Statistical modelling, dichotomization and non‐linearity

2021 ◽  
Author(s):  
Ibrahim Halil Tanboğa
Keyword(s):  
Statistical Modelling

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.

scholarly journals Cocktail of carbohydrases from Aspergillus niger: an economical and eco-friendly option for biofilm clearance from biopolymer surfaces

AMB Express ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arashdeep Kaur ◽  
Sanjeev Kumar Soni ◽  
Shania Vij ◽  
Praveen Rishi
Keyword(s):  
Aspergillus Niger ◽  
Laser Scanning ◽  
Statistical Modelling ◽  
Laser Scanning Microscopy ◽  
Enzyme Treatment ◽  
Confocal Laser ◽  
Abiotic Surfaces ◽  
Enzyme Cocktail ◽  
Natural Variant

AbstractBiofilm formation on both biotic and abiotic surfaces accounts for a major factor in spread of antimicrobial resistance. Due to their ubiquitous nature, biofilms are of great concern for environment as well as human health. In the present study, an integrated process for the co-production of a cocktail of carbohydrases from a natural variant of Aspergillus niger was designed. The enzyme cocktail was found to have a noteworthy potential to eradicate/disperse the biofilms of selected pathogens. For application of enzymes as an antibiofilm agent, the enzyme productivities were enhanced by statistical modelling using response surface methodology (RSM). The antibiofilm potential of the enzyme cocktail was studied in terms of (i) in vitro cell dispersal assay (ii) release of reducing sugars from the biofilm polysaccharides (iii) the effect of enzyme treatment on biofilm cells and architecture by confocal laser scanning microscopy (CLSM). Potential of the enzyme cocktail to disrupt/disperse the biofilm of selected pathogens from biopolymer surfaces was also assessed by field emission scanning electron microscopy (FESEM) analysis. Further, their usage in conjunction with antibiotics was assessed and it was inferred from the results that the use of enzyme cocktail augmented the efficacy of the antibiotics. The study thus provides promising insights into the prospect of using multiple carbohydrases for management of heterogeneous biofilms formed in natural and clinical settings.

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