Checking linear hypothesis in factor evaluation by use of trend-surface gradients in the investigation of eolian deposits

1970 ◽  
Vol 2 (3) ◽  
pp. 231-240 ◽  
Author(s):  
M. A. Romanova
Keyword(s):  
Linear Hypothesis ◽  
Trend Surface ◽  
Factor Evaluation ◽  
Eolian Deposits

Modeling a random sequence of extremes of loads for fatigue tests under irregular loading

2020 ◽  
pp. 13-19
Author(s):  
N.A. Mahutov ◽  
I.V. Gadolina ◽  
S.G. Lebedinskiy ◽  
E.S. Oganyan ◽  
A.A. Bautin
Keyword(s):  
Random Sequence ◽  
Fatigue Tests ◽  
Linear Hypothesis ◽  
Resource Estimation ◽  
Transition Matrices ◽  
Random Loading ◽  
Markov Transition ◽  
Random Nature ◽  
Irregular Loading ◽  
Markov Transition Matrices

Methods and approaches to tests under random loading are considered, their role is characterized. To ensure the random nature of loading, a modeling method based on Markov transition matrices and real processes recorded in operation is proposed. Keywords: random loading process, Markov repetition matrices, resource estimation, corrected linear hypothesis, parameter of completeness of the loading spectrum. [email protected]

Stratigraphy of the Colorado Creek Mammoth Locality, Alaska

1992 ◽  
Vol 37 (2) ◽  
pp. 214-228 ◽  
Author(s):  
Robert M. Thorson ◽  
R. Dale Guthrie
Keyword(s):  
Regional Climate ◽  
Slope Aspect ◽  
Proxy Record ◽  
Radiocarbon Dates ◽  
West Central ◽  
Permafrost Table ◽  
Eolian Deposits ◽  
Paleoclimatic Proxy

AbstractThe Colorado Creek mammoth locality in west-central Alaska contains the remains of two mammoths that were scavenged by carnivores. Sedimentologic interpretations of the reworked eolian deposits surrounding the bones, supplemented by 10 radiocarbon dates, indicate that the lower and upper mammoths died and were buried within separate, but superimposed, thaw gullies about 23,000 and 16,000 yr ago, respectively. From our results, we propose a polycyclic taphonomic model for thaw gullies governed largely by slope aspect, rather than regional climate, and in which mixing between faunal horizons is more likely than not. Variations in the rate of silt influx and the position of the permafrost table provide a paleoclimatic proxy record that can be correlated to other records in eastern Beringia.

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 651 ◽  
Author(s):  
John Triantafilis ◽  
Scott Mitchell Lesch ◽  
Kevin La Lau ◽  
Sam Mostyn Buchanan
Keyword(s):  
Regression Model ◽  
Cation Exchange Capacity ◽  
Spatial Regression ◽  
Soil Management ◽  
Exchange Capacity ◽  
Ancillary Data ◽  
Trend Surface ◽  
Spatial Regression Model ◽  
Field Level ◽  
Mlr Model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

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