80 resultados para Layer dependent order parameters


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We investigate the entanglement spectrum near criticality in finite quantum spin chains. Using finite size scaling we show that when approaching a quantum phase transition, the Schmidt gap, i.e., the difference between the two largest eigenvalues of the reduced density matrix ?1, ?2, signals the critical point and scales with universal critical exponents related to the relevant operators of the corresponding perturbed conformal field theory describing the critical point. Such scaling behavior allows us to identify explicitly the Schmidt gap as a local order parameter.

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A forthcoming challenge in ultracold lattice gases is the simulation of quantum magnetism. That involves both the preparation of the lattice atomic gas in the desired spin state and the probing of the state. Here we demonstrate how a probing scheme based on atom-light interfaces gives access to the order parameters of nontrivial quantum magnetic phases, allowing us to characterize univocally strongly correlated magnetic systems produced in ultracold gases. This method, which is also nondemolishing, yields spatially resolved spin correlations and can be applied to bosons or fermions. As a proof of principle, we apply this method to detect the complete phase diagram displayed by a chain of (rotationally invariant) spin-1 bosons.

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Density-functional theory (DFT) is used to examine the basal and prism surfaces of ice Ih. Similar surface energies are obtained for the two surfaces; however, in each case a strong dependence of the surface energy on surface proton order is identified. This dependence, which can be as much as 50% of the absolute surface energy, is significantly larger than the bulk dependence (< 1%) on proton order, suggesting that the thermodynamic ground state of the ice surface will remain proton ordered well above the bulk order-disorder temperature of about 72 K. On the basal surface this suggestion is supported by Monte Carlo simulations with an empirical potential and solution of a 2D Ising model with nearest neighbor interactions taken from DFT. Order parameters that define the surface energy of each surface in terms of nearest neighbor interactions between dangling OH bonds (those which point out of the surface into vacuum) have been identified and are discussed. Overall, these results suggest that proton order-disorder effects have a profound impact on the stability of ice surfaces and will most likely have an effect on ice surface reactivity as well as ice crystal growth and morphology. S Supplementary data are available from stacks.iop.org/JPhysCM/22/074209/mmedia

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We study the dynamics of the entanglement spectrum, that is the time evolution of the eigenvalues of the reduced density matrices after a bipartition of a one-dimensional spin chain. Starting from the ground state of an initial Hamiltonian, the state of the system is evolved in time with a new Hamiltonian. We consider both instantaneous and quasi adiabatic quenches of the system Hamiltonian across a quantum phase transition. We analyse the Ising model that can be exactly solved and the XXZ for which we employ the time-dependent density matrix renormalisation group algorithm. Our results show once more a connection between the Schmidt gap, i.e. the difference of the two largest eigenvalues of the reduced density matrix and order parameters, in this case the spontaneous magnetisation.

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Novel technology dependent scaling parameters i.e. spacer to gradient ratio and effective channel length (Leff) are proposed for source/drain engineered DG MOSFET, and their significance in minimizing short channel effects (SCES) in high-k gate dielectrics is discussed in detail. Results show that a high-k dielectric should be associated with a higher spacer to gradient ratio to minimise SCEs The analytical model agrees with simulated data over the entire range of spacer widths, doping gradients, high-k gate dielectrics and effective channel lengths.

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Probing non trivial magnetic ordering in quantum magnets realized with ultracold lattice gases demands detection methods with some spatial resolution built on it. Here we demonstrate that the Faraday matter-light interface provides an experimentally feasible tool to distinguish indubitably different quantum phases of a given many-body system in a non-demolishing way. We illustrate our approach by focussing on the Heisenberg chain for spin-1 bosons in the presence of a SU(2) symmetry breaking field. We explain how using the light signal obtained via homodyne detection one can reconstruct the phase diagram of the model. Further we show that the very same technique that provides a direct experimentally measurable signal of different order parameters can be extended to detect also the presence of multipartite entanglement in such systems.

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Single-phase magnetoelectric multiferroics are ferroelectric materials that display some form of magnetism. In addition, magnetic and ferroelectric order parameters are not independent of one another. Thus, the application of either an electric or magnetic field simultaneously alters both the electrical dipole configuration and the magnetic state of the material. The technological possibilities that could arise from magnetoelectric multiferroics are considerable and a range of functional devices has already been envisioned. Realising these devices, however, requires coupling effects to be significant and to occur at room temperature. Although such characteristics can be created in piezoelectric-magnetostrictive composites, to date they have only been weakly evident in single-phase multiferroics. Here in a newly discovered room temperature multiferroic, we demonstrate significant room temperature coupling by monitoring changes in ferroelectric domain patterns induced by magnetic fields. An order of magnitude estimate of the effective coupling coefficient suggests a value of ~1 × 10-7 sm-1.

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Recently, lead iron tantalate/lead zirconium titanate (PZTFT) was demonstrated to possess large, but unreliable, magnetoelectric coupling at room temperature. Such large coupling would be desirable for device applications but reproducibility would also be critical. To better understand the coupling, the properties of all 3 ferroic order parameters, elastic, electric, and magnetic, believed to be present in the material across a range of temperatures, are investigated. In high temperature elastic data, an anomaly is observed at the orthorhombic mm2 to tetragonal 4mm transition, Tot = 475 K, and a softening trend is observed as the temperature is increased toward 1300 K, where the material is known to become cubic. Thermal degradation makes it impos- sible to measure elastic behavior up to this temperature, however. In the low temperature region, there are elastic anomalies near ≈40 K and in the range 160–245 K. The former is interpreted as being due to a magnetic ordering transition and the latter is interpreted as a hysteretic regime of mixed rhom- bohedral and orthorhombic structures. Electrical and magnetic data collected below room temperature show anomalies at remarkably similar temperature ranges to the elastic data. These observations are used to suggest that the three order parameters in PZTFT are strongly coupled.

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This paper proposes a hierarchical energy management system for multi-source multi-product (MSMP) microgrids. Traditional energy hub based scheduling method is combined with a hierarchical control structure to incorporate transient characteristics of natural gas flow and dynamics of energy converters in microgrids. The hierarchical EMS includes a supervisory control layer, an optimizing control layer, and an execution control layer. In order to efficiently accommodate the systems multi time-scale characteristics, the optimizing control layer is decomposed into three sub-layers: slow, medium and fast. Thermal, gas and electrical management systems are integrated into the slow, medium, and fast control layer, respectively. Compared with wind energy, solar energy is easier to integrate and more suitable for the microgrid environment, therefore, potential impacts of the hierarchical EMS on MSMP microgrids is investigated based on a building energy system integrating photovoltaic and microturbines. Numerical studies indicate that by using a hierarchical EMS, MSMP microgrids can be economically operated. Also, interactions among thermal, gas, and electrical system can be effectively managed.

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Epitaxial heterostructures combining ferroelectric (FE) and ferromagnetic (FiM) oxides are a possible route to explore coupling mechanisms between the two independent order parameters, polarization and magnetization of the component phases. We report on the fabrication and properties of arrays of hybrid epitaxial nanostructures of FiM NiFe(2)O(4) (NFO) and FE PbZr(0.52)Ti(0.48)O(3) or PbZr(0.2)Ti(0.8)O(3), with large range order and lateral dimensions from 200 nm to 1 micron. METHODS: The structures were fabricated by pulsed-laser deposition. High resolution transmission electron microscopy and high angle annular dark-field scanning transmission electron microscopy were employed to investigate the microstructure and the epitaxial growth of the structures. Room temperature ferroelectric and ferrimagnetic domains of the heterostructures were imaged by piezoresponse force microscopy (PFM) and magnetic force microscopy (MFM), respectively. RESULTS: PFM and MFM investigations proved that the hybrid epitaxial nanostructures show ferroelectric and magnetic order at room temperature. Dielectric effects occurring after repeated switching of the polarization in large planar capacitors, comprising ferrimagnetic NiFe2O4 dots embedded in ferroelectric PbZr0.52Ti0.48O3 matrix, were studied. CONCLUSION: These hybrid multiferroic structures with clean and well defined epitaxial interfaces hold promise for reliable investigations of magnetoelectric coupling between the ferrimagnetic / magnetostrictive and ferroelectric / piezoelectric phases.

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This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

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Gas absorption accompanied by an irreversible chemical reaction of first-order or second-order in a liquid layer of finite thickness in plug flow has been investigated. The analytical solution to the enhancement factor has been derived for the case of a first-order reaction, and the exact solution to the enhancement factor has been obtained via numerical simulation for the case of a second-order reaction. The enhancement factor in both cases is presented as a function of the Fourier number and tends to deviate from the prediction of the existing enhancement factor expressions based on the penetration theory at Fourier numbers above 0.1 due to the absence of a well-mixed bulk region in the liquid layer. Approximate enhancement factor expressions that describe the analytical and exact solutions with an accuracy of 5?% and 9?%, respectively, have been proposed.

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Glycosylation of low density lipoproteins obtained from 16 patients with Type 1 (insulin-dependent) diabetes and from 16 age-, sex-, and race-matched controls, was determined. The diabetic patients were normolipaemic and were in good or fair glycaemic control. Eleven patients performed home blood glucose monitoring. Glycosylation of low density lipoproteins in the diabetic patients was significantly higher (p less than 0.001) than in the control subjects, and was significantly correlated with haemoglobin A1c, (p less than 0.01), glycosylation of plasma proteins, (p less than 0.001), and mean home blood glucose, (p less than 0.01). This study confirms that, in diabetic patients, increased glycosylation of low density lipoprotein occurs to an extent which correlates closely with other commonly used indices of glycaemic control.

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We employ the time-dependent R-matrix (TDRM) method to calculate anisotropy parameters for positive and negative sidebands of selected harmonics generated by two-color two-photon above-threshold ionization of argon. We consider odd harmonics of an 800-nm field ranging from the 13th to 19th harmonic, overlapped by a fundamental 800-nm IR field. The anisotropy parameters obtained using the TDRM method are compared with those obtained using a second-order perturbation theory with a model potential approach and a soft photon approximation approach. Where available, a comparison is also made to published experimental results. All three theoretical approaches provide similar values for anisotropy parameters. The TDRM approach obtains values that are closest to published experimental values. At high photon energies, the differences between each of the theoretical methods become less significant.

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Compensation for the dynamic response of a temperature sensor usually involves the estimation of its input on the basis of the measured output and model parameters. In the case of temperature measurement, the sensor dynamic response is strongly dependent on the measurement environment and fluid velocity. Estimation of time-varying sensor model parameters therefore requires continuous textit{in situ} identification. This can be achieved by employing two sensors with different dynamic properties, and exploiting structural redundancy to deduce the sensor models from the resulting data streams. Most existing approaches to this problem assume first-order sensor dynamics. In practice, however second-order models are more reflective of the dynamics of real temperature sensors, particularly when they are encased in a protective sheath. As such, this paper presents a novel difference equation approach to solving the blind identification problem for sensors with second-order models. The approach is based on estimating an auxiliary ARX model whose parameters are related to the desired sensor model parameters through a set of coupled non-linear algebraic equations. The ARX model can be estimated using conventional system identification techniques and the non-linear equations can be solved analytically to yield estimates of the sensor models. Simulation results are presented to demonstrate the efficiency of the proposed approach under various input and parameter conditions.