scholarly journals Statistical Modelling of CSP Solving Algorithms Performance

2005 ◽  
pp. 861-861
Author(s):  
Ramon Béjar ◽  
Cèsar Fernández ◽  
Carles Mateu
Keyword(s):  
Statistical Modelling ◽  
Csp Solving

Climate change and food security in tropical West Africa – a dynamic-statistical modelling approach

Erdkunde ◽  
2008 ◽  
Vol 62 (2) ◽  
pp. 101-115 ◽  
Author(s):  
Heiko Paeth ◽  
Arcade Capo-Chichi ◽  
Wilfried Endlicher
Keyword(s):  
Climate Change ◽  
Food Security ◽  
West Africa ◽  
Statistical Modelling ◽  
Modelling Approach

scholarly journals Optimization Issues of a Hammer Mill Working Process Using Statistical Modelling

2021 ◽  
Vol 13 (2) ◽  
pp. 973
Author(s):  
Gigel Paraschiv ◽  
Georgiana Moiceanu ◽  
Gheorghe Voicu ◽  
Mihai Chitoiu ◽  
Petru Cardei ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Optimization Problem ◽  
Specific Energy Consumption ◽  
Statistical Modelling ◽  
Hammer Mill ◽  
Objective Functions ◽  
Working Process ◽  
Rotor Spinning ◽  
Milled Material ◽  
Different Types

Our paper presents the hammer mill working process optimization problem destined for milling energetic biomass (MiscanthusGiganteus and Salix Viminalis). For the study, functional and constructive parameters of the hammer mill were taken into consideration in order to reduce the specific energy consumption. The energy consumption dependency on the mill rotor spinning frequency and on the sieve orifices in use, as well as on the material feeding flow, in correlation with the vegetal biomass milling degree was the focus of the analysis. For obtaining this the hammer mill was successively equipped with 4 different types of hammers that grind the energetic biomass, which had a certain humidity content and an initial degree of reduction ratio of the material. In order to start the optimization process of hammer mill working process, 12 parameters were defined. The objective functions which minimize hammer mill energy consumption and maximize the milled material percentage with a certain specific granulation were established. The results obtained can serve as the basis for choosing the optimal working, constructive, and functional parameters of hammer mills in this field, and for a better design of future hammer mills.

scholarly journals Statistical modelling of the cosmological dispersion measure

2021 ◽  
Vol 502 (2) ◽  
pp. 2615-2629
Author(s):  
Ryuichi Takahashi ◽  
Kunihito Ioka ◽  
Asuka Mori ◽  
Koki Funahashi
Keyword(s):  
Dark Matter ◽  
Correlation Function ◽  
Probability Distribution ◽  
Power Spectrum ◽  
Free Electron ◽  
Statistical Modelling ◽  
Dispersion Measure ◽  
Host Galaxies ◽  
Angular Power Spectrum ◽  
Bias Factor

ABSTRACT We have investigated the basic statistics of the cosmological dispersion measure (DM)—such as its mean, variance, probability distribution, angular power spectrum, and correlation function—using the state-of-the-art hydrodynamic simulations, IllustrisTNG300, for the fast radio burst cosmology. To model the DM statistics, we first measured the free-electron abundance and the power spectrum of its spatial fluctuations. The free-electron power spectrum turns out to be consistent with the dark matter power spectrum at large scales, but it is strongly damped at small scales (≲  Mpc) owing to the stellar and active galactic nucleus feedback. The free-electron power spectrum is well modelled using a scale-dependent bias factor (the ratio of its fluctuation amplitude to that of the dark matter). We provide analytical fitting functions for the free-electron abundance and its bias factor. We next constructed mock sky maps of the DM by performing standard ray-tracing simulations with the TNG300 data. The DM statistics are calculated analytically from the fitting functions of the free-electron distribution, which agree well with the simulation results measured from the mock maps. We have also obtained the probability distribution of source redshift for a given DM, which helps in identifying the host galaxies of FRBs from the measured DMs. The angular two-point correlation function of the DM is described by a simple power law, $\xi (\theta) \approx 2400 (\theta /{\rm deg})^{-1} \, {\rm pc}^2 \, {\rm cm}^{-6}$, which we anticipate will be confirmed by future observations when thousands of FRBs are available.

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