Parameter Region Research Articles

Turing-Hopf bifurcation and bi-stable spatiotemporal periodic orbits in a delayed predator-prey model with predator-taxis

When Turing-Hopf bifurcation occurs, it is naturally worth discussing the types of spatiotemporal patterns and their stabilities. In this paper, for partial functional differential equations with nonlinear diffusion, we establish the explicit formulae for the coefficients of normal form associated with Turing-Hopf bifurcation. This provides an explicit connection between the original system and the third-order ordinary differential equations (i.e. normal form) restricted on center manifold. Subsequently, with the aid of extended formulae, we discuss a delayed predator-prey model with predator-taxis and find that Turing-Hopf bifurcation leads to the emergence of a pair of stable single-peak spatially inhomogeneous steady states, a pair of stable multi-peak spatially inhomogeneous periodic orbits and so on. Also, the parameter regions in which these phenomena arise are given quantitatively. It is further shown that the pair of bistable spatially inhomogeneous periodic orbits arise due to the interaction of predator-taxis and time delay, which won't emerge in the same system without predator-taxis.

Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model

Root-zone soil moisture (RZSM) plays a key role in the hydrologic cycle and regulates water–heat exchange. Although site observations can provide soil profile moisture measurements, they have a restricted representation. Satellites can determine soil moisture on a large scale, yet the depth of detection is limited. RZSM can be estimated on a large scale using the soil moisture analytical relationship (SMAR) and surface soil moisture (SSM). However, the applicability of the SMAR to different deep-root zones and covariate sources is unclear. This paper investigates the applicability of the SMAR in the Shandian River Basin, upstream of the Luan River in China, by combining site and regional soil moisture, soil properties, and meteorological data. In particular, we first compared the estimation results of the SMAR at different depths (10–20 cm; 10–50 cm) and using covariates from different sources (dataset, SMAR-P1; literature, SMAR-P2) at the site in order to generate SMAR calibration parameters. The parameters were then regionalized based on multiple linear regression by combining the SMAR-P1, SMAR-P2, and SMAR calibration parameters in the 10–50 cm root zone. Finally, the Shandian River RZSM was estimated using regional surface soil moisture and the aforementioned regionalized parameters. At the site scale, diffusion coefficient b obtained in the 10–20 cm root zone at the same depth as the surface layer exceeded the upper limit of the SMAR by one. This is not fit an environment within the site context, and thus the SMAR is not applicable at this particular depth. The opposite is observed for the 10–50 cm root zone. In addition, SMAR-P1 (RMSE = 0.02) outperformed SMAR-P2 (RMSE = 0.04) in the estimation of the RZSM at 10–50 cm. Parameter regionalization analysis revealed the failure of SMAR-P2 to pass the significance test (p > 0.05) for building a multivariate linear model, while SMAR-P1 successfully passed the significance test (p < 0.05) and finished the parameter regionalization process. The median RMSE and median R2adj of the regional RZSM results were determined as 0.12 and 0.3, respectively. The regional RZSM agrees with the spatial trend of the Shandian River. This study examines the suitability of the SMAR model in varying deep-root zones and with diverse covariate sources. The results provide a crucial basis for future utilization of the SMAR.

A minimal inverse seesaw model with S4 flavour symmetry

We construct an S4 flavour symmetric minimal inverse seesaw model where the standard model is extended by adding two right-handed and two standard model gauge singlet neutrinos to explain the origin of tiny neutrino masses. The resulting model describes the lepton mass spectra and flavour mixing quite well for the case of the normal hierarchy of neutrino masses. The prediction of the model on the Dirac CP-violating phase is centered around 370.087°. Furthermore, using the allowed region for the model parameters, we have calculated the value of the effective Majorana neutrino mass, |〈mee〉|, which characterizes neutrinoless double beta decay.

Single-step first order phase transition and gravitational waves in a SIMP dark matter scenario

We investigate the non-zero temperature dynamics of a sub-GeV dark matter scenario freezing-out via self-interactions. As a prototype, we take up the case of a scalar dark matter species undergoing 3→2 number changing annihilations catalysed by another scalar. We study the shape of the thermal potential of this scenario in a parameter region accounting for the observed relic abundance. An analysis reveals the possibility of a first order phase transition with bubble nucleation occurring at sub-GeV temperatures. This finding can be correlated with the typical sub-GeV masses in the framework. The gravitational wave spectra associated with such a phase transition is subsequently computed.

Electroweak corrections to Higgs boson decays in a Complex Singlet extension of the SM and their phenomenological impact

The complex singlet extension CxSM of the Standard Model (SM) is a simple extension of the SM with two visible Higgs bosons in the spectrum and a Dark Matter (DM) candidate. In this paper we complete the computation of the next-to-leading (NLO) electroweak (EW) corrections to on-shell and non-loop-induced Higgs decays. Our calculations are implemented in the code EWsHDECAY which also includes the relevant QCD corrections. Performing an extensive parameter scan in the model and including all relevant theoretical and experimental single- and di-Higgs as well as DM constraints, we obtain a viable parameter sample. We find that current DM constraints are able to test the model in DM mass regions where collider searches are not sensitive. The relative EW corrections turn out to be large for scenarios with relatively large couplings, threshold effects or small leading-order (LO) widths. Otherwise, they are of typical EW size and can amount up to about 20–25%. The theory uncertainty derived from the change of the renormalization scheme dependence then is of a few per cent. While the NLO corrections applied in the constraints due to single- and di-Higgs searches impact the validity of specific parameter points, the overall shape of the allowed parameter region is not yet sensitive to the EW corrections. This picture will change with further increased experimental precision in the future and necessitates precise predictions on the theory side as presented in this paper.

The Nonlinear Dynamics of a MEMS Resonator with a Triangular Tuning Comb.

The nonlinear dynamic response of a MEMS resonator with a triangular tuning comb is studied. The motion equation with dis-smooth tuning electrostatic force is derived according to Newton's second law. The analytical solution of the periodic response is obtained using the harmonic balance method and section integral method. The singularity theory is then applied to investigate the bifurcation of the periodic response of the untuned system. The transition sets on the DC-AC voltage plane dividing the planes into several persistent regions are obtained. The bifurcation diagrams' topological structures and jump phenomena corresponding to different parameter regions are analyzed. We explore the effects of tuning voltage on the response. This demonstrates that the amplitude-frequency curves present more hardening characteristics with increased tuning voltage. Many twists, bifurcation points, and unstable solutions appear, leading to complicated jump phenomena. Two bifurcation points exist on the response curves: the smooth and dis-smooth bifurcation points, with the latter occurring on the switching plane of non-uniform fingers.

Analysis of Excitement Caused by Colored Noise in a Thermokinetic Model

In this paper, a thermokinetic model forced by colored noise is studied. We analyze the mechanisms of stochastic excitement of equilibrium modes under variation of correlation time and noise intensity. It is shown that the phenomenon of colored-noise-induced excitement is accompanied by stochastic P-bifurcations. The region of the correlation parameter in which resonance occurs is localized. To study the phenomenon of colored-noise-induced excitement, we develop the probabilistic analysis based on the confidence domains method.

Role of QCD in moduli stabilization during inflation and axion dark matter

Ignorance of the initial condition for the axion dynamics in the early Universe has led us to consider an O(1) valued initial amplitude, and that prefers the decay constant, Fa, of the QCD axion to be an intermediate scale such as 1012 GeV in order to explain the dark matter abundance. We explore a cosmological scenario of Fa being much larger than 1012 GeV by considering the axion and modulus dynamics during inflation to set the initial amplitude. We show that if the volume modulus (radion) of the extra-dimension is stabilized mainly by the QCD contribution to the modulus potential during inflation, the QCD axion with the string-scale decay constant obtains a mass around the inflationary Hubble parameter. This means that the axion rolls down to the θ = 0 minimum during the inflation realizing almost vanishing initial amplitude, and the inflationary quantum fluctuation can be the dominant source of the current number density of axions. We find natural parameter regions where the axion explains the cold dark matter of the Universe, while the constraint on the isocurvature perturbation is avoided. The presence of the axion miniclusters or axion stars are predicted in a wide range of parameters, including the one explains the Subaru-HCS microlensing event.

Quantum (in)stability of maximally symmetric space-times

Classical gravity coupled to a CFT4 (matter) is considered. The effect of the quantum dynamics of matter on gravity is studied around maximally symmetric spaces (flat, de Sitter and Anti de Sitter). The structure of the graviton propagator is modified and non-trivial poles appear due to matter quantum effects. The position and residues of such poles are mapped as a function of the relevant parameters, the central charge of the CFT4, the two R2 couplings of gravity as well as the curvature of the background space-time. The instabilities induced are determined. Such instabilities can be important in cosmology as they trigger the departure from de Sitter space and in some regions of parameters are more important than the well-known scalar instabilities. It is also determined when the presence of such instabilities is unreliable if the associated scales are larger than the “species” cutoff of the gravitational theory.

Performance Improvement of a Direct Carbon Fuel Cell through an Irreversible Vacuum Thermionic Generator

A novel hybrid system consisting of a direct carbon fuel cell (DCFC), a thermionic generator (TIG), and a regenerator is developed to recover the exhaust heat from the fuel cell. Expressions for the power output and efficiency of subsystems and the hybrid system are derived. Based on the energy balance equation, the area matching problem between the DCFC and the TIG is discussed and solved. By considering the main irreversibilities, the influences of the DCFC’s current density and the TIG’s voltage on the performance of the hybrid system are revealed. The maximum power output density and the corresponding efficiency of the hybrid system are, respectively, equal to 379 W/m2 and 36%. To enhance the maximum power density of the single DCFC, the optimal regions of the main parameters are determined.

Vertical nonlinear vibration analysis of cold rolling mill considering structural gap and dynamic rolling force

To reveal the impact of vertical nonlinear vibration on the stabilization of the precision cold rolling system, the vertical nonlinear vibration model with consideration of structural gap and dynamic rolling force has been established. Utilizing the Magnus series method with high accuracy and long-term numerical stabilization, we calculate the vibration response of the cold rolling system and conduct numerical simulations to determine the bifurcation characteristics, maximum Lyapunov exponent, and two-parameter dynamical behavior. The results reveal the parameter regions of bifurcation and chaos that cause the vibration instability of the cold rolling system. It has also been found that the system has the nonlinear phenomenon of transition from period-3 motion to chaotic motion. More importantly, two-parameter matching regions for the cold rolling system performing periodic and chaotic motions are determined. Two-parameter matching solutions for improving and deteriorating the stabilization of the cold rolling system are clarified, and the preferred matching regions for the parameters coupling with the structural gap and dynamic rolling force are revealed. The results reveal the impact of system parameters upon the vertical nonlinear vibration of the cold rolling mill, which can serve as an academic reference for reducing the vibration and enhancing the stabilization of the cold rolling system.

New constraint on dark photon at T2K off-axis near detector

The T2K experiment is one of the most powerful long-baseline experiments to investigate neutrino oscillations. The off-axis near detector called ND280 is installed 280 m downstream from the neutrino production target to measure the neutrino energy spectrum. In this paper, we study the capability of the ND280 detector to search for the dark photon produced through the meson rare decay and proton bremsstrahlung processes at the proton beam dump. We find that the ten-year operation of T2K with the ND280 detector excludes the unexplored parameter region for the dark photon mass and kinetic mixing. We also show that a broader parameter region can be searched by the ND280 in the future T2K operation for dark photon as well as U(1)B−L gauge boson.

Statistical Inference for Partially Linear Varying Coefficient Spatial Autoregressive Panel Data Model

This paper studies the estimation and inference of a partially linear varying coefficient spatial autoregressive panel data model with fixed effects. By means of the basis function approximations and the instrumental variable methods, we propose a two-stage least squares estimation procedure to estimate the unknown parametric and nonparametric components, and meanwhile study the asymptotic properties of the proposed estimators. Together with an empirical log-likelihood ratio function for the regression parameters, which follows an asymptotic chi-square distribution under some regularity conditions, we can further construct accurate confidence regions for the unknown parameters. Simulation studies show that the finite sample performance of the proposed methods are satisfactory in a wide range of settings. Lastly, when applied to the public capital data, our proposed model can also better reflect the changing characteristics of the US economy compared to the parametric panel data models.

Shortcut‐to‐Adiabatic Controlled‐Phase Gate in Rydberg Atoms

AbstractA shortcut‐to‐adiabatic protocol for the realization of a fast and high‐fidelity controlled‐phase gate in Rydberg atoms is developed. The adiabatic state transfer, driven in the high‐blockade limit, is sped up by compensating nonadiabatic transitions via oscillating fields that mimic a counterdiabatic Hamiltonian. High fidelities are obtained in wide parameter regions. The implementation of the bare effective counterdiabatic field, without original adiabatic pulses, enables to bypass gate errors produced by the accumulation of blockade‐dependent dynamical phases, making the protocol efficient also at low blockade values. As an application toward quantum algorithms, how the fidelity of the gate impacts the efficiency of a minimal quantum‐error correction circuit is analyzed.

A model with vectorlike fermions and U(1)X symmetry: CKM unitarity, b → s transitions, and prospect at Belle II

The updated analysis of the LHCb Collaboration on the lepton flavor violation suggests that the new physics should couple to muons and electrons with comparable magnitudes, resulting in the anomalies in both rare decay channels, b→sμ+μ− and b→se+e−. Meanwhile, the recent result of the Muon g−2 experiment with higher precision has increased the existing tension with the standard model prediction. In this paper, we consider an extension of the standard model with a new sector consisting of vectorlike fermions and two scalar charged under an extra U(1)X gauge symmetry. The exotic Yukawa interactions in this model lead to the quark mixing responsible for the additional contributions to the flavor changing neutral currents in B-meson decays, and solve the muon g−2 discrepancy. We derive the analytic expression of the new physics contributions to the Wilson coefficient C7 in the effective Hamiltonian, and point out that the CKM unitarity violation can be explained within this context. By calculating the branching ratio of the inclusive radiative B decay, the impact of current experimental data of the b→sγ transition on the model and the future prospect at the Belle II experiment are investigated. Taking into account the current data on the muon anomalous magnetic moment, the CKM unitarity violation, the constraints on the flavor observables relevant to the b→s transitions, the LHC searches for vectorlike quarks, and the perturbation limits of the couplings, the viable parameter regions of the model are identified.

Sharpening the A → Z(*)h signature of the Type-II 2HDM at the LHC through advanced Machine Learning

The A → Z(*)h decay signature has been highlighted as possibly being the first testable probe of the Standard Model (SM) Higgs boson discovered in 2012 (h) interacting with Higgs companion states, such as those existing in a 2-Higgs Doublet Model (2HDM), chiefly, a CP-odd one (A). The production mechanism of the latter at the Large Hadron Collider (LHC) takes place via bb¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ b\\overline{b} $$\\end{document}-annihilation and/or gg-fusion, depending on the 2HDM parameters, in turn dictated by the Yukawa structure of this Beyond the SM (BSM) scenario. Among the possible incarnations of the 2HDM, we test here the so-called Type-II, for a twofold reason. On the one hand, it intriguingly offers two very distinct parameter regions compliant with the SM-like Higgs measurements, i.e., where the so-called ‘SM limit’ of the 2HDM can be achieved. On the other hand, in both configurations, the AZh coupling is generally small, hence the signal is strongly polluted by backgrounds, so that the exploitation of Machine Learning (ML) techniques becomes extremely useful. In this paper, we show that the application of advanced ML implementations can be decisive in establishing such a signal. This is true for all distinctive kinematical configurations involving the A → Z(*)h decay, i.e., below threshold (mA < mZ + mh), at its maximum (mZ + mh < mA < 2mt) and near the onset of tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ t\\overline{t} $$\\end{document} pair production (mA ≈ 2mt), for which we propose Benchmark Points (BPs) for future phenomenological analyses.

Soft and hard scales of the transverse momentum distribution in the color string percolation model

In color string models, the transverse momentum distribution (TMD) is obtained through the convolution of the Schwinger mechanism with the string tension fluctuations distribution. Considering a q-Gaussian distribution for these fluctuations, the TMD becomes a hypergeometric confluent function that adequately reproduces the characteristic scales at low and high p T values. In this approach, the hard scale of the TMD is a consequence of considering a heavy-tailed distribution for the string tension fluctuations whose width rises as , multiplicity or centrality increases. In this paper, we introduce broader information of the TMD in the color string percolation model by determining the color suppression factor, which now also depends on the parameters of the q-Gaussian. To this end, we analyze the reported data on pp and AA collisions at different center of mass energies, multiplicities, and centralities. In particular, for minimum bias pp collisions, we found that the q-Gaussian parameters and the effective temperature are monotonically increasing functions of the center of mass energy. Similar results are found for AA collisions as a function of the centrality at fixed . We summarize these results in a phase diagram that indicates the q-Gaussian parameters region allowing the quark–gluon plasma formation.

Robust estimation and bias-corrected empirical likelihood in generalized linear models with right censored data

In this paper, we study the robust estimation and empirical likelihood for the regression parameter in generalized linear models with right censored data. A robust estimating equation is proposed to estimate the regression parameter, and the resulting estimator has consistent and asymptotic normality. A bias-corrected empirical log-likelihood ratio statistic of the regression parameter is constructed, and it is shown that the statistic converges weakly to a standard χ 2 distribution. The result can be directly used to construct the confidence region of regression parameter. We use the bias correction method to directly calibrate the empirical log-likelihood ratio, which does not need to be multiplied by an adjustment factor. We also propose a method for selecting the tuning parameters in the loss function. Simulation studies show that the estimator of the regression parameter is robust and the bias-corrected empirical likelihood is better than the normal approximation method. An example of a real dataset from Alzheimer's disease studies shows that the proposed method can be applied in practical problems.

Enhancing active disturbance rejection design via deep reinforcement learning and its application to autonomous vehicle

Taking the velocity regulation of autonomous driving as an example, this paper developed an enhancing active disturbance rejection design employing the deep reinforcement learning-deep deterministic policy gradient (DDPG). In this scheme, the active disturbance rejection control (ADRC) is adopted to online estimate and compensate disturbances and uncertainties, and feasible regions of control parameters are obtained through the Lyapunov method. Then DDPG is combined with ADRC to online adaptively tune control parameters in response to the changing environments, where safety, comfort, and energy-saving are considered in the reward design, and mapping relation between the defined action and state is constructed for the maximal reward. Besides, numerical simulations demonstrate the better performance and stronger robustness of the enhancing design when facing uncertainties, sensor noise, and mechanical faults, and comfort and energy consumption have also been improved to some extent compared with the general ADRC and model predictive control (MPC).

A general framework for constructing the feasibility-constraint function of lamination parameters in optimization

Lamination parameters are extensively employed as design variables in the optimization design of composite laminates. The feasibility-constraint function of the lamination parameters in optimization is essential since it is related to the accuracy of the optimal results. In the existing literature, the feasible region for lamination parameters is expressed by multiple inequalities, which is inaccurate and unsmooth at some corners and tedious in the cases of high dimensions. This paper aims at proposing a general framework for constructing the feasibility-constraint functions in optimization for any given set of lamination parameters with higher precision compared to the functions given by the inequalities. The feasibility-constraint function is generated by training an artificial neural network with the backpropagation algorithm based on the idea of the level set method. The training data mainly comes from the boundary points shaped by the hyperplanes calculated by a variational method. The proposed framework consists of four parts: obtaining the hyperplanes, generating the boundary points of the feasible region, constructing the feasibility-constraint function, and evaluating the accuracy of the generated function. Several experiments are implemented to validate the proposed method in the cases of four-, six-, and eight-dimensional lamination parameters. The 4D results show that the generated feasibility-constraint functions have the advantage in precision compared to the existing constraint functions in literature in the real application of structural optimization. Besides, in the 6D and 8D cases, the smoothness and accuracy of the generated feasible boundaries are also shown. The performance of enforcing the generated constraint in optimization is also studied in all three cases, which indicates that the generated constraint functions are applicable in the optimization design of composite laminates.