Building Protein Folds Using Distance Geometry: Towards a General Modeling and Prediction Method

1994 ◽  
pp. 165-192 ◽  
Author(s):  
William R. Taylor ◽  
András Aszódi
Keyword(s):  
Distance Geometry ◽  
Prediction Method ◽  
Protein Folds ◽  
Modeling And Prediction

scholarly journals A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM2.5 Removal Modes on a Micro-Temporal Scale

2021 ◽  
Vol 13 (19) ◽  
pp. 11022
Author(s):  
Tingchen Wu ◽  
Xiao Xie ◽  
Bing Xue ◽  
Tao Liu
Keyword(s):  
Air Quality ◽  
Southern China ◽  
Prediction Method ◽  
Temporal Scale ◽  
Atmospheric Environment ◽  
Quantitative Modeling ◽  
Observation Data ◽  
Important Indicator ◽  
Modeling And Prediction ◽  
Prediction And Prevention

PM2.5 is unanimously considered to be an important indicator of air quality. Sustained rainfall is a kind of typical but complex rainfall process in southern China with an uncertain duration and intervals. During sustained rainfall, the variation of PM2.5 concentrations in hour-level time series is diverse and complex. However, existing analytical methods mainly examine overall removals at the annual/monthly time scale, missing a quantitative analysis mode that applies micro-scale time data to describe the removal phenomenon. In order to further achieve air quality prediction and prevention in the short term, it is necessary to analyze its micro-temporal removal effect for atmospheric environment quality forecasting. This paper proposed a quantitative modeling and prediction method for sustained rainfall-PM2.5 removal modes on a micro-temporal scale. Firstly, a set of quantitative modes for sustained rainfall-PM2.5 removal mode in a micro-temporal scale were constructed. Then, a mode-constrained prediction of the sustained rainfall-PM2.5 removal effect using the factorization machines (FM) was proposed to predict the future sustained rainfall removal effect. Moreover, the historical observation data of Nanjing city at an hourly scale from 2016 to January 2020 were used for mode modeling. Meanwhile, the whole 2020 year observation data were used for the sustained rainfall-PM2.5 removal phenomenon prediction. The experiment shows the reasonableness and effectiveness of the proposed method.

Testability Modeling and Prediction Method with Unreliable Test

2013 ◽  
Vol 756-759 ◽  
pp. 665-668
Author(s):  
Jin Luo ◽  
Qi Bin Deng ◽  
Chen Zhang
Keyword(s):  
Bayesian Networks ◽  
Failure Modes ◽  
Prediction Method ◽  
Uncertain Information ◽  
Modeling And Prediction

For the problem of being lack of consideration of uncertain information during the process of current testability modeling and prediction, the testability modeling and prediction method based on uncertain information is researched from both aspects of uncertain test and uncertain relationship between failure modes and functions. A kind of model called hybrid diagnostic Bayesian networks is designed which is proven to be highly accurate the confidence of the testability prediction results.

Analysis on the Non-Arrhennius Life Prediction Method of Rubber

2013 ◽  
Vol 683 ◽  
pp. 366-371 ◽  
Author(s):  
You Shan Wang ◽  
Wei Wang ◽  
Qiang Liu ◽  
Zhi Bo Cui ◽  
Jun Wang
Keyword(s):  
Activation Energy ◽  
Life Prediction ◽  
Prediction Method ◽  
Butadiene Rubber ◽  
Arrhenius Behavior ◽  
Rubber Material ◽  
Material Life ◽  
Modeling And Prediction ◽  
Sealing Material ◽  
Life Prediction Model

In the present paper, butadiene rubber is analyzed. The activation energy variation of butadiene rubber material has been studied and found that non-Arrhenius behavior. On this basis, the life prediction model of the rubber sealing material has been established, in which the activation energy is the characterization parameters. The storage lifetime of the conducted rubber seal material has been calculated. Under the same conditions, the storage lifetime are 7 years, while according to the GB the storage life are 20.25 years, with comparing the actual storage of the material life of 3-5 years, could prove more accurate modeling and prediction methods established in this study. This would provide a theoretical basis for more accurate life prediction of rubbers.

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

A Prediction Method of Groundwater Table Fluctuations in Landslide Area by Neural Networks.

Landslides ◽  
1995 ◽  
Vol 31 (4) ◽  
pp. 9-15 ◽  
Author(s):  
Hiroyuki YOSHIMATSU ◽  
Akira MUKAI
Keyword(s):  
Neural Networks ◽  
Prediction Method ◽  
Groundwater Table ◽  
Landslide Area
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