286,650 research outputs found
Phase Transitions for the Brusselator Model
Dynamic phase transitions of the Brusselator model is carefully analyzed,
leading to a rigorous characterization of the types and structure of the phase
transitions of the model from basic homogeneous states. The study is based on
the dynamic transition theory developed recently by the authors
Quark Mass Matrices from a Softly Broken U(1) Symmetry
Assigning U(1) charges to the quarks of the standard model, and allowing one
extra scalar doublet with m^2 > 0, the correct pattern of the up and down quark
mass matrices is obtained, together with their charged-current mixing matrix.Comment: 10 pages, no figur
Dynamic Model and Phase Transitions for Liquid Helium
This article presents a phenomenological dynamic phase transition theory --
modeling and analysis -- for superfluids. As we know, although the
time-dependent Ginzburg-Landau model has been successfully used in
superconductivity, and the classical Ginzburg-Landau free energy is still
poorly applicable to liquid helium in a quantitative sense. The study in this
article is based on 1) a new dynamic classification scheme of phase
transitions, 2) new time-dependent Ginzburg-Landau models for general
equilibrium transitions, and 3) the general dynamic transition theory. The
results in this article predict the existence of a unstable region H, where
both solid and liquid He II states appear randomly depending on fluctuations
and the existence of a switch point M on the lambda-curve, where the
transitions changes types
Subsidization Competition: Vitalizing the Neutral Internet
Unlike telephone operators, which pay termination fees to reach the users of
another network, Internet Content Providers (CPs) do not pay the Internet
Service Providers (ISPs) of users they reach. While the consequent cross
subsidization to CPs has nurtured content innovations at the edge of the
Internet, it reduces the investment incentives for the access ISPs to expand
capacity. As potential charges for terminating CPs' traffic are criticized
under the net neutrality debate, we propose to allow CPs to voluntarily
subsidize the usagebased fees induced by their content traffic for end-users.
We model the regulated subsidization competition among CPs under a neutral
network and show how deregulation of subsidization could increase an access
ISP's utilization and revenue, strengthening its investment incentives.
Although the competition might harm certain CPs, we find that the main cause
comes from high access prices rather than the existence of subsidization. Our
results suggest that subsidization competition will increase the
competitiveness and welfare of the Internet content market; however, regulators
might need to regulate access prices if the access ISP market is not
competitive enough. We envision that subsidization competition could become a
viable model for the future Internet
Neutrino, Lepton, and Quark Masses in Supersymmetry
The recently proposed model of neutrino mass with no new physics beyond the
TeV energy scale is shown to admit a natural and realistic supersymmetric
realization, when combined with another recently proposed model of quark masses
in the context of a softly broken U(1) symmetry. Four Higgs doublets are
required, but two must have masses at the TeV scale. New characteristic
experimental predictions of this synthesis are discussed.Comment: 7 pages, no figur
Quantum Monte Carlo Study of Pairing Symmetry and Correlation in Iron-based Superconductors
We perform a systematic quantum Monte Carlo study of the pairing correlation
in the symmetric microscopic model for iron-based superconductors. It is
found that the pairing with an extensive s-wave symmetry robustly dominates
over other pairings at low temperature in reasonable parameter region. The
pairing susceptibility, the effective pairing interaction and the
antiferromagnetic correlation strongly increase as the on-site Coulomb
interaction increases, indicating the importance of the effect of
electron-electron correlation. Our non-biased numerical results provide a
unified understanding of superconducting mechanism in iron-pnictides and
iron-chalcogenides and demonstrate that the superconductivity is driven by
strong electron-electron correlation effects.Comment: Accepted for publication as a Letter in Physical Review Letters, and
more discussions are adde
The Steady-State Response of a Class of Dynamical Systems to Stochastic Excitation
In this paper a class of coupled nonlinear dynamical systems subjected to stochastic excitation is considered. It is shown how the exact steady-state probability density function for this class of systems can be constructed. The result is then applied to some classical oscillator problems
Stratified Rotating Boussinesq Equations in Geophysical Fluid Dynamics: Dynamic Bifurcation and Periodic Solutions
The main objective of this article is to study the dynamics of the stratified
rotating Boussinesq equations, which are a basic model in geophysical fluid
dynamics. First, for the case where the Prandtl number is greater than one, a
complete stability and bifurcation analysis near the first critical Rayleigh
number is carried out. Second, for the case where the Prandtl number is smaller
than one, the onset of the Hopf bifurcation near the first critical Rayleigh
number is established, leading to the existence of nontrivial periodic
solutions. The analysis is based on a newly developed bifurcation and stability
theory for nonlinear dynamical systems (both finite and infinite dimensional)
by two of the authors [16]
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Building thermal load prediction through shallow machine learning and deep learning
Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty
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