Exploring a valid model for the variogram of an isotropic spatial process

2004 ◽  
Vol 18 (6) ◽  
pp. 366-376 ◽  
Author(s):  
D. S. Maglione ◽  
A. M. Diblasi
Keyword(s):  
Spatial Process ◽  
Valid Model

The rat fundus, a valid model to characterize agents acting at human 5-HT29 receptors

2001 ◽  
Vol 120 (5) ◽  
pp. A208-A208
Author(s):  
E WEBER ◽  
M BERNHARD ◽  
H PFANNKUCHE
Keyword(s):  
Valid Model

scholarly journals Factors Affecting Indonesian Preservice Teachers’ Use of ICT During Teaching Practices Through Theory of Planned Behavior

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110275
Author(s):  
Farrah Dina Yusop ◽  
Akhmad Habibi ◽  
Rafiza Abdul Razak
Keyword(s):  
Preservice Teachers ◽  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Teaching Practices ◽  
Partial Least Square ◽  
Equation Modeling ◽  
Valid Model ◽  
Information And Communication

The goals of the research were to inform whether the theory of planned behavior (TPB) is a valid model to explain Indonesian preservice teachers’ (PSTs) use of information and communication technology (UICT) during teaching practices, to report best factor affecting PSTs’ UICT during teaching practices, and to elaborate differences regarding UICT during teaching practices in terms of demographic information, gender, major, university, and information and communication technology (ICT)-based courses. The sample of this study was 1,133 PSTs from three Indonesian universities who completed a 24-item printed questionnaire of four constructs: behavioral beliefs (BB), normative beliefs (NB), control beliefs (CB), and UICT. Using partial least square–structural equation modeling (PLS-SEM), the results informed that the TPB is a valid model to help explain Indonesian PSTs’ UICT during teaching practices. All constructs (BB, NB, and CB) significantly predict UICT during teaching practices where NB is reported to be the strongest predictor (β = .354). There are no significant differences in terms of genders and ICT-based courses; however, significant differences are indicated in terms of majors and universities.

scholarly journals Transposition of Tn5367 in Mycobacterium marinum, Using a Conditionally Recombinant Mycobacteriophage

2003 ◽  
Vol 185 (5) ◽  
pp. 1745-1748 ◽  
Author(s):  
Jan Rybniker ◽  
Martina Wolke ◽  
Christiane Haefs ◽  
Georg Plum
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Skin Infection ◽  
Model Organism ◽  
Transposon Mutagenesis ◽  
Mycobacterium Marinum ◽  
Close Relative ◽  
Mutant Library ◽  
Human Pathogen ◽  
Valid Model ◽  
Highly Efficient

ABSTRACT Mycobacterium marinum is a close relative of the obligate human pathogen Mycobacterium tuberculosis. As with M. tuberculosis, M. marinum causes intracellular infection of poikilothermic vertebrates and skin infection in humans. It is considered a valid model organism for the study of intracellular pathogenesis of mycobacteria. Low transformation efficiencies for this species have precluded approaches using mutant libraries in pathogenesis studies. We have adapted the conditionally replicating mycobacteriophage phAE94, originally developed as a transposon mutagenesis tool for M. tuberculosis, to meet the specific requirements of M. marinum. Conditions permissive for phage replication in M. tuberculosis facilitated highly efficient transposon delivery in M. marinum. Using this technique we succeeded in generating a representative mutant library of this species, and we conclude that TM4-derived mycobacteriophages are temperature-independent suicide vectors for M. marinum.

scholarly journals Asymptotic Normality of a Nonparametric Conditional Quantile Estimator for Random Fields

2011 ◽  
Vol 2011 ◽  
pp. 1-35
Author(s):  
Sidi Ali Ould Abdi ◽  
Sophie Dabo-Niang ◽  
Aliou Diop ◽  
Ahmedoune Ould Abdi
Keyword(s):  
Asymptotic Normality ◽  
Random Fields ◽  
Quantile Function ◽  
Spatial Process ◽  
Kernel Estimate ◽  
Conditional Quantile ◽  
Conditional Quantile Estimator ◽  
Conditional Quantile Function ◽  
Mixing Sequence ◽  
Explicative Variable

Given a stationary multidimensional spatial process , we investigate a kernel estimate of the spatial conditional quantile function of the response variable given the explicative variable . Asymptotic normality of the kernel estimate is obtained when the sample considered is an -mixing sequence.

scholarly journals Spatial Data Analysis in epidemiology of sensorineural hearing loss

2020 ◽  
Vol 19 (4) ◽  
pp. 13-20
Author(s):  
A. A. Korneenkov ◽  
◽  
S. V. Ryazantsev ◽  
I. V. Fanta ◽  
E. E. Vyazemskaya ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Information Systems ◽  
Spatial Analysis ◽  
Sensorineural Hearing Loss ◽  
Spatial Data ◽  
Sensorineural Hearing ◽  
Spatial Data Analysis ◽  
Point Pattern ◽  
Spatial Process ◽  
One Year

The identification of risk factors, features and patterns of the emergence and spread of diseases in space requires a large array of diverse data and the use of a serious mathematical and statistical apparatus. The distribution of diseases in space is studied using spatial analysis tools, which are now widely used as information systems are introduced and data are accumulated that are relevant to public health. For most tasks of working with spatial data (data, events that have geographical, spatial coordinates), various geographic information systems are used. As a disease for spatial analysis, sensorineural hearing loss was chosen, with which patients were treated at the Saint-Petersburg Research of Ear, Throat, Nose and Speech during one year of the study. The main tasks of the spatial analysis of data on the incidence of sensorineural hearing loss (SNHL) for hospitalization were: visualization of a point pattern, which can form the geographical coordinates of the places of residence of inpatients with SNHL; assessment of the properties of the spatial process that generates this point image (assessment of the intensity of the process, its laws) using various statistical indicators; testing the hypothesis about the spatial randomness of this process and the influence of individual factors on it. R-code accompanied all calculations in the article. Calculations can be reproduced quite easily. The text of the article can be used as step-by-step instructions for their implementation.

Application and comparison of spatial models in analyzing tree-genetics field trials

1990 ◽  
Vol 20 (5) ◽  
pp. 536-546 ◽  
Author(s):  
Steen Magnussen
Keyword(s):  
Classical Model ◽  
Substantial Reduction ◽  
Spatial Models ◽  
Tree Height ◽  
Process Models ◽  
Spatial Process ◽  
Spatial Analyses ◽  
Spatial Covariance ◽  
Significant Difference ◽  
Tree Genetics

Tree height of jack pine full-sib families, originating from all possible combinations of three parental provenances and growing on three sites, was analyzed with 1 classical model and 11 nearest-neighbour spatial process models. Extension of the classical linear model with spatial interaction terms was deemed necessary in light of significant neighbourhood correlations among effect-free observations (residuals) on two of the three sites. The strength and extent of spatial and temporal correlations are demonstrated in both visual and tabular form. Only 4 of the 11 spatial models provided a substantial reduction (5–20%) in the significant difference between two estimates of full-sib family tree height. Spatial adjustments averaged 1–3% at the family level, with few families adjusted by more than 10%. The cumulative (temporal) effect of spatial covariance was demonstrated in rank correlations between adjusted and observed family means. No simple trends were obtained when adjusted variance components and heritabilities were compared with their unadjusted counter-parts, but most models tended to deflate genetic effects and reduce heritabilities. It is concluded that although spatial analyses provide an attractive tool for the experimenter, the lack of a cause and effect hypothesis in forest genetic trials necessitates model searching without the guarantee of true treatment effects. Spatial analysis provides good indicators of the need to collect additional site information for more powerful analyses. Careful planning and intensive site preparation may greatly reduce spatial covariances and the need for spatial analyses.

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