Alignment and orientation of an adsorbed dipole molecule

Half-cycle laser pulse is applied on an absorbed molecule to investigate its alignment and orientation behavior. Crossover from field-free to hindered rotation motion is observed by varying the angel of hindrance of potential well. At small hindered angle, both alignment and orientation show sinusoidal-like behavior because of the suppression of higher excited states. However, mean alignment decreases monotonically as the hindered angle is increased, while mean orientation displays a minimum point at certain hindered angle. The reason is attributed to the symmetry of wavefunction and can be explained well by analyzing the coefficients of eigenstates.Comment: 4 pages, 4 figures, to appear in Phys. Rev. B (2004

InGaAs implant-free quantum-well MOSFETs: performance evaluation using 3D Monte Carlo simulation

In this paper we use numerical simulations to evaluate the performance of III-V Implant-Free Quantum-Well (IFQW) MOSFET devices that offer simultaneously high channel mobility, high drive current and excellent electrostatic integrity. Using 3D Monte Carlo simulations we show that to fully understand the performance of this device architecture, Fermi-Dirac statistics and quantum-corrections must be considered to account for the impact of low density-of-states and quantum confinement in the channel layer respectively

Higgs radiation off quarks in supersymmetric theories at e^+e^- colliders

Yukawa couplings between Higgs bosons and quarks in supersymmetric theories can be measured in the processes e^+e^- -> Q Qbar + Higgs. We have determined the cross sections of these processes in the minimal supersymmetric model including the complete set of next-to-leading order QCD corrections for all channels.Comment: 12 pages, latex, 3 figure

Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market

Local nonsimilarity method for the two-phase boundary layer in mixed convection laminar film condensation

The two-phase boundary layer in laminar film condensation was solved by Koh for the free convection regime and forced convection regime using the similarity method. But the problem on mixed convection does not admit similarity solutions. The current work develops a local nonsimilarity method for the full spectrum of mixed convection, with a generic boundary layer formulation reduced to two specific cases mathematically identical to Koh's formulations on the two limiting cases for either free or forced convection. Other solution methods for mixed convection in the literature are compared and critically evaluated to ensure a high level of accuracy in the current method. The current solution is used to extend Fujii's correlation for mixed convection to the region where the energy convection effect is significant but has been hitherto neglected. The modified Fujii correlation provides a practical engineering tool for evaluating laminar film condensation with a mixed convection boundary laye

Oracle Estimation of a Change Point in High-Dimensional Quantile Regression

© 2018, © 2018 The Author(s). Published with license by Taylor & Francis. © 2018, © Sokbae Lee, Yuan Liao, Myung Hwan Seo and Youngki Shin. In this article, we consider a high-dimensional quantile regression model where the sparsity structure may differ between two sub-populations. We develop ℓ1-penalized estimators of both regression coefficients and the threshold parameter. Our penalized estimators not only select covariates but also discriminate between a model with homogenous sparsity and a model with a change point. As a result, it is not necessary to know or pretest whether the change point is present, or where it occurs. Our estimator of the change point achieves an oracle property in the sense that its asymptotic distribution is the same as if the unknown active sets of regression coefficients were known. Importantly, we establish this oracle property without a perfect covariate selection, thereby avoiding the need for the minimum level condition on the signals of active covariates. Dealing with high-dimensional quantile regression with an unknown change point calls for a new proof technique since the quantile loss function is nonsmooth and furthermore the corresponding objective function is nonconvex with respect to the change point. The technique developed in this article is applicable to a general M-estimation framework with a change point, which may be of independent interest. The proposed methods are then illustrated via Monte Carlo experiments and an application to tipping in the dynamics of racial segregation. Supplementary materials for this article are available online

On peaked solitary waves of Degasperis - Procesi equation

The Degasperis - Procesi (DP) equation describing the propagation of shallow water waves contains a physical parameter $\omega$, and it is well-known that the DP equation admits solitary waves with a peaked crest when $\omega = 0$. In this article, we illustrate, for the first time, that the DP equation admits peaked solitary waves even when $\omega \neq 0$. This is helpful to enrich our knowledge and deepen our understandings about peaked solitary waves of the DP equation.Comment: 11 pages, 3 figures, accepted by Science China - Physics, Mechanics & Astronom

Place classification with a graph regularized deep neural network

© 2016 IEEE. Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. In recent years, there is a high exploitation of artificial intelligence algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. First, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Second, each layer of data are fed into a deep neural network for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effectiveness of our end-to-end place classification framework in which both the multilayer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information