Constant Coefficient Model Research Articles

Thomson/Joule Power Compensation and the Measurement of the Thomson Coefficient.

The energy transported by the electric current that circulates a thermoelectric element (TE) varies with position due to the Joule and Thomson effects. The Thomson effect may enhance or compensate the Joule effect. A method for measuring the Thomson coefficient of a TE is presented. This method is based on the total compensation of the Joule and Thomson effects. The electric current then flows without delivering power to the TE or absorbing power from it. For a TE, the global Thomson/Joule compensation ratio Φ¯T/J is defined as the ratio of the power absorbed by the current due to the Thomson effect and the power delivered by the current to the TE due to the Joule effect. It can be expressed as Φ¯T/J=I0/I, where I is the electric current and I0 is the zero-power current, a quantity that is proportional to the average Thomson coefficient. When I=I0, the Thomson effect exactly compensates the Joule effect and the net power delivered by the current to the TE is zero. Since the power delivered by the current is related to the temperature distribution, temperature measurements for currents around I0 can be used as the basis for a measurement technique of the Thomson coefficient. With varying current, the difference between the temperature at the center of the TE and the mean temperature between its extremes reverses its sign at the zero-power current, I=I0. This observation suggests the possibility of measuring the Thomson coefficient, but a quantitative analysis is needed. With calculations using the constant transport coefficients model for Bi2Te0.94Se0.063 and Bi0.25Sb0.752Te3, it is theoretically shown that a null temperature detector with a sensitivity of the order of 1 mK allows for the accurate determination of the Thomson coefficient.

Inverse Scattering Integrability and Fractional Soliton Solutions of a Variable-Coefficient Fractional-Order KdV-Type Equation

In the field of nonlinear mathematical physics, Ablowitz et al.’s algorithm has recently made significant progress in the inverse scattering transform (IST) of fractional-order nonlinear evolution equations (fNLEEs). However, the solved fNLEEs are all constant-coefficient models. In this study, we establish a fractional-order KdV (fKdV)-type equation with variable coefficients and show that the IST is capable of solving the variable-coefficient fKdV (vcfKdV)-type equation. Firstly, according to Ablowitz et al.’s fractional-order algorithm and the anomalous dispersion relation, we derive the vcfKdV-type equation contained in a new class of integrable fNLEEs, which can be used to describe the dispersion transport in fractal media. Secondly, we reconstruct the potential function based on the time-dependent scattering data, and rewrite the explicit form of the vcfKdV-type equation using the completeness of eigenfunctions. Thirdly, under the assumption of reflectionless potential, we obtain an explicit expression for the fractional n-soliton solution of the vcfKdV-type equation. Finally, as specific examples, we study the spatial structures of the obtained fractional one- and two-soliton solutions. We find that the fractional soliton solutions and their linear, X-shaped, parabolic, sine/cosine, and semi-sine/semi-cosine trajectories formed on the coordinate plane have power–law dependence on discrete spectral parameters and are also affected by variable coefficients, which may have research value for the related hyperdispersion transport in fractional-order nonlinear media.

Study on Friction Characteristics of AA7075 Aluminum Alloy under Pulse Current-Assisted Hot Stamping

Friction during contact between metals can be very complex in pulse current-assisted forming. Based on stamping process characteristics, a reciprocating friction tester was designed to study the friction characteristics between AA7075 aluminum alloy and P20 steel under different current densities. Origin software was used to process the experimental data, and a current friction coefficient model was established for the pulse current densities. The results show that the friction coefficient of the aluminum alloy sheet decreased with the increase in the pulse current density (2–10 A/mm2). After that, the friction mechanism was determined by observing microscopic morphology and SEM: some oxide cracked on the friction surface when the current was large. Finally, finite element simulations with Abaqus software and a cylindrical case validated the constant and current friction coefficient models. The thickness distribution patterns of the fixed friction coefficient and the current coefficient model were compared with an actual cylindrical drawing part. The results indicate that the new current friction model had a better fit than the fixed one. The simulation results are consistent with the actual verification results. The maximum thinning was at the corner of the stamping die, which improved the simulation accuracy by 7.31%. This indicates the effectiveness of the pulse current friction model.

Validation of turbulent heat transfer models against eddy covariance flux measurements over a seasonally ice-covered lake

Abstract. In this study we analyzed turbulent heat fluxes over a seasonal ice cover on a boreal lake located in southern Finland. Eddy covariance (EC) flux measurements of sensible (H) and latent heat (LE) from four ice-on seasons between 2014 and 2019 are compared to three different bulk transfer models: one with a constant transfer coefficient and two with stability-adjusted transfer coefficients: the Lake Heat Flux Analyzer and SEA-ICE. All three models correlate well with the EC results in general while typically underestimating the magnitude and the standard deviation of the flux in comparison to the EC observations. Differences between the models are small, with the constant transfer coefficient model performing slightly better than the stability-adjusted models. Small difference in temperature and humidity between surface and air results in low correlation between models and EC. During melting periods (surface temperature T0>0 ∘C), the model performance for LE decreases when compared to the freezing periods (T0<0 ∘C), while the opposite is true for H. At low wind speed, EC shows relatively high fluxes (±20 W m−2) for H and LE due to non-local effects that the bulk models are not able to reproduce. The complex topography of the lake surroundings creates local violations of the Monin–Obukhov similarity theory, which helps explain this counterintuitive result. Finally, the uncertainty in the estimation of the surface temperature and humidity affects the bulk heat fluxes, especially when the differences between surface and air values are small.

Panel Data Modeling for Indian Food Grain Production

The present investigation was carried out to study the food grain production trends in selected states of India for the period 2001-02 to 2020-2021. In this study a panel data regression model is used to combat the complicated relations and strong auto-correlation present in the crop production time series data. Panel data modelling control the heterogeneity of cross-sectional units over time. The results reveal that between state-to-state food grain production is highly significant, the highest food grain production was registered in Uttar Pradesh, followed by Punjab and Madhya Pradesh. Very lowest was registered in Kerala and Himachal Pradesh. Levin, Lin & Chu t statistics values are found to be significant indicating that the variable under study is stationary at level and hence the variable under study is I(0).Among the three different models viz., constant coefficient model, fixed effect model and random effect model, the fixed effect model was selected as an appropriate trend model as per the Hausman test. Based on the statistical significance of the estimated coefficients and the substantial increase in the R2 value to 95%, the fixed effect model or the least-square dummy variable regression model performs better than the panel least square regression model. The Hausman test confirms the selection of fixed effect over the random effect model. The fixed effects are positive in Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Gujarat, Chhattisgrah, Bihar, Haryana and Madhya Pradesh. Negative fixed effects are observed in Himachal Pradesh, Jharkhand, Maharashtra, Odisha, Punjab, Rajasthan, Uttar Pradesh, Uttarakhand and West Bengal. Increases in food grain production have been observed

SOLITARY AND LUMP WAVES INTERACTION IN VARIABLE-COEFFICIENT NONLINEAR EVOLUTION EQUATION BY A MODIFIED ANSÄTZ WITH VARIABLE COEFFICIENTS

<abstract> In this work, we examine variable-coefficient nonlinear evolution equations that often describe complex physical models more than constant coefficient models. A modified ansätz with variable coefficients is used for studying the solitary and lump waves interaction in these variable-coefficient nonlinear evolution equations. We discuss the variable-coefficient Kadomtsev-Petviashvili equation to achieve this goal. We present lump wave and interaction solutions between solitary and lump waves for this model. By choosing appropriate values of the variable coefficients, 3d plots and corresponding contour plots are drawn to illustrate the dynamical behaviors of the obtained solutions. </abstract>

An Improved UKF Algorithm for Extracting Weak Signals Based on RBF Neural Network

It is difficult to extract effective weak signals from complex noise environment in many engineering applications. To address the problem, an improved unscented Kalman filter (UKF) algorithm based on a radial basis function (RBF)-neural network (NN) is proposed in this paper. This algorithm achieves effective extraction of weak signals under the condition of low signal-to-noise ratio (SNR). It uses RBF-NN to establish a distributed system of state and observation models to replace the traditional UKF modeling method. Then, the RBF-NN is applied to build a constant coefficient model to modify the UKF filtering deviation value, and thus, the accuracy of the algorithm is improved. Finally, simulation experiments are conducted at different SNRs of surface nuclear magnetic resonance signals. Results show that effective weak signals can still be obtained when SNR = -10 dB, verifying the effectiveness of the algorithm.

An Empirical Study of National Education and Economic Development Based on a Variable Coefficient Model

This paper studies the relationship between the level of national education and economic development in 31 provinces and cities in China from 2012 to 2019. Principal component analysis is used to comprehensively evaluate the level of national education, on the basis of which a variable coefficient model is tested and a constant coefficient model is established for comparison. The results show that: (1) there is a co-integration relationship between the level of national education and economic development, i.e. the two are in equilibrium in the long run. (2) In some provinces and cities in China, economic development is positively correlated with the level of national education, and some provinces and cities are negatively correlated. (3) Economic development has different effects on the level of national education in the east, middle and west. (4) Economic development and national education level are positively correlated in the average sense in China.

Induction Motor Modelling for Load-Frequency Control System

The accuracy of the load-frequency control (LFC) system depends on the accuracy of models used for the components that affect the frequency response. In the conventional LFC system, the frequency-sensitive loads, including induction motors, are simply modeled by the load-damping coefficient. The purpose of this paper is to obtain the fifth-, third- and first-order induction motors models for applying in the LFC system. Also, in this paper, the performance and accuracy of these models on the frequency response in a small-scale stand-alone microgrid are compared. In addition, the effect of system and motor parameters on the frequency response characteristics are investigated. The simulation results point out that the commonly used constant load-damping coefficient model cannot always appropriately reflect the impact of the induction motors on the frequency response. In addition, it is shown that a constant load-damping coefficient cannot present an accurate model for all induction motors when a significant disturbance has occurred in the low-inertia microgrids.

Mixing matters

ABSTRACT All hydrodynamical simulations of turbulent astrophysical phenomena require sub-grid scale models to properly treat energy dissipation and metal mixing. We present the first implementation and application of an anisotropic eddy viscosity and metal mixing model in Lagrangian astrophysical simulations, including a dynamic procedure for the model parameter. We compare these two models directly to the common Smagorinsky and dynamic variant. Using the mesh-free finite mass method as an example, we show that the anisotropic model is best able to reproduce the proper Kolmogorov inertial range scaling in homogeneous, isotropic turbulence. Additionally, we provide a method to calibrate the metal mixing rate that ensures numerical convergence. In our first application to cosmological simulations, we find that all models strongly impact the early evolution of galaxies, leading to differences in enrichment and thermodynamic histories. The anisotropic model has the strongest impact, with little difference between the dynamic variant and the constant-coefficient variant. We also find that the metal distribution functions in the circumgalactic gas are significantly tighter at all redshifts, with the anisotropic model providing the tightest distributions. This is contrary to a recent study that found metal mixing to be relatively unimportant on cosmological scales. In all of our experiments, the constant-coefficient Smagorinsky and anisotropic models rivalled their dynamic counterparts, suggesting that the computationally inexpensive constant-coefficient models are viable alternatives in cosmological contexts.

Nonlinear dynamics of deep water subsea lifting operations considering KC-dependent hydrodynamic coefficients

The dynamics of deep water subsea lifting operations considering hydrodynamic coefficients that depend on the Keulegan-Carpenter (KC) number are analysed in this study. Firstly, experimental data from the literature is presented for a typical subsea manifold, relating the added mass and drag coefficients to the amplitude of oscillation, represented by the KC number. Then, the nonlinear non-dimensional equation of motion, that considers the variable hydrodynamic coefficients, is presented. The solution of this equation is obtained via the harmonic balance method and by iterative time domain integration, and the results are compared to those of a conventional model with constant hydrodynamic coefficients. The results obtained via the harmonic balance method are considered almost as accurate as from the time domain integration, but require significantly less computational effort. Also, it is shown that the amplitude-dependent model predicts variations in the natural frequency and damping of the system as a function of the amplitude of the response of the payload. This results in significant differences in the maximum cable tension and the payload depth at which it occurs, compared with the constant hydrodynamic coefficient model. Hence this shows the importance of considering variable hydrodynamic coefficients when analysing subsea lifting systems.

Evaluation of ultrasound and microbubbles effect on pork meat during brining process

In this study combined effect of ultrasound-induced acoustic cavitation and microbubbles during meat brining on pork loin (Longissimus dorsi) was evaluated. Cylindrical shape (diameter 15 mm, height 80 mm) pork loin samples were cut and immersed in 200 g L-1 NaCl brine and treated with the following brining methods for 180 min: static brining (SB), ultrasound assisted brining (US) and ultrasound combined with microbubbles in brine (USMB). Ultrasound was generated with 20 kHz frequency, 5,09 W/cm2 maximum intensity and 100 W maximum power. Microbubbles in brine were produced by a gas–liquid mixing pump. Effect of ultrasound and microbbubles on NaCl content and diffusion in pork loin, mass balance, water binding capacity (WBC), protein denaturation and meat tissue microstructure were evaluated. The US and USMB brinings enhanced the NaCl diffusion into meat compared to meat brined under static conditions. The constant diffusion coefficient (D) model precisely described the NaCl diffusion kinetics during brinings. The ultrasound and microbbubles resulted in microscopic pores on the surface of myofibers. Decreasing WBC was observed for all brining methods. Myosin was not detectable in any of the brining methods. Denaturation temperature of actin showed a decreasing tendency with increasing brining time independently the brining methods.

A Numerical Study on the Effect of Variable Wear Coefficient on Fretting Wear Characteristics.

Fretting wear is a common phenomenon that happens between contact parts when there is an oscillatory relative movement. To investigate wear characteristics history in the fretting process, the finite element method (FEM) is commonly applied to simulate the fretting by considering the wear in the model. In most literature publications, the wear coefficient is considered as a constant, which is not a real case based on the experimental results. To consider the variation of wear coefficient, a double-linear model is applied in this paper, and the tribologically transformed structure (TTS) phase is considered in the study of the wear coefficient variation model. By using these models for variable wear coefficient for both flat and cylinder, the difference of wear characteristics, plastic strain, and stress between variable wear coefficient model (VWCM) and constant wear coefficient model (CWCM) are analyzed. The results show that the variable wear coefficient has no significant effect on the wear characteristic at the end of the process in the gross sliding regime. However, in the partial slip regime, the effect of variable wear coefficient on wear characteristics is significant. Due to the difference in contact geometry in the fretting process between VWCM and CWCM, the tangential and shear stress and equivalent plastic strain also show differences during the fretting process.

Carleman estimates for a class of variable coefficient degenerate elliptic operators with applications to unique continuation

&lt;p style='text-indent:20px;'&gt;In this paper, we obtain new Carleman estimates for a class of variable coefficient degenerate elliptic operators whose constant coefficient model at one point is the so called Baouendi-Grushin operator. This generalizes the results obtained by the two of us with Garofalo in [&lt;xref ref-type="bibr" rid="b10"&gt;10&lt;/xref&gt;] where similar estimates were established for the "constant coefficient" Baouendi-Grushin operator. Consequently, we obtain: (ⅰ) a Bourgain-Kenig type quantitative uniqueness result in the variable coefficient setting; (ⅱ) and a strong unique continuation property for a class of degenerate sublinear equations. We also derive a subelliptic version of a scaling critical Carleman estimate proven by Regbaoui in the Euclidean setting using which we deduce a new unique continuation result in the case of scaling critical Hardy type potentials.&lt;/p&gt;

Time-varying equity premium forecasts based on industry indexes

Various studies report that the ability of industry indexes to predict the broad market disappeared during the most recent years. I revisit this theme using more flexible switching models and imposing economically motivated constraints on the predictions. My results show that traditional constant coefficients linear models are unable to forecast the stock market over the period considered, but restricting the equity premium to be non-negative, five industries predict the market. I also show that the Markov-switching models exhibit a dismal performance, which is even worse than the ones from the constant coefficients model. Finally, I test a model with two regimes- recession and expansion- which are identified in real-time through the Arouba-Diebold-Scotti Business Conditions Index. Using this model, I find that 8 out of 33 industries can successfully forecast the market. Furthermore, a mean-variance investor who bases his decisions on it obtains sizeable utility gains, relative to another investor who uses, exclusively, the historical returns.

Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models

The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying parameter (TVP) predictive regression should be employed in each out-of-sample forecasting step. This newly developed method has the advantages of both model shrinkage and automatic switch between CC and TVP forecasting models; thus, this may produce more accurate predictions of crude oil prices. The empirical results show that this hierarchical shrinkage model can outperform many commonly used forecasting benchmark methods, such as AR, unobserved components stochastic volatility (UCSV), and multivariate regression models in forecasting crude oil price on various forecasting horizons.

Economic role of education in agriculture: evidence from rural Vietnam

PurposeThis paper investigates the impact of education on output of rice farming households in Vietnam.Design/methodology/approachGiven the literature review, this paper specifies three empirical models (i.e. linear constant coefficient model, partially nonlinear model and linear varied coefficient model) with variables that well describe the mechanism through which education affects output. The data were collected from 901 rice farming households randomly selected out of ten provinces and city in the Mekong River Delta (MRD) of Vietnam. The models are estimated using ordinary least squares (OLS) and Robinson's (1988) double residual estimators.FindingsEstimates of the empirical models show that seed, fertilizer, labor and farm size have significant impacts on output of rice farming households while pesticide and herbicide do not. Education is also found to have a positive effect on output of rice farming households because it helps them better manage farms of larger size via combining various inputs in a more desirable way.Originality/valueThis paper confirms the positive impact of education on agricultural output, which implies that policies aiming to provide better education to rural people will greatly enhance their income as well as trigger long-term economic and agricultural growth.

Propagation properties of chirped optical similaritons with dual-power law nonlinearity

Abstract We study the transmission of femtosecond light waves in an inhomogeneous dual-power law nonlinear model under the influence of self-frequency shift effect. Pulse evolution in such system is described by a generalized higher-order nonlinear Schrodinger equation incorporating the contributions of distributed self-frequency shift, dispersion, dual-power law nonlinearity, and gain/loss. As a result, a variety of analytical similaritons are derived for the first time by a similarity transformation connected to the related constant-coefficients model. We found that in the presence of self-frequency shift effect, the optical similaritons acquire a nonlinear chirp which has a power-law dependence on the intensity of the pulse. The evolution behaviors of presented chirped similaritons are also presented in two different soliton control systems.

Survival and recurrence after intraperitoneal chemotherapy use: Retrospective review of ovarian cancer hospital registry data.

BackgroundIntraperitoneal/intravenous chemotherapy (IP/IV) was associated with improved survival for ovarian cancer (OC) patients in several randomized clinical trials. However, the uptake of IP/IV in clinical practice is varied due to conflicting evidence about its impact on survival and recurrence. The aim of this study was to explore the uptake of IP/IV treatment and to evaluate its impact on survival and recurrence in OC patients.MethodsDemographic and clinical information on OC patients (N = 2916) who underwent treatment for OC between 2000 and 2017 was obtained from the large healthcare system cancer registry. Duplicate records, grade 1, rare (eg, gelatinous carcinoma), and non‐epithelial (eg, granulosa cell carcinoma) tumors were excluded. Kaplan‐Meier survival curves were constructed to compare 5‐ and 10‐year survival based on the chemotherapy type, surgery type, and stage. Multivariable Gray's piecewise constant time‐varying coefficient models were fitted to evaluate the effect of IP/IV on adjusted hazard ratio (AHR) of OC survival and recurrence adjusting for potential confounders.ResultsThe final sample consisted of 1846 OC patients, 14% (250/1846) of which received IP/IV chemotherapy. IP/IV was significantly associated with improved 10‐year survival (P < .001). Multivariable Gray's model demonstrated that IP/IV therapy significantly reduced the AHR of death (AHR = 0.39‐1.07, P < .001) with the beneficial effect gradually declining over time. Use of IP/IV chemotherapy had no impact on OC recurrence.ConclusionsThese findings demonstrated that only a small fraction of eligible patients underwent IP/IV chemotherapy. We report a significant 10‐year survival, but not necessarily recurrence benefit is associated with IP/IV chemotherapy compared to IV only, suggesting the need for novel ways of identifying patients who may benefit from IP/IV chemotherapy.

Forecasting Volatility with Time-Varying Coefficient Regressions

We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC). The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models. By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods. Portfolio exercises also demonstrate the superiority of time-varying coefficient models.