A note on the central limit theorem for bipower variation of general functions

In this paper we present a central limit theorem for general functions of the increments of Brownian semimartingales. This provides a natural extension of the results derived in [O.E. Barndorff-Nielsen, S.E. Graversen, J. Jacod, M. Podolskij, N. Shephard, A central limit theorem for realised power and bipower variations of continuous semimartingales, in: From Stochastic Analysis to Mathematical Finance, Festschrift for Albert Shiryaev, Springer, 2006], where the central limit theorem was shown for even functions. We prove an infeasible central limit theorem for general functions and state some assumptions under which a feasible version of our results can be obtained. Finally, we present some examples from the literature to which our theory can be applied.     

Optimum design and sensitivity analysis of piezoelectric truss structures

This paper is concerned with the optimum design and sensitivity analysis methods of piezoelectric intelligent trusses on the structural stiffness and free-vibration frequency. On the basis of the finite element method and taking into account the mechanical-electric coupling effect under electric load and mechanical load, the computational formula of sensitivities of structural displacement and free-vibration frequency with respect to the size and shape design variables are proposed. A new kind of design variable of the electric voltage and the calculating method of displacement sensitivity with respect to the electric voltage variable is presented. The new method for structural deformation control by optimizing the voltages of piezoelectric active bars is given. The numerical methods of optimum design and sensitivity analysis for piezoelectric truss structures are implemented in JIFEX software. Numerical examples demonstrate the effectiveness of the methods and the program. Project supported by the Special Funds for National Key Basic Research of China (No. G1999032805) and the Foundation for University Key Teachers by the Ministry of Education of China.     

Design of Dynamically Loaded Fiber-Reinforced Structures with Account of Their Vibro-Acoustic Behavior

Dynamically loaded structures for high-technology applications generally require high material damping combined with low construction weight and adequate stiffness. Advanced lightweight structures will have to meet not only these dynamic demands but also improved acoustic (low noise) standards. High-performance materials like magnesium, aluminum, or titanium, which are mainly used in today's lightweight applications, reach their limits with respect to these dynamic and especially vibro-acoustic requirements. They offer a high specific stiffness and strength, but a relatively low damping, which leads to intense acoustic radiation. Therefore, composites or compound materials with a dynamically and vibro-acoustically optimized property profile are needed. The structural dynamic and vibro-acoustic behavior of these types of lightweight structures cannot be described by the use of classical models. Here, the advanced methods developed at ILK are considered, which take into account the special mechanical properties of the fiber-matrix compound. Also, sophisticated numerical simulation techniques such as the finite and the boundary element method are successfully applied.     

Direct spatial optimization in natural resource management: Four linear programming examples

This paper discusses methods for directly incorporating spatial relationships in natural resource optimization models. The approaches are contrasted with the conventional emphasis on adjacency restrictions. Four formulations are presented that address a variety of problems using linear (continuous-variable) programming, which allows reliable solvability of even very large models. The problems addressed include: habitat placement for the black-footed ferret (an aggressive disperser), habitat placement for the black-tailed prairie dog (a gregarious, recalcitrant disperser), placement of control efforts directed at restricting the spread of an exotic pest, and the semi-permanent placement of habitat for the northern spotted owl (a static problem).     

Random coefficient differential equation models for bacterial growth

In the mathematical modeling of population growth, and in particular of bacterial growth, parameters are either measured directly or determined by curve fitting. These parameters have large variability that depends on the experimental method and its inherent error, on differences in the actual population sample size used, as well as other factors that are difficult to account for. In this work the parameters that appear in the Monod kinetics growth model are considered random variables with specified distributions. A stochastic spectral representation of the parameters is used, together with the polynomial chaos method, to obtain a system of differential equations, which is integrated numerically to obtain the evolution of the mean and higher-order moments with respect to time.     

拟似然非线性模型中最大拟似然估计的强相合性

This paper proposes some regularity conditions. On the basis of the proposed regularity conditions, we show the strong consistency of maximum quasi-likelihood estimation （MQLE） in quasi-likelihood nonlinear models （QLNM）. Our results may be regarded as a further generalization of the relevant results in Ref. [4].     

A reduced‐order simulated annealing approach for four‐dimensional variational data assimilation in meteorology and oceanography

Four‐dimensional variational data assimilation in meteorology and oceanography suffers from the presence of local minima in the cost function. These local minima arise when the system under study is strongly nonlinear. The number of local minima further dramatically increases with the length of the assimilation period and often renders the solution to the problem intractable. Global optimization methods are therefore needed to resolve this problem. However, the huge computational burden makes the application of these sophisticated techniques unfeasible for large variational data assimilation systems. In this study, a Simulated Annealing (SA) algorithm, complemented with an order‐reduction of the control vector, is used to tackle this problem. SA is a very powerful tool of combinatorial minimization in the presence of several local minima at the cost of increasing the execution time. Order‐reduction is then used to reduce the dimension of the search space in order to speed up the convergence rate of the SA algorithm. This is achieved through a proper orthogonal decomposition. The new approach was implemented with a realistic eddy‐permitting configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) of the tropical Pacific Ocean. Numerical results indicate that the reduced‐order SA approach was able to efficiently reduce the cost function with a reasonable number of function evaluations. Copyright © 2008 John Wiley & Sons, Ltd.     

Pedestrian flows and non‐classical shocks

We present a model for the flow of pedestrians that describes features typical of this flow, such as the fall due to panic in the outflow of people through a door. The mathematical techniques essentially depend on the use of non‐classical shocks in scalar conservation laws. Copyright © 2005 John Wiley & Sons, Ltd.     

A preferential attachment model with Poisson growth for scale-free networks

We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot of the celebrated model of Barabási and Albert where a network is generated iteratively from a small seed network; at each step a node is added together with a number of incident edges preferentially attached to nodes already in the network. A key feature of our model is that the number of edges added at each step is a random variable with Poisson distribution, and, unlike the Barabási–Albert model where this quantity is fixed, it can generate any network. Our model is motivated by an application in Bayesian inference implemented as Markov chain Monte Carlo to estimate a network; for this purpose, we also give a formula for the probability of a network under our model.     

Asymmetric impacts of global risk appetite on the risk premium for an emerging market

This paper analyzes the impact of global risk appetite on the risk premium utilizing high-frequency data. Taking the Turkish economy as our laboratory, we find that the risk premium volatility responds only to a worsening in the risk appetite for the Turkish economy, which is a result that we do not observe for the other emerging markets. Then, we investigate the role of current account dynamics on this asymmetric effect, by focusing also on an economy with similar current account performance. The empirical results find supporting evidence for the role of current account dynamics on the estimated asymmetry.