scholarly journals A Survey of Factors on Design for Manufacture of Chemical Fertilizer Granulating Machine

2021 ◽  
Vol 6 (4) ◽  
pp. 124-128
Author(s):  
J. A. Ikimi ◽  
W. E. Odinikuku ◽  
T. B. Adeleke
Keyword(s):  
National Policy ◽  
Principal Component ◽  
Factor Loading ◽  
Chemical Fertilizer ◽  
Data Matrix ◽  
Sufficient Evidence ◽  
Small Scale ◽  
Principal Factors ◽  
Coefficient Of Concordance ◽  
Influential Variable

The design for manufacturing of granulating machines to produce fertilizer granules in small scale using locally available materials is often challenging and this results in low fertilizer usage among Nigerian farmers when compared with the world’s average usage. A lot of factors are associated with chemical fertilizer granulating machine, and it is necessary to examine and understand the interplay among these factors. This study weighs up a number of variables that relates with the design and usage of chemical fertilizer granulating machine and offers increased insight and awareness about their insidiousness. The study employed a survey approach, using the Rensis Likert’s attitudinal scale, to generate respondents’ data matrix that was analyzed with Principal Component Analysis (PCA), and which was facilitated by statistiXL software. Kendall’s Coefficient of Concordance (KCC) was used to rank the thirty two (32) identified variables and PCA was thereafter deployed to ascertain the degree of interplay among the variables. Results obtained by KCC suggested that judges ranking were consistent as there was sufficient evidence to reject the null hypothesis Also, PCA was indicating parsimony in data reduction from 32 variables to mere five. The result established five principal factors creatively labeled Miscellany Components, Technical Considerations, Granulation Efficiency, Agricultural National policy and Biophysical Elements. The most influential variable by its factor loading of 0.894 is Agricultural National policy. A gamut of variables which seem to affect chemical fertilizer granulating machine has been examined. This has helped in discerning similarities in dissimilarities.

scholarly journals Statistical Research of Intersection of Technology and Entrepreneurship to Modern Practices, Policies and Promises

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Peter O. Emereje ◽  
C. U. Okolie ◽  
Tunde B. Adeleke
Keyword(s):  
Principal Component ◽  
Factor Loading ◽  
Entrepreneurship Policy ◽  
Statistical Research ◽  
Factors Affecting ◽  
Number Of Factors ◽  
Principal Factors ◽  
Coefficient Of Concordance ◽  
Matrix Eigenvalues ◽  
Influential Variable

There are many factors affecting the relationship between entrepreneurship and technology which has not been examined and studied and this has been a daunting problem for researchers in this area. This paper seeks to identify a number of factors that deal with technology and entrepreneurship with a view to understanding the inter-correlation among the identified factors thereby making us to know the intersection between them. It will help provide an overview of the state of the art in terms of technology and offers fresh insights for entrepreneurship policy for technology. This study employed Kendall’s Coefficient of Concordance (KCC) to rank the 32 identified variables and subsequently apply Principal Component Analysis (PCA. KCC was used to rank 32 identified variables in descending order of importance. Furthermore, the PCA was used to analyze a set of questionnaire crafted with the 32 variables and administered to knowledgeable respondents in the area. The outputs gotten from the statistical software include descriptive statistic, correlation matrix, eigenvalues, eigenvector, varimax rotated factor loadings, explained variance and factor plot, among others and thereafter interpretation was given. Result obtained unveiled five principal factors which were labeled creatively. Results obtained by KCC suggested that judges ranking were consistent. Also, PCA was indicating parsimony in data   reduction from 32 variables to just five. The most influential variable by its factor loading of 0.954 is innovation. The import of this is that innovation which has the highest factor loading is the nexus between technology and entrepreneurship and should therefore be embraced.

Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

scholarly journals Farm Size, Risk Aversion and Overuse of Fertilizer: The Heterogeneity of Large-Scale and Small-Scale Wheat Farmers in Northern China

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 111
Author(s):  
Haixia Wu ◽  
Hantao Hao ◽  
Hongzhen Lei ◽  
Yan Ge ◽  
Hengtong Shi ◽  
...  
Keyword(s):  
Risk Aversion ◽  
Large Scale ◽  
Northern China ◽  
Fertilizer Application ◽  
Chemical Fertilizer ◽  
Farm Size ◽  
Small Scale ◽  
Fertilizer Use ◽  
Deviation Degree ◽  
Small Scale Farmers

The excessive use of fertilizer has resulted in serious environmental degradation and a high health cost in China. Understanding the reasons for the overuse of fertilizer is critical to the sustainable development of Chinese agriculture, and large-scale operation is considered as one of the measures to deal with the excessive fertilizer use. Under the premise of fully considering the resource endowment and heterogeneity of large-scale farmers and small-scale farmers in production and management, different production decision-making frameworks were constructed. Based on the 300 large-scale farmers and 480 small-scale farmers in eight provinces of northern China wheat region, we analyzed the optimal fertilizer use amount and its deviation as well as the influencing factors of small-scale and large-scale farmers, then further clarified whether the development of scale management could solve the problem of excessive fertilizer use. The empirical results show that: (1) both small-scale farmers and large-scale farmers deviated from the optimal fertilizer application amount, where the deviation degree of optimal fertilizer application of small-scale farmers is significantly higher than that of large-scale farmers, with a deviation degree of 35.43% and 23.69% for small and large scale farmers, respectively; (2) not all wheat growers in North China had the problem of excessive use of chemical fertilizer, as the optimal level of chemical fertilizer application in Heilongjiang and Inner Mongolia are 346.5 kgha−1 and 335.25 kgha−1, while the actual fertilizer use amount was 337.2 kgha−1 and 324.6 kgha−1, respectively; and (3) the higher the risk aversion level, farmers tended to apply more fertilizer to ensure grain output. Therefore, increasing farm size should be integrated into actions such as improving technological innovation and providing better information transfer to achieve the goal of zero-increase in Chinese fertilizer use.

scholarly journals The Development of Athlete Performance Capabilities Index (APCI) Model for Male U12 Player Selection using Multivariate Analysis

2018 ◽  
Vol 7 (4.34) ◽  
pp. 97
Author(s):  
Mohamad Razali Abdullah ◽  
Hafizan Juahir ◽  
N. Mohamad Shukri ◽  
N. A. Fuat ◽  
N. A. Mohd Ros ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Factor Score ◽  
Principal Component ◽  
Factor Loading ◽  
Group Classification ◽  
Performance Group ◽  
Two Factors ◽  
Low Performance ◽  
High Factor Loading ◽  
Athlete Performance

This study develops an Athlete Performance Capabilities Index (APCI) model using multivariate analysis for selecting the best player of under twelve (U12).  Measurement of anthropometrics and physical fitness were evaluated among 178 male players aged 12±0.52 years. Factor score derived by Principal Component Analysis were used to obtain a model for APCI and Discriminant Analysis (DA) were conducted to validate the correctness of group classification by APCI. Result was found two factors with eigenvalues greater than 1 were extracted which accounted for 62.00% of the variations present in the original variables. The two factors were used to obtain the factor score coefficients explained by 35.72% and 26.67% of the variations in athlete performance respectively. Factor 1 revealed high factor loading on fitness compared to Factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing APCI ≥ 75%, 25% ≤ APCI < 75% and APCI < 25% as high, moderate and low performance group respectively. Three discriminated variables out of thirteen variables were obtained using Forward and Backward stepwise mode of DA, which were weight, standing broad jump, and 40 meters’ speed. Such variables were established as essential indicator for selecting the best player among male U12.   

FFT Calculation of the L1-norm Principal Component of a Data Matrix

2021 ◽  
pp. 108286
Author(s):  
Stefania Colonnese ◽  
Panos P. Markopoulos ◽  
Gaetano Scarano ◽  
Dimitris A. Pados
Keyword(s):  
Principal Component ◽  
Data Matrix ◽  
L1 Norm

Triticum polonicum L. as potential source material for the biofortification of wheat with essential micronutrients

2018 ◽  
Vol 17 (03) ◽  
pp. 213-220 ◽  
Author(s):  
Teresa Bieńkowska ◽  
Elżbieta Suchowilska ◽  
Wolfgang Kandler ◽  
Rudolf Krska ◽  
Marian Wiwart
Keyword(s):  
Durum Wheat ◽  
Common Wheat ◽  
Principal Component ◽  
Small Scale ◽  
Wheat Varieties ◽  
Triticum Polonicum ◽  
Iron And Zinc ◽  
Modern Wheat ◽  
Wheat Lines ◽  
Source Materials

AbstractThe grain of modern wheat cultivars has a significantly lower mineral content, including the content of copper, iron, magnesium, manganese, phosphorous, selenium and zinc. For this reason cereal breeders, are constantly searching for new genetic sources of minerals that are essential in human nutrition. Triticum polonicum, which is grown on a small scale in Spain, southern Italy, Algeria, Ethiopia and warm regions of Asia, deserves special attention in this context. The micronutrient and macronutrient content of T. polonicum versus T. durum and T. aestivum was compared in this study. Polish wheat grain was characterized by the significantly highest content of phosphorus (4.55 g/kg), sulphur (1.82 g/kg), magnesium (1.42 g/kg), zinc (49.5 mg/kg), iron (39.1 mg/kg) and boron (0.56 mg/kg) as well as a low content of aluminium (only 1.04 mg/kg). The macronutrient profile of most T. polonicum lines differed completely from that of common wheat and durum wheat. The principal component analysis supported discrimination of seven Polish wheat lines with a particularly beneficial micronutrient profile (P2, P3, P5, P7, P9, P22 and P25). These lines were characterized by the highest content of copper, iron and zinc, as well as the lowest concentrations of strontium, aluminium and barium which are undesirable in food products. The above lines can be potentially applied as source materials for breeding new wheat varieties. The results of this study indicate that Polish wheat could be used in genetic biofortification of durum wheat and common wheat.

Granite Weathering

2006 ◽  
Author(s):  
Piotr Migon
Keyword(s):  
Solid Rock ◽  
Chemical Change ◽  
Transitional Zone ◽  
Sufficient Evidence ◽  
Small Scale ◽  
Bare Rock ◽  
Moisture Deficit ◽  
Geomorphic Features ◽  
Drying Up ◽  
Physical And Chemical

Weathering is a necessary precursor for landform development. However, in the context of granite it acquires a particular importance for various reasons. First, many granite terrains show an extensive development of deep weathering profiles, which can be extremely varied in terms of their depth, vertical zonation, degree of rock decomposition, and mineralogical and chemical change. Moreover, the transitional zone between the weathering mantle and the solid rock, for which the term ‘weathering front’ is used (Mabbutt, 1961b), may be very thin. There is now sufficient evidence that many geomorphic features of granite landscapes, including boulders, domes, and plains, have been sculpted at the solid rock/weathering mantle interface and they are essentially elements of an exposed weathering front. Therefore, the origin of granite landscapes cannot be satisfactorily explained and understood without a proper understanding of the phenomenon of deep weathering. Second, granites break down via a range of weathering mechanisms, both physical and chemical, which interact to produce an extreme diversity of small-scale surface features and minor landforms. In this respect, it is only limestones and some sandstones which show a similar wealth of weathering-related surface phenomena. Third, both superficial and deep weathering of granite act very selectively, exploiting a variety of structural and textural features, including fractures, microfractures, veins, enclaves, and textural inhomogeneities. In effect, the patterns of rock breakdown may differ very much between adjacent localities, and so the resultant landforms differ. In the context of deep weathering, selectivity is evident in significant changes of profile thickness and its properties over short distances, and in the presence of unweathered compartments (corestones) within an altered rock mass. Fourth, it is emphasized that granites are particularly sensitive to the amount of moisture in the environment (Bremer, 1971; Twidale, 1982). They alter very fast in moist environments, whereas moisture deficit enhances rock resistance and makes it very durable. Hence, a bare rock slope shedding rainwater and drying up quickly after rain will be very much immune to weathering, whereas at its foot a surplus of moisture will accelerate decomposition.

scholarly journals Near-Infrared Spectroscopy Coupled Chemometric Algorithms for Rapid Origin Identification and Lipid Content Detection of Pinus Koraiensis Seeds

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4905
Author(s):  
Hongbo Li ◽  
Dapeng Jiang ◽  
Jun Cao ◽  
Dongyan Zhang
Keyword(s):  
Lipid Content ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component ◽  
Two Dimensions ◽  
Breeding Value ◽  
Pinus Koraiensis ◽  
Small Scale ◽  
Important Indicator ◽  
Chemometric Algorithms

Lipid content is an important indicator of the edible and breeding value of Pinus koraiensis seeds. Difference in origin will affect the lipid content of the inner kernel, and neither can be judged by appearance or morphology. Traditional chemical methods are small-scale, time-consuming, labor-intensive, costly, and laboratory-dependent. In this study, near-infrared (NIR) spectroscopy combined with chemometrics was used to identify the origin and lipid content of P. koraiensis seeds. Principal component analysis (PCA), wavelet transformation (WT), Monte Carlo (MC), and uninformative variable elimination (UVE) methods were used to process spectral data and the prediction models were established with partial least-squares (PLS). Models were evaluated by R2 for calibration and prediction sets, root mean standard error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). Two dimensions of input data produced a faster and more accurate PLS model. The accuracy of the calibration and prediction sets was 98.75% and 97.50%, respectively. When the Donoho Thresholding wavelet filter ‘bior4.4’ was selected, the WT–MC–UVE–PLS regression model had the best predictions. The R2 for the calibration and prediction sets was 0.9485 and 0.9369, and the RMSECV and RMSEP were 0.0098 and 0.0390, respectively. NIR technology combined with chemometric algorithms can be used to characterize P. koraiensis seeds.

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