Magneto-Electric Effect for Multiferroic Thin Film by Monte Carlo Simulation

Magneto-electric effect in a multiferroic heterostructure film, i.e. a coupled ferromagnetic-ferroelectric thin film, has been investigated through the use of the Metropolis algorithm in Monte Carlo simulations. A classical Heisenberg model describes the energy stored in the ferromagnetic film, and we use a pseudo-spin model with a transverse Ising Hamiltonian to characterise the energy of electric dipoles in the ferroelectric film. The purpose of this article is to demonstrate the dynamic response of polarisation is driven by an external magnetic field, when there is a linear magneto-electric coupling at the interface between the ferromagnetic and ferroelectric components.Comment: 5 pages, 8 figures, The 3rd Advanced Electromagnetics Symposium, Citation: Eur. Phys. J. Appl. Phys., Volume 70, Number 3, (2015

Global synchronization for discrete-time stochastic complex networks with randomly occurred nonlinearities and mixed time delays

Copyright [2010] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the problem of stochastic synchronization analysis is investigated for a new array of coupled discrete-time stochastic complex networks with randomly occurred nonlinearities (RONs) and time delays. The discrete-time complex networks under consideration are subject to: (1) stochastic nonlinearities that occur according to the Bernoulli distributed white noise sequences; (2) stochastic disturbances that enter the coupling term, the delayed coupling term as well as the overall network; and (3) time delays that include both the discrete and distributed ones. Note that the newly introduced RONs and the multiple stochastic disturbances can better reflect the dynamical behaviors of coupled complex networks whose information transmission process is affected by a noisy environment (e.g., Internet-based control systems). By constructing a novel Lyapunov-like matrix functional, the idea of delay fractioning is applied to deal with the addressed synchronization analysis problem. By employing a combination of the linear matrix inequality (LMI) techniques, the free-weighting matrix method and stochastic analysis theories, several delay-dependent sufficient conditions are obtained which ensure the asymptotic synchronization in the mean square sense for the discrete-time stochastic complex networks with time delays. The criteria derived are characterized in terms of LMIs whose solution can be solved by utilizing the standard numerical software. A simulation example is presented to show the effectiveness and applicability of the proposed results

Asymptotic stability for neural networks with mixed time-delays: The discrete-time case

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2009 Elsevier LtdThis paper is concerned with the stability analysis problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability analysis problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01 and EP/C524586/1, an International Joint Project sponsored by the Royal Society of the UK, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, the National Natural Science Foundation of China under Grant 60774073, and the Alexander von Humboldt Foundation of Germany

Three Essays on Climate Change, Renewable Energy and Agriculture in the US

This dissertation contains three essays. The first essay addresses climate impacts on agricultural yields. One practical difficulty in estimating climate impacts is the presence of regionally correlated but omitted factors such as solar radiation and wind speed. Typical panel estimations account for time invariant omitted variables, but do not handle time varying ones that are regionally correlated. To overcome this, an estimation approach incorporating spatial structure is used. We find that the resultant estimates exhibit improved out-of-sample prediction accuracy compared with conventional panel model results but still reveal basic findings found elsewhere in the literature on relationships between temperature and crop yield. The second essay is about projection of biofuel production and practical considerations involving expensive biorefineries. Many analyses addressing national level expanded biofuel production exhibit unrealistic, time varying locations of facilities. Namely, once built biorefineries are fixed in location, technology and general class of feedstocks they use but these studies ignore such facts. To examine the implications, we do a market penetration analysis with and without that fixity. We find that neglecting asset fixity leads to upwardly biased projections of ethanol attractiveness, as well as unrealistic production variations over time and space. In particular, when asset fixity is considered the price needed to achieve cellulosic market penetration levels comparable to those in legislation is significantly increased, reaching $1.06/liter as opposed to$0.79/liter without it. The third essay examines renewable electricity and its future market share. Investments in renewable electricity have increased recently due to rapid technological progress. Questions going forward are: (1) Will such technical achievement stimulating market based adoption persist? (2) Are additional developments needed to enhance additional adoption? These questions are addressed in this study using a sector modeling approach. The results indicate that adoption of renewable electricity under current projections of technical progress, will lead to a 25% market share by 2050. If greater market shares are desired, we find this can be stimulated by faster technological progress, reliability enhancing electricity storage and power system management, or direct carbon pricing, with combinations of these supporting as much as a 60% market share by 2050

A GIS-based Multi-objective Optimization of a Lignocellulosic Biomass Supply Chain: A Case Study in Tennessee

To achieve an economically and environmentally sustainable lignocellulosic biomass (LCB)-based biofuel industry sector, the design and location of a sustainable LCB supply chain is important. In this study, a multi-objective optimization model integrated with high-resolution geographical data was developed to examine the optimal switchgrass supply chain for a potential biorefinery in Tennessee, specifically evaluating the potential tradeoffs between the objectives of minimizing plant-gate cost and GHG emissions from the switchgrass supply chain. The key findings of this study are as follows: both plant-gate feedstock cost and GHG emissions were sensitive to the type of land converted into switchgrass production, the type of land use change also affected the density of the feedstock supply region due to the spatial heterogeneity in the availability of different types of land, hence affecting transportation-related cost and GHG emissions, and a tradeoff relationship was discovered between cost and GHG emissions for the switchgrass supply chain, primarily driven by the type of land converted. As a result of land use changes and transportation distances, the imputed cost to reduce one unit of GHG emissions was initially modest; however, the imputed cost increased considerably when the supply chain GHG emissions were further mitigated. This implied that the location of switchgrass production and the resulting changes in crop production should be considered in targeting government incentives to encourage switchgrass-based biofuel production in the state and the southeastern region. Sensitivity analyses indicated that the dry matter loss (DML) decomposition, if considered as a source of GHG emissions, would considerably increase the supply chain GHG emissions. Different harvest and storage technology used in the feedstock supply chain altered the DML rate and corresponding GHG emissions however did not change the tradeoffs between the two objectives significantly. The consideration of GHG emissions from cattle relocation, on the other hand, appears to reduce the GHG emission level of the supply chain to a great extent and change the tradeoff relation between the two objectives

State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: The discrete-time case

Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the problem of state estimation for a class of discrete-time coupled uncertain stochastic complex networks with missing measurements and time-varying delay. The parameter uncertainties are assumed to be norm-bounded and enter into both the network state and the network output. The stochastic Brownian motions affect not only the coupling term of the network but also the overall network dynamics. The nonlinear terms that satisfy the usual Lipschitz conditions exist in both the state and measurement equations. Through available output measurements described by a binary switching sequence that obeys a conditional probability distribution, we aim to design a state estimator to estimate the network states such that, for all admissible parameter uncertainties and time-varying delays, the dynamics of the estimation error is guaranteed to be globally exponentially stable in the mean square. By employing the Lyapunov functional method combined with the stochastic analysis approach, several delay-dependent criteria are established that ensure the existence of the desired estimator gains, and then the explicit expression of such estimator gains is characterized in terms of the solution to certain linear matrix inequalities (LMIs). Two numerical examples are exploited to illustrate the effectiveness of the proposed estimator design schemes