Reedy model categories

1999 ◽  
pp. 353-387 ◽  
Author(s):  
Paul G. Goerss ◽  
John F. Jardine
Keyword(s):  
Model Categories

scholarly journals On the construction of functorial factorizations for model categories

2013 ◽  
Vol 13 (2) ◽  
pp. 1089-1124 ◽  
Author(s):  
Tobias Barthel ◽  
Emily Riehl
Keyword(s):  
Model Categories

Model Categories

2009 ◽  
pp. 65-138
Author(s):  
Paul G. Goerss ◽  
John F. Jardine
Keyword(s):  
Model Categories

scholarly journals The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jin Xu ◽  
Chao Yi
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Effective Theory ◽  
Standard Errors ◽  
Limited Data ◽  
Analysis Model ◽  
Multiple Objects ◽  
Model Categories ◽  
Model Expression

Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

scholarly journals Obstruction theory in model categories

2004 ◽  
Vol 181 (2) ◽  
pp. 396-416 ◽  
Author(s):  
J.Daniel Christensen ◽  
William G. Dwyer ◽  
Daniel C. Isaksen
Keyword(s):  
Obstruction Theory ◽  
Model Categories

Cost Model Categories

2015 ◽  
pp. 611-647
Author(s):  
Caesar Wu ◽  
Rajkumar Buyya
Keyword(s):  
Cost Model ◽  
Model Categories

Towards a nudging of stratospheric overshoots in the 3D mesoscale BRAMS model

2021 ◽  
Author(s):  
Justine Pichon ◽  
Emmanuel Riviere ◽  
Abhinna Behera ◽  
Jeremie Burgalat
Keyword(s):  
Mesoscale Model ◽  
Water Concentration ◽  
Global Scale ◽  
Satellite Measurement ◽  
Model Categories ◽  
Tropical Regions ◽  
Water Ice ◽  
Convective Clouds ◽  
Starting Point ◽  
Ice Water

<p>Water repartition in the stratosphere is a key compound in the atmospheric chemical and<br>radiative equilibrium. Since the 80’s, an increase of the water concentration in the<br>stratosphere has been observed.This presence in the stratosphere can be explained by the<br>slow ascent of air mass above convective clouds in tropical regions. The amount of water<br>vapor entering in the stratosphere depends on the coldest temperature and countered<br>during this slow ascent because it can lead to ice cristal formation that sediment and<br>dehydrate the air masses. But some other processes may contribute to the stratospheric<br>water budget, especially to explain the increase of water vapor. Stratospheric overshoots<br>phenomenon can take part in the stratospheric hydratation, by injecting directly water ice in<br>the stratosphere. Injected ice water, by sublimation, will hydrate stratosphere locally. The<br>local role of overshoots is better known but their contributions at the global scale steal need<br>to be quantified. In order to estimate this contribution, previous studies have used the 3D<br>simulation mesoscale model BRAMS to show overshoot impact in the upper Tropical<br>Tropopause Layer (TTL). These studies are the starting point of our study.</p><p>The aim of this paper is to present the new development inside BRAMS to nudge<br>stratospheric ice injection by overshoots. It uses an overshoot occurrence climatology from<br>MHS (Microwave Humidity Sounder) satellite measurement. Ice injection in the model is<br>made according to ice model categories previously shown to be present in the overshoot<br>plumes with ratios already diagnosed in previous studies. Ice injection is made between two<br>layers of TTL’s stratospheric part: between 380 and 385K and between 385 et 400K. Nudging<br>is triggered only if, in the grid mesh (20 x 20 km) where MHS has detected an overshoot,<br>BRAMS computes a cumulonimbus with a top above 13.5km. For the layer above 385 K<br>isentrope, a subgrid box of 2 km x 2 km is considered for the computation of ice injection.<br>Sensibility test of this nudging scheme will be presented in this presentation. </p>

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