Mass-number dependence of statistical model parameters and its impact on incomplete fusion fraction calculations

2021 ◽  
Vol 104 (3) ◽  
Author(s):  
Alpna Ojha ◽  
Sunita Gupta ◽  
Unnati Gupta ◽  
Pushpendra P. Singh ◽  
Abhishek Yadav ◽  
...  
1972 ◽  
Vol 3 (16) ◽  
pp. 673-675 ◽  
Author(s):  
A. Y. Abul-Magd ◽  
M. H. Simbel

2020 ◽  
Author(s):  
Boguslaw Usowicz ◽  
Jerzy Lipiec

<p>Soil organic carbon accumulation is central to the improvement of many soil properties and functions. Biochar use and management could be particularly beneficial for soils with low organic carbon content. It's known that many of soils in the world intrinsically exhibit little ability to retain water and nutrients due to their texture and mineralogy. Also, acquiring biomass for other than agricultural purposes can reduce the organic carbon accumulation and worsens the soil quality. Adding biochar to the soil can affect saturated hydraulic conductivity, water holding capacity and reduce soil erosion and mineral fertilization. It has been shown that saturated hydraulic conductivity depends on type of feedstock and pyrolysis temperatures used for biochar production and application dose but the results are inconsistent. Therefore, in order to explain the different biochar impacts, we propose in this study the use the physical-statistical model of B. Usowicz for predicting the saturated hydraulic conductivity using literature data for various soils amended with biochars (from woodchip, rice straw and dairy manure), pyrolyzed at 300, 500 and 700 °C.  </p><p>Soil with biochar and pores between them can be represented by a pattern (net) of more or less cylindrically interconnected channels with different capillary radius. When we view a porous medium as a net of interconnected capillaries, we can apply a statistical approach for the description of the liquid or gas flow. The soil and biochar phases and their configuration is decisive for pore distribution and the course of the water retention curve in this medium. The physical-statistical model considers the pore space as the capillary net that is represented by parallel and serial connections of hydraulic resistors in the layer and between the layers, respectively. The polynomial distribution was used in this model to determine probability of the occurrence of a given capillary configuration. Capillary size radii and the probability of occurrence of a given capillary configuration were calculated based on the measured water retention curve and saturated water content. It was found a good agreement between measured and the model-predicted hydraulic conductivity data for the biochar amended soils. It indicates that the used variables and model parameters to predict the saturated hydraulic conductivities of the soils were chosen correctly. The different types and pyrolysis temperatures of biochars affected the soil water retention and the equivalent length of the capillaries that characterize the pore tortuosity in the soil.</p><p> </p><p>Acknowledgements. Research was conducted under the project “Water in soil - satellite monitoring and improving the retention using biochar” no. BIOSTRATEG3/345940/7/NCBR/2017 which was financed by Polish National Centre for Research and Development in the framework of “Environment, agriculture and forestry” - BIOSTRATEG strategic R&D programme.</p>


1995 ◽  
Vol 48 (1) ◽  
pp. 125
Author(s):  
A.J Morton ◽  
DG Sargood

Nuclear reaction cross sections derived from statistical-model calculations have been used in the calculation of thermonuclear reaction rates for 36 nuclei at temperatures that are representative of the interiors of evolving stars and supernovae as nucleosynthesis approaches the production of nuclei with N = 28. The statistical-model calculations used optical-model parameters in the particle channels which had been selected to give the best overall agreement between theoretical and experimental cross sections for reactions on stable target nuclei in the mass and energy ranges of importance for the stellar conditions of interest. The optical-model parameters used, and the stellar reaction rates obtained, are tabulated. Comparisons are made between these stellar rates and those from other statistical-model calculations in the literature.


Author(s):  
MOHAMAD FOROUZANFAR ◽  
HAMID ABRISHAMI MOGHADDAM ◽  
SONA GHADIMI

Recently, the use of wavelet transform has led to significant advances in image denoising applications. Among wavelet-based denoising approaches, the Bayesian techniques give more accurate estimates. Considering interscale dependencies, these estimates become closer to the original image. In this context, the choice of an appropriate model for wavelet coefficients is an important issue. The performance can also be improved by estimating model parameters in a local neighborhood. In this paper, we propose the bivariate normal inverse Gaussian (NIG) distribution, which can model a wide range of heavy-tailed to less heavy-tailed processes, to model the local wavelet coefficients at adjacent scales. We will show that this new statistical model is superior to the conventional generalized Gaussian (GG) model. Then, a minimum mean square error-based (MMSE-based) Bayesian estimator is designed to effectively remove noise from wavelet coefficients. Exploiting this new statistical model in the dual-tree complex wavelet domain, we achieved state-of-the-art performance among related recent denoising approaches, both visually and in terms of peak signal-to-noise ratio (PSNR).


2000 ◽  
Vol 279 (3) ◽  
pp. E669-E683 ◽  
Author(s):  
Emery N. Brown ◽  
Yong Choe ◽  
Harry Luithardt ◽  
Charles A. Czeisler

We formulate a statistical model of the human core-temperature circadian rhythm in which the circadian signal is modeled as a van der Pol oscillator, the thermoregulatory response is represented as a first-order autoregressive process, and the evoked effect of activity is modeled with a function specific for each circadian protocol. The new model directly links differential equation-based simulation models and harmonic regression analysis methods and permits statistical analysis of both static and dynamical properties of the circadian pacemaker from experimental data. We estimate the model parameters by using numerically efficient maximum likelihood algorithms and analyze human core-temperature data from forced desynchrony, free-run, and constant-routine protocols. By representing explicitly the dynamical effects of ambient light input to the human circadian pacemaker, the new model can estimate with high precision the correct intrinsic period of this oscillator (∼24 h) from both free-run and forced desynchrony studies. Although the van der Pol model approximates well the dynamical features of the circadian pacemaker, the optimal dynamical model of the human biological clock may have a harmonic structure different from that of the van der Pol oscillator.


1989 ◽  
Vol 46 (5) ◽  
pp. 743-769 ◽  
Author(s):  
Jon T. Schnute ◽  
Laura J. Richards ◽  
Alan J. Cass

In this paper and its companion (Schnute et al. 1989, Can. J. Fish. Aquat. Sci. 46: 730–742), we present a statistical analysis of Schnute's size-structured model for exploited fish populations. Here we emphasize model parameters associated with survival and recruitment. We present a simple method of graphical analysis for assessing these parameters, and we develop a related statistical model, based on the modern theory of time series analysis. Our model incorporates a range of possible error structures that include both natural variability (process error) and data uncertainty (measurement error). We illustrate the model's application with data from an exploited lingcod (Ophiodon elongatus) stock. Preliminary graphical estimates of parameters agree remarkably well with final estimates from the statistical model. Where possible, we investigate alternative viewpoints associated with (1) choices of error structure, (2) dynamic and equilibrium versions of the model, (3) uncertain growth parameters, (4) different abundance indices, (5) mark–recapture experiments, and (6) availability of size-frequency data.


1981 ◽  
Vol 30 (1) ◽  
pp. 9-38 ◽  
Author(s):  
James S. Williams ◽  
Hariharan Iyer

A statistical model and analysis for genetic and environmental effects in twin-family data are presented. The model is used to derive expressions for phenotypic correlations of 22 essential pair relationships in twin-family units. The analysis proceeds in two steps. First, differential effects of sex, generation, and sex-zygosity of twin-family units and correlations due to cluster sampling are eliminated from correlation data. Then, estimates and tests of model parameters are calculated from the adjusted data. The theory and methods were developed for a Swedish twin-family study of many behaviors possibly related to the smoking habit. There, it is important to screen for behaviors that clearly are under genetic control and to assess relative influences of various biological and social environments on the development of all behaviors. Height data from the Swedish study are used to illustrate concepts and methods presented in this paper.


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