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Effects of Pressure and Moisture Content on Bulk Density of Triticale Grain under Compaction
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Highlights

Compaction of triticale grain with three moisture contents (8%, 12%, and 16% wet basis) was measured at five applied pressures (0, 7, 14, 34, and 55 kPa).

Bulk density increased with increasing pressure for all moisture contents and was significantly (p < 0.0001) dependent on both moisture content and applied pressure.

A Verhulst logistic equation was found to model the changes in bulk density of triticale grain with R2 of 0.986.

The model showed similar behavior to that of wheat and rye, indicating that the results of this study can be used with the methods of ASABE Standard S413 to predict the quantity of triticale grain stored in bins.

Abstract. The objective of this study was to determine the combined effects of moisture content (MC) and pressure on the changes in bulk density of triticale grain under compaction at conditions typical of those seen in storage structures and to develop mathematical models to describe the compression behavior. Triticale compaction was measured at three MCs (8%, 12%, and 16% wet basis) and four compaction pressures (7, 14, 34, and 55 kPa) using a square metal box based on the design used in an earlier study by Thompson and Ross. Data from the compaction tests were used to calculate bulk densities for the three MCs and four pressures. Bulk densities were found to be significantly (p < 0.0001) dependent on both MC and pressure. Bulk densities varied with increasing MC, as has been observed in similar studies for other agricultural grains such as rye and wheat. These results provide guidance for estimating the bulk density of triticale in bins and other storage structures. The Verhulst logistic equation was found to best describe the changes in bulk density of triticale caused by rearrangement of the grain kernels at lower pressures for the three MCs. At higher pressures, the grain was observed to be more compliant, and Hooke’s law was used to accurately describe the observed changes. Data from the compaction tests were used to estimate the model parameters, with a correlation coefficient (R2) of 0.986. The model was then used in WPACKING to compare the results of this study to pack factor predictions for triticale and wheat. WPACKING is a computer program that is the basis for ASABE Standard S413. The results of this comparison showed that this method can be used with the methods of ASABE Standard S413 to predict the quantity of triticale grain stored in bins. Keywords: Bulk density, Interaction, Moisture content, Pressure, Triticale, Verhulst logistic equation.

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Sun Mar 07 2010
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Publication Date
Tue Sep 11 2018
Journal Name
Iraqi Journal Of Physics
The effects of silica addition on the structural, electrical and mechanical characteristics of MgAl2O4 spinel ceramic phase
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Publication Date
Wed Jun 15 2022
Journal Name
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Effects of various analgesics on pain perception and rate of tooth movement: a randomized controlled clinical study
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Sun Jan 01 2023
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The Effects of Different Water Types on the Dissolution, Physical Appearance, and Compressive Strength of Gypsum Rock
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Sun Dec 10 2017
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The speech communication btween the shape and content in commercial advertising
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Publication Date
Fri Jul 01 2011
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25th International Cartographic Conference
User generated content and formal data sources for integrating geospatial data
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Today, problems of spatial data integration have been further complicated by the rapid development in communication technologies and the increasing amount of available data sources on the World Wide Web. Thus, web-based geospatial data sources can be managed by different communities and the data themselves can vary in respect to quality, coverage, and purpose. Integrating such multiple geospatial datasets remains a challenge for geospatial data consumers. This paper concentrates on the integration of geometric and classification schemes for official data, such as Ordnance Survey (OS) national mapping data, with volunteered geographic information (VGI) data, such as the data derived from the OpenStreetMap (OSM) project. Useful descriptions o

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Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

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