<div> The study aimed to identify the optimum pyrolysis temperature and cooling conditions for producing biochar from industrial hemp stalks to enhance asphalt binder performance, while also assessing the potential of industrial hemp stalks as a viable alternative in the pavement sector by evaluating the temperature sensitivity and the high and low temperature performance of biochar-modified binders. The asphalt binders were subjected to conventional and performance tests to assess their properties. Penetration and softening point values of neat and biochar modified binders were determined and the penetration index of binders was calculated. The temperature susceptibility, permanent deformation, aging and fatigue performances of neat and biochar modified asphalt binders were determined by the dynamic shear rheometer test. Bending beam rheometer equipment was used to determine the low temperature performance of asphalt binders. The experimental results showed that biochar obtained from industrial hemp stalk improved aging resistance, reduced the temperature susceptibility, increased high-temperature performance grade, and so developed permanent deformation resistance of the binder. The optimal production conditions for biochar, utilized in the modification of asphalt binders to achieve better rutting resistance, have been identified as a combination of a pyrolysis temperature of 300°C and rapid cooling. The low-temperature performance grade of biochar-modified asphalt binders decreased by one grade, while the high-temperature performance grade increased by at least one grade. The addition of biochar was shown to have a negative impact on the binder’s low-temperature performance. Based on these findings, it was concluded that the biochar additive, derived from industrial hemp stalk, is particularly well-suited for regions characterized by hot climates. </div>

Cited by 0Open access

Statistical model results.

Lena Kretz (10325749), Katinka Koll (10325752), Carolin Seele-Dilbat (10325755) et al. · Figshare · 2021

Cited by 0Open access

A Statistical Model

Stephen E. Fienberg, David C. Hoaglin, William Kruskal et al. · Springer series in statistics · 1990

Cited by 41Open access

Statistical Model Checking

Benoît Barbot, Serge Haddad, Claudine Picaronny et al. · 2012

Abstract. Statistical model-checking is an alternative verification technique applied on stochastic systems whose size is beyond numerical analysis ability. Given a model (most often a Markov chain) and a formula, it provides a confidence interval for the probability that the model satisfies the formula. One of the main limitations of the statistical approach is the computation time explosion triggered by the evaluation of very small probabilities. In order to solve this problem we develop a new approach based on importance sampling and coupling. The corresponding algorithms have been implemented in our tool COSMOS. We present experimentation on several relevant systems, with estimated time reductions reaching a factor of 10 −120.

Cited by 0

Statistical Model Building

Jürgen Hedderich, Lothar Sachs · 2024

Cited by 0

Statistical Potential Model

Shigeru Machida, Mikio Namiki, Ichiro Ohba · Progress of Theoretical Physics · 1967

The statistical potential model for high energy elastic scattering is proposed in order to account for large angle scattering in a unified framework describing the real part of the forward elastic scattering amplitude. The whole amplitude is considered to be decomposed into two parts: one is the pure imaginary shadow scattering amplitude and the other the statistical potential scattering amplitude. The statistical potential scattering amplitude T<e> is formulated under the assumption that many scattering centers are produced when the central collision occurs, and that the distribution of these scattering centers is determined by a distribution function subject to some statistic3.1 law. The amplitude T<r:> can be treated in analogy to the theory of neutron scattering by crystal. Some empirical formulas can be reproduced from rather natural assumptions on the correlation function of the above system of scattering centers. ~ l. Introduction In high energy jJp elastic scattering where the momentum of the incoming proton exceeds, say, 10 Ge VIc, we see clearly two distinct regions of scattering angles. One is the forward scattering region, where -t<l.O (Ge VI c)\ t being the square of the invariant momentum transfer, and the other is the region of intermediate and large angle scattering, where -t>l.O(GeVIc)

Cited by 2Open access

Parametric Statistical Model

George ThomasKurian · The Encyclopedia of Political Science · 2011

Cited by 0

Statistical Channel Model

Luigi Paura · WORLD SCIENTIFIC eBooks · 2025

Cited by 0

Statistical Substance Model

WORLD SCIENTIFIC eBooks · 2016

Cited by 0

The Statistical Model

Harold L. Cole · Oxford University Press eBooks · 2020

This chapter covers some of the basics of time series statistics as they relate to our model. An analytic solution to our model is derived with log preferences. Then the chapter discusses how to program up the model and compute sample statistic from model simulations. Finally it discusses Monte Carlo simulations to determine the sampling distributions for statistics from our model simulations.

Cited by 0

Statistical model outputs.

Dasamal Tharanga Fernando (9368326), Angela Clapperton (10918163), Janneke Berecki-Gisolf (462648) · Figshare · 2022

Cited by 0Open access

STATISTICAL MODEL SELECTION

Graeme Hutcheson · WORLD SCIENTIFIC eBooks · 2012

AbstractStatistical models are basic features of quantitative analysis and are commonly reported in academic articles and books to provide evidence of relationships and processes that are of importance in the population. Interpreting these models is, however, rarely straight forward as estimates for the model parameters and significance levels are dependent upon the methods used to build them, the so-called ‘variable selection problem’. This chapter investigates how parameter estimates and significance values may be related to the methods used for their construction and uses simulated and real-world data to demonstrate that modelsmaynot always provide valid indications of ‘actual’ relationships and processes in the population. In particular, the use of subset-selection methods may enable variables that are not important in the population to enter into models simply due to chance. In addition to this, the validity of conclusions drawn from the research may be affected by the use of single-model selection, which forces analysts to report just one instance of what might be a multimodel solution. It is proposed that many published reports and papers (including undergraduate and postgraduate projects) interpret statistical models that are invalid as little or no consideration is taken as to how they were constructed or whether single-solution reporting is appropriate. This chapter aims to demonstrate these problems and proposes a solution in the form of a modelling procedure that uses a restricted-set of variables and a reporting method which enables multiple-models to be described.

Cited by 0

Statistical model error

Armia Ahmadi-Hadad · Kidney International · 2023

Cited by 1Open access

Statistical model results.

Lena Kretz (10325749), Katinka Koll (10325752), Carolin Seele-Dilbat (10325755) et al. · Figshare · 2021

<p>Residuals of sediment on the vegetation and underneath the vegetation explained by the measured variables.</p> <p>(DOCX)</p>

Cited by 0Open access