A homogenization‐based state‐dependent model for gap‐graded granular materials with fine‐dominated structure

2021 ◽  
Author(s):  
X. S. Shi ◽  
Jidong Zhao ◽  
Yufeng Gao
Keyword(s):  
Granular Materials ◽  
State Dependent ◽  
Dependent Model

scholarly journals Model error in weather forecasting

2001 ◽  
Vol 8 (6) ◽  
pp. 357-371 ◽  
Author(s):  
D. Orrell ◽  
L. Smith ◽  
J. Barkmeijer ◽  
T. N. Palmer
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Error ◽  
Local Model ◽  
State Dependent ◽  
Rapid Divergence ◽  
Dependent Model

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

scholarly journals Ecology and Prediction of Compensatory Growth: From Theory to Application in Forestry

2021 ◽  
Vol 12 ◽  
Author(s):  
Chao Li ◽  
Hugh Barclay ◽  
Bernard Roitberg ◽  
Robert Lalonde
Keyword(s):  
Compensatory Growth ◽  
Population Level ◽  
Human Society ◽  
Common Phenomenon ◽  
Partial Mortality ◽  
Individual Level ◽  
State Dependent ◽  
Dependent Model ◽  
Accelerated Growth ◽  
The Individual

Compensatory growth has been observed in forests, and it also appears as a common phenomenon in biology. Though it sometimes takes different names, the essential meanings are the same, describing the accelerated growth of organisms when recovering from a period of unfavorable conditions such as tissue damage at the individual level and partial mortality at the population level. Diverse patterns of compensatory growth have been reported in the literature, ranging from under-, to compensation-induced-equality, and to over-compensation. In this review and synthesis, we provide examples of analogous compensatory growth from different fields, clarify different meanings of it, summarize its current understanding and modeling efforts, and argue that it is possible to develop a state-dependent model under the conceptual framework of compensatory growth, aimed at explaining and predicting diverse observations according to different disturbances and environmental conditions. When properly applied, compensatory growth can benefit different industries and human society in various forms.

scholarly journals Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters

2020 ◽  
Vol 13 (4) ◽  
pp. 64 ◽  
Author(s):  
Pietro Coretto ◽  
Michele La Rocca ◽  
Giuseppe Storti
Keyword(s):  
Mixture Model ◽  
Likelihood Estimation ◽  
Risk Groups ◽  
Gaussian Mixture ◽  
Cross Sectional ◽  
The Arts ◽  
State Dependent ◽  
Empirical Performance ◽  
Dependent Model ◽  
The Cross

The inhomogeneity of the cross-sectional distribution of realized assets’ volatility is explored and used to build a novel class of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. The inhomogeneity of the cross-sectional distribution of realized volatility is captured by a finite Gaussian mixture model plus a uniform component that represents abnormal variations in volatility. Based on the cross-sectional mixture model, at each time point, memberships of assets to risk groups are retrieved via maximum likelihood estimation, as well as the probability that an asset belongs to a specific risk group. The latter is profitably used for specifying a state-dependent model for volatility forecasting. We propose novel GARCH-type specifications the parameters of which act “clusterwise” conditional on past information on the volatility clusters. The empirical performance of the proposed models is assessed by means of an application to a panel of U.S. stocks traded on the NYSE. An extensive forecasting experiment shows that, when the main goal is to improve overall many univariate volatility forecasts, the method proposed in this paper has some advantages bover the state-of-the-arts methods.

scholarly journals Predicting pathogenicity behavior in Escherichia coli population through a state dependent model and TRS profiling

2018 ◽  
Vol 14 (1) ◽  
pp. e1005931 ◽  
Author(s):  
Krzysztof Bartoszek ◽  
Marta Majchrzak ◽  
Sebastian Sakowski ◽  
Anna B. Kubiak-Szeligowska ◽  
Ingemar Kaj ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
State Dependent ◽  
Dependent Model

Evaluating the Predictive Ability of a Path-Dependent Thermal Inactivation Model for Salmonella Subjected to Prior Sublethal Heating in Ground Turkey, Beef, and Pork

2013 ◽  
Vol 76 (2) ◽  
pp. 220-226 ◽  
Author(s):  
M. I. TENORIO-BERNAL ◽  
B. P. MARKS ◽  
E. T. RYSER ◽  
A. M. BOOREN
Keyword(s):  
Thermal Inactivation ◽  
Predictive Ability ◽  
The State ◽  
Slow Heating ◽  
Holding Period ◽  
State Dependent ◽  
Dependent Model ◽  
Meat Species ◽  
Beef Round ◽  
Path Dependent

Pathogen thermal inactivation models currently available to and used by industry consider only the present state of the product when predicting inactivation rates. However, bacteria subjected to sublethal thermal injury can develop partial protection against lethal temperatures. The objective of this study was to extend the capabilities of a previously published path-dependent Salmonella inactivation model by accounting for longer sublethal heating periods and different substrates and to test this new model against independent data. Ground samples of irradiated (>10 kGy) turkey breast, beef round, and pork loin were inoculated with an eight-serovar Salmonella cocktail and subjected to 53 nonisothermal treatments (in triplicate) that combined a linear heating rate (1, 2, 3, 4, or 7 K/min), a variable length sublethal holding period (at 40, 45, or 50°C), a lethal holding temperature (55, 58, 61, or 64°C), and a nominal target kill (3- or 5-log reductions) (n = 159 for each meat species). When validated against nonisothermal data from similar treatments, traditional state-dependent model predictions resulted in root mean squared errors (RMSEs) of 2.9, 2.2, and 4.6 log CFU/g for turkey, beef, and pork, respectively. RMSEs for the new path-dependent model were 0.90, 0.81, and 0.82 log CFU/g for the same species, respectively, with reductions in error of 63 to 82% relative to the state-dependent model. This new path-dependent model can significantly reduce error from the state-dependent model and could become a useful tool for assuring product safety, particularly relative to slow heating processes.

scholarly journals Earthquake dynamics constrained from laboratory experiments: new insights from granular materials

2021 ◽  
Vol 64 (4) ◽  
pp. SE441
Author(s):  
Andrea Bizzarri ◽  
Alberto Petri ◽  
Andrea Baldassarri
Keyword(s):  
Granular Materials ◽  
Laboratory Experiments ◽  
Seismic Waves ◽  
Laboratory Data ◽  
Numerical Approach ◽  
Earthquake Dynamics ◽  
Friction Behavior ◽  
Dynamic Propagation ◽  
State Dependent ◽  
Basic Features

The traction evolution is a fundamental ingredient to model the dynamics of an earthquake rupture which ultimately controls, during the coseismic phase, the energy release, the stress redistribution and the consequent excitation of seismic waves. In the present paper we explore the use of the friction behavior derived from laboratory shear experiments performed on granular materials at low normal stress. We find that the rheological properties emerging from these laboratory experiments can not be described in terms of preexisting governing models already presented in literature; our results indicate that neither rate–and state–dependent friction laws nor nonlinear slip–dependent models, commonly adopted for modeling earthquake ruptures, are able to capture all the features of the experimental data. Then, by exploiting a novel numerical approach, we directly incorporate the laboratory data into a code to simulate the fully dynamic propagation of a 3–D slip failure. We demonstrate that the rheology of the granular material, imposed as fault boundary condition, is dynamically consistent. Indeed, it is able to reproduce the basic features of a crustal earthquake, spontaneously accelerating up to some terminal rupture speed, both sub– and supershear.

TONE RECOGNITION OF CONTINUOUS MANDARIN SPEECH BASED ON HIDDEN MARKOV MODEL

1994 ◽  
Vol 08 (01) ◽  
pp. 233-246 ◽  
Author(s):  
YIH-RU WANG ◽  
JYH-MING SHIEH ◽  
SIN-HORNG CHEN
Keyword(s):  
Lower Layer ◽  
Recognition Rate ◽  
Model Performance ◽  
Acoustic Features ◽  
State Dependent ◽  
Sentence Level ◽  
Tone Recognition ◽  
Dependent Model ◽  
F0 Contour ◽  
Tone Discrimination

In this paper, several tone recognition schemes for continuous Mandarin speech are discussed. First, an SCHMM is used to model the acoustic features of a syllable for tone discrimination. Parameters extracted from the F0 and energy contours of the syllable by discrete Legendre orthonormal transform are used as the recognition features. Then, a scheme using two-layer network is proposed to cope with the difficulty resulting from the declination effect on the F0 contour of the declarative sentential utterance. The declination effect is modeled by a sentence-level HMM on the upper layer and the acoustic features of each tone are modeled by a state-dependent SCHMM on the lower layer. Lastly, the coarticulation effect coming from neighboring syllables is considered in the scheme using context-dependent model. Performance of these recognition schemes was examined by simulations. A recognition rate of 86.34% was achieved.

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