organizational axes.These maturational changes significantly link to improvements in inhibitory control and working memory performance.Collectively,our findings reveal the developmental patterns of temporal integration and segregation hierarchies within hippocampus,and highlights the profound significance of INT as a neural underpinning that orchestrates cognitive growth.

Velocity forecasts using a combined deep learning model in hybrid electric vehicles with V2V and V2I communication

Vehicle velocity forecast is an important clue in improving the performance of energy management in hybrid electric vehicles(HEV). This paper presents a new combined model for predicting vehicle’s velocity time series. The main features of the model are to combine the feature extraction capability of deep restricted Boltzmann machines(DBM) and sequence pattern predicting capability of bidirectional long short-term memory(BLSTM). Hence, the model is named as DBMBLSTM. In addition, the DRMBLSTM model utilizes the vehicle driving information and roadside infrastructure information provided respectively through vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication channels to predict vehicle velocity at various length of prediction horizon. Furthermore, the predictions results of this study are compared with the state of the art of vehicle velocity forecasts. The root mean square error(RMSE) is used as an evaluation criteria of predictions accuracy. Finally,these compared prediction model are applied in model predictive control(MPC) energy management strategy for the verifications of fuel economy improvement of a HEV. Simulation results confirm that the proposed combined deep learning model performs better than other five prediction methods. Therefore, it is a means of arriving at a reliable forecast model for HEV.