Intensified Impacts of Central Pacific ENSO on the Reversal of December and January Surface Air Temperature Anomaly over China since 1997

The reversal of surface air temperature anomalies (SATA) in winter brings a great challenge for short-term climate prediction, and the mechanisms are not well understood. This study found that the reversal of SATA between December and January over China could be demonstrated by the second leading mode of multivariate empirical orthogonal function analysis on the December–January SATA. It further reveals that the central Pacific El Niño–Southern Oscillation (CP ENSO) has contributed more influence on such a reversal of SATA since 1997. CP ENSO shows positive but weak correlations with SATA over China in both December and January during the pre-1996 period, whereas it shows significant negative and positive correlations with the SATA in December and January, respectively, during the post-1997 period. The CP ENSO–related circulations suggest that the change of the Siberian high has played an essential role in the reversal of SATA since 1997. The pattern of sea surface temperature anomalies associated with the CP ENSO leads to a westward-replaced Walker circulation that alters the local meridional circulation and, further, has impacted the Siberian high and SATA over China since 1997. Moreover, the seasonal northward march of the convergence zone from December to January causes a northward-replaced west branch of the Walker circulation in January compared with that in December. The west branch of the Walker circulation in December and January directly modulates local Hadley and Ferrel circulations and then causes contrasting Siberian high anomalies by inducing opposite vertical motion anomalies over Siberia. The reversal of SATA between December and January, therefore, has been more frequently observed over China since 1997. The abovementioned mechanisms are validated by the analysis at pentad time scales and confirmed by numerical simulations.publishedVersio

Determination of reference intervals of serum levels of human epididymis protein 4 (HE4) in Chinese women

The detailed numbers of subjects from different centers. (DOCX 17.3 kb

A study of asymptotic stability for delayed recurrent neural networks

This paper addresses the problem of asymptotic stability for discrete-time recurrent neural networks with time-varying delay. The analysis starts with a general assumption that the time-varying delay may be expressed as the lower bound plus the length of an interval over which the delay varies. Then the delay partitioning technique is used to establish a new delay-dependent sufficient condition under which the asymptotic stability of recurrent neural networks with time-varying delay can be guaranteed. The new stability criterion takes the form of linear matrix inequalities, thus lending itself to being readily checkable by the available software package. The obtained theoretical result is further illustrated by numerical results, including their superiority over the existing results on asymptotic stability of delayed recurrent neural networks

Coordination of multiple agents with double-integrator dynamics under generalized interaction topologies

The problem of the convergence of the consensus strategies for multiple agents with double-integrator dynamics is studied in this paper. The investigation covers two kinds of different settings. In the setting with the interaction topologies for the position and velocity information flows being modeled by different graphs, some sufficient conditions on the fixed interaction topologies are derived for the agents to reach consensus. In the setting with the interaction topologies for the position and velocity information flows being modeled by the same graph, we systematically investigate the consensus algorithm for the agents under both fixed and dynamically changing directed interaction topologies. Specifically, for the fixed case, a necessary and sufficient condition on the interaction topology is established for the agents to reach (average) consensus under certain assumptions. For the dynamically changing case, some sufficient conditions are obtained for the agents to reach consensus, where the condition imposed on the dynamical topologies is shown to be more relaxed than that required in the existing literature. Finally, we demonstrate the usefulness of the theoretical findings through some numerical examples

New synchronization stability of complex networks with an interval time-varying coupling delay

In this brief, the problem of synchronization stability analysis for complex dynamical networks with a time-varying coupling delay is studied. The delay considered in this brief is assumed to vary over an interval where the lower and upper bounds are known. By dividing the interval time-varying delay into a constant and a time-varying part and using a delay-partitioning approach, a new Lyapunov-Krasovskii functional is constructed. Based on this, a new delay-range-dependent criterion is obtained in terms of linear matrix inequalities. A numerical example is provided to show the effectiveness of the proposed results

A new approach to stability analysis of discrete-time recurrent neural networks with time-varying delay

In this paper, the problem of stability analysis of discrete-time recurrent neural networks with time-varying delay is studied. Based on the general assumption of time delay (that is 0 < dm ≤ d(k) ≤ dM), we represent d(k) as dm+h(k) with 0 ≤ h(k) ≤ dM-dm, and introduce a new Lyapunov functional with the idea of delay partitioning. A new stability criterion is then obtained by utilizing the most updated techniques for achieving delay dependence, which is characterized in terms of linear matrix inequalities (LMIs) and can be easily checked by utilizing the efficient LMI toolbox. The merit of the proposed stability lies in its less conservatism than most of the existing results, which is well illustrated via an example

Dissipativity-based sliding mode control of switched stochastic systems

This technical brief is concerned with dissipativity analysis and dissipativity-based sliding mode control (SMC) of continuous-time switched stochastic systems. Firstly, a sufficient condition is proposed to guarantee the mean-square exponential stability and strict dissipativity for the switched stochastic system. Then, an integral-type sliding surface function is designed for establishing a sliding mode dynamics, which can be formulated by a switched stochastic system with an external disturbance/uncertainty. Dissipativity analysis and synthesis are both investigated for the sliding mode dynamics, and consequently sufficient conditions are derived, which pave the way for solving the dissipativity analysis and control problems. Moreover, a SMC law is synthesized to drive the system trajectories onto the predefined sliding surface in a finite time. Finally, the efficiency of the theoretical findings is demonstrated by an illustrative example