STATISTICAL MODEL DEVELOPMENT TO ANTICIPATE FILTER BREAKTHROUGH AND OPTIMISE CHEMICALS DURING HIGH COLOUR RAW WATER EVENTS

2017 ◽  
Vol 2 (4) ◽  
pp. 1-6
Author(s):  
Asm Mohiuddin ◽  
Yue-cong Wang
Keyword(s):  
Statistical Model ◽  
Model Development ◽  
Raw Water ◽  
Statistical Model Development

Statistical model development for high-dimensional big data analytics

2020 ◽  
Vol 31 (6) ◽  
pp. 1009-1020
Author(s):  
Dae Heung Jang ◽  
Il Do Ha ◽  
Dong Jun Park ◽  
In Ho Park ◽  
Seung Jae Lee
Keyword(s):  
Big Data ◽  
Statistical Model ◽  
Data Analytics ◽  
Model Development ◽  
Big Data Analytics ◽  
High Dimensional ◽  
Statistical Model Development

A Statistical Model of Bubble Coalescence and Its Application to Boiling Heat Flux Prediction—Part II: Experimental Validation

2009 ◽  
Vol 131 (12) ◽  
Author(s):  
Wen Wu ◽  
Barclay G. Jones ◽  
Ty A. Newell
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Statistical Model ◽  
Mechanistic Model ◽  
Model Development ◽  
Critical Diameter ◽  
Bubble Coalescence ◽  
Average Error ◽  
Static Contact ◽  
Lift Off

A mechanistic model for the boiling heat flux prediction proposed in Part I of this two-part paper (2009, “A Statistical Model of Bubble Coalescence and Its Application to Boiling Heat Flux Prediction—Part I: Model Development,” ASME J. Heat Transfer, 131, p. 121013) is verified in this part. In the first step, the model is examined by experiments conducted using R134a covering a range of pressures, inlet subcoolings, and flow velocities. The density of the active nucleation sites is measured and correlated with critical diameter Dc and static contact angle θ. Underlying submodels on bubble growth and bubble departure/lift-off radii are validated. Predictions of heat flux are compared with the experimental data with an overall good agreement observed. This model achieves an average error of ±25% for the prediction of R134a boiling curves, with the predicted maximum surface heat flux staying within ±20% of the experimentally measured critical heat flux. In the second step, the model is applied to water data measured by McAdams et al. (1949, “Heat Transfer at High Rates to Water With Surface Boiling,” Ind. Eng. Chem., 41(9), pp. 1945–1953) in vertical circular tubes. The consistency suggests that the application of this mechanistic model can be extended to other flow conditions if the underlying submodels are appropriately chosen and the assumptions made during model development remain valid.

scholarly journals Coupling Sensing Hardware with Data Interrogation Software for Structural Health Monitoring

2006 ◽  
Vol 13 (4-5) ◽  
pp. 519-530 ◽  
Author(s):  
Charles R. Farrar ◽  
David W. Allen ◽  
Gyuhae Park ◽  
Steven Ball ◽  
Michael P. Masquelier
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Software Package ◽  
Model Development ◽  
General Purpose ◽  
Statistical Pattern Recognition ◽  
Structural Health ◽  
Statistical Pattern ◽  
Statistical Model Development ◽  
Processing Platform

The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). The authors' approach is to address the SHM problem in the context of a statistical pattern recognition paradigm. In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition and Cleansing, (3) Feature Extraction and Data Compression, and (4) Statistical Model Development for Feature Discrimination. These processes must be implemented through hardware or software and, in general, some combination of these two approaches will be used. This paper will discuss each portion of the SHM process with particular emphasis on the coupling of a general purpose data interrogation software package for structural health monitoring with a modular wireless sensing and processing platform. More specifically, this paper will address the need to take an integrated hardware/software approach to developing SHM solutions.

scholarly journals A meta-analysis of groundwater contamination by nitrates at the African scale

2016 ◽  
Author(s):  
Issoufou Ouedraogo ◽  
Marnik Vanclooster
Keyword(s):  
Statistical Model ◽  
Nitrate Concentration ◽  
Regional Scale ◽  
Model Development ◽  
Shallow Groundwater ◽  
Nitrate Pollution ◽  
Nitrate Contamination ◽  
Pan African ◽  
Nitrate Concentrations ◽  
Groundwater Systems

Abstract. Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space-time distribution of this contamination, as well as understanding the factors that explain this contamination is important to manage sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the pan-African scale by statistical modeling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology and anthropogenic data for statistical model development. The maximum, medium and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, 4 variables are retained in the statistical explanatory model: (1) Depth to groundwater (shallow groundwater, typically < 50 m); (2) Recharge rate; (3) Aquifer type; and (4) Population density. The former three variables represent intrinsic vulnerability of groundwater systems towards pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the pan-Africa scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class and region type improve the prediction of maximum nitrate concentrations at the pan-African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the dataset, we do not develop a statistical model for these data. The data based statistical model presented here represents an important step toward developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data becomes available and/or combined with more conceptual descriptions of the fate of nutrients in the hydro system.

scholarly journals Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy

2020 ◽  
Author(s):  
Vivaswat Shastry ◽  
Paula E. Adams ◽  
Dorothea Lindtke ◽  
Elizabeth G. Mandeville ◽  
Thomas L. Parchman ◽  
...  
Keyword(s):  
Genetic Relatedness ◽  
Spatial Clustering ◽  
Sequence Data ◽  
Random Mating ◽  
Model Development ◽  
Natural Populations ◽  
Simulated Data ◽  
Ancestry Estimation ◽  
Statistical Model Development ◽  
High Depth

AbstractNon-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can utilize low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low depth sequencing across a larger fraction of the genome than with high depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

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