scholarly journals Influence of pilot-fuel mixing on the spatio-temporal progression of two-stage autoignition of diesel-sprays in low-reactivity ambient fuel-air mixture

2020 ◽  
Author(s):  
Rajavasanth Rajasegar ◽  
Yoichi Niki ◽  
Zheming Li ◽  
Jose Maria García-Oliver ◽  
Mark P.B. Musculus
Keyword(s):  
Diesel Sprays ◽  
Two Stage ◽  
Pilot Fuel ◽  
Fuel Mixing ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Progression of Two-Stage Autoignition for Diesel Sprays in a Low-Reactivity Ambient: n-Heptane Pilot-Ignited Premixed Natural Gas

2021 ◽  
Author(s):  
Rajavasanth Rajasegar ◽  
Yoichi Niki ◽  
Jose M Garcia-Oliver ◽  
Zheming Li ◽  
Mark Musculus
Keyword(s):  
Natural Gas ◽  
Diesel Sprays ◽  
Two Stage ◽  
Spatio Temporal

Two-stage Local Spatio-temporal Event Filter based on Adaptive Thresholds

2021 ◽  
Author(s):  
Luoxi Jing ◽  
Jun Luo ◽  
Dianxi Shi ◽  
Ruihao Li ◽  
Yuqi Zhu ◽  
...  
Keyword(s):  
Two Stage ◽  
Adaptive Thresholds ◽  
Spatio Temporal

scholarly journals Modelling spatio-temporal variation in exposure to particulate matter: a two-stage approach

2008 ◽  
Vol 19 (6) ◽  
pp. 549-566 ◽  
Author(s):  
T. R. Fanshawe ◽  
P. J. Diggle ◽  
S. Rushton ◽  
R. Sanderson ◽  
P. W. W. Lurz ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Temporal Variation ◽  
Two Stage ◽  
Spatio Temporal

scholarly journals Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China

2019 ◽  
Vol 11 (22) ◽  
pp. 2679 ◽  
Author(s):  
Kainan Zhang ◽  
Gerrit de Leeuw ◽  
Zhiqiang Yang ◽  
Xingfeng Chen ◽  
Xiaoli Su ◽  
...  
Keyword(s):  
Statistical Model ◽  
Cross Validation ◽  
Temporal Variations ◽  
Coefficient Of Determination ◽  
Prediction Errors ◽  
Two Stage ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Guanzhong Basin ◽  
Gwr Model

Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km × 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R2), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, −0.378 µg/m3, and 12.746 µg/m3, respectively, and the model cross-validation results were 0.703, 1.451 µg/m3, and 15.731 µg/m3, respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM2.5 concentrations in the Guanzhong Basin, and PM2.5 concentrations vary with the seasons. This method can provide reliable PM2.5 predictions to reduce the bias of exposure assessment in air pollution and health research.

TOOLS FOR EXPERIENTIAL RECOGNITION

1992 ◽  
Vol 01 (03) ◽  
pp. 427-461 ◽  
Author(s):  
PANOS A. LIGOMENIDES
Keyword(s):  
Decision Maker ◽  
Formal Description ◽  
Two Stage ◽  
One Dimensional ◽  
Human Expert ◽  
Sensory Data ◽  
Structural Identity ◽  
Perceptual Recognition ◽  
Perceptual Ability ◽  
Spatio Temporal

Our objective in the interactive formulation of the "formal description schema - fds" model is the modeling of the prototypical, i.e. the subjective, perceptual ability of a human "expert", the ultimate human or robotic decision maker. In this paper, we present our fds-approach and methodology for solving the problem of modeling and exercising perceptual recognition [3–6]. We limit our discussion to one-dimensional variational profiles. We view the fds-model as a two-stage procedural model. Concerning the "early" (pre-attentive) recognition stage, we define the "structural identity of a k-norm class, k∈K" — SkID — as a tool for quick shadowing of sensory data and positioning instantiations of sufficient resemblance to interactively pre-defined spatio–temporal norm classes. Attentive recognition tools follow for assessing conformity of SkID-pointed occurrences.

scholarly journals Correlation and studies of habitat selection: problem, red herring or opportunity?

2010 ◽  
Vol 365 (1550) ◽  
pp. 2233-2244 ◽  
Author(s):  
John Fieberg ◽  
Jason Matthiopoulos ◽  
Mark Hebblewhite ◽  
Mark S. Boyce ◽  
Jacqueline L. Frair
Keyword(s):  
Habitat Selection ◽  
Resource Selection ◽  
New Technologies ◽  
Animal Movement ◽  
Mixed Effects ◽  
Correlated Data ◽  
Two Stage ◽  
Wide Range ◽  
Spatio Temporal ◽  
Post Hoc

With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use–availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data.

Tailorable and programmable liquid-crystalline elastomers using a two-stage thiol–acrylate reaction

RSC Advances ◽  
2015 ◽  
Vol 5 (25) ◽  
pp. 18997-19001 ◽  
Author(s):  
C. M. Yakacki ◽  
M. Saed ◽  
D. P. Nair ◽  
T. Gong ◽  
S. M. Reed ◽  
...  
Keyword(s):  
Liquid Crystalline ◽  
Main Chain ◽  
Temporal Control ◽  
Two Stage ◽  
Spatio Temporal

A methodology is introduced to synthesize main-chain liquid-crystalline elastomers (LCEs) using a thiol–acrylate-based reaction. This method can program an aligned LCE monodomain and offer spatio-temporal control over liquid-crystalline behavior.

scholarly journals A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

2021 ◽  
Author(s):  
Diana M. P&eacuterez-Valencia ◽  
Mar&iacutea Xos&eacute Rodr&iacuteguez-&Aacutelvarez ◽  
Martin P. Boer ◽  
Lukas Kronenberg ◽  
Andreas Hund ◽  
...  
Keyword(s):  
High Throughput ◽  
Growth Curves ◽  
Temporal Analysis ◽  
Two Stage ◽  
Temporal Noise ◽  
Large Sets ◽  
Field Phenotyping ◽  
Corrected Data ◽  
High Throughput Phenotyping ◽  
Spatio Temporal

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.

scholarly journals Spatio-temporal evolution of diesel sprays at the early start of injection

2017 ◽  
Vol 205 ◽  
pp. 391-398 ◽  
Author(s):  
Radboud Pos ◽  
Robert Wardle ◽  
Roger Cracknell ◽  
Lionel Ganippa
Keyword(s):  
Temporal Evolution ◽  
Diesel Sprays ◽  
Early Start ◽  
Start Of Injection ◽  
Spatio Temporal
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