9,809 Matching Annotations
  1. Jul 2019
    1. The table 6.1 gives the mixing probabilities and the associated parametricvalues fork(number of components) = 2,3, and 4. It may be noted thatthe Log likelihood value is smaller fork= 4 (the results fork= 5 , 6 etc.are not better than that fork= 4 and hence are not given here). The fourcomponents Poisson Mixture model is given in table 6.2. It may be notedthat 58% of wards may have higher incidence/relative risk and the remainingwards have lesser/lower incidence for the Cancer disease. We computed theposterior probability for each component for each ward (see table 6.3). Eachward is assigned to a particular component so that the posterior probability islarger. These results are also given in table 6.3 Finally we present Choroplethmaps based on those results
    2. Algorithm
    3. Data Sources
    1. Poisson Model
    2. We have analysed the Cancer data of patients in 155 wards of Chennai Cor-poration by the above described method. As preliminary analyses, we havecreated the Choropleth maps for Observed counts, Population of wards, ex-pected counts for patients and SMR's.The Choropleth map for the observed counts Figure 5.2 does not show anypattern. But the Choropleth map for the expected counts Figure: 5.4 indi-cate that the inner regions of the Chennai Corporations have lower expectedcounts and the regions along the border have larger counts of patients. As ameasure of spatial heterogeneity we have computed PSH= 0:7108:Hence ofthe total spatial random variation, nearly 71% is due to spatial heterogene-ity and the remaining 28:92% is due to Poisson variation. Thus the spatialvariation is present in the data.The Choropleth map for Empirical Bayes smoothed rates Figure 5.5 re-veals that only 13 sub regions have high risk values. The wards with numbers53, 64, 67, 70, 78, 93, 100, 103, 110, 117, 122, 147 and 151 have high riskvalues. Though this information could be used by the health managers toconcentrate their work on these regions, one can look for additional covariatesin these regions for further study
    3. Empirical Bayesian Smoothing
    4. Incidence Rate and SMR
    5. Spatial Analysis of Cancer PatientCount Data
  2. Jun 2019
    1. At each stage of developing the sample application, we will write small, bite-sized pieces of code—simple enough to understand, yet novel enough to be challenging. The cumulative effect will be a deeper, more flexible knowledge of Rails, giving you a good background for writing nearly any type of web application.
    2. Following the scaffolding approach risks turning you into a virtuoso script generator with little (and brittle) actual knowledge of Rails.
    1. Phenological traits and plant height
    2. Analysis of variance and differences among wheat varieties released in different years in India
    3. Estimation of total N% of wheat grainsand straw
    4. Chlorophyllcontent
    5. Root length (cm) and Root weight (mg)
    6. Coleoptile length(cm)
    7. Stomata / cm2
    8. Leaf area index (LAI)
    9. Physiological parameters
    10. Normalized difference vegetation index (NDVI)
    11. Spike length (cm)
    12. Last node to spike length(cm)
    13. Peduncle length(cm)
    14. HarvestIndex
    15. Grain yield per plot (g)
    16. Biological Yield(g)
    17. 1000 Kernel weight(mg)
    18. Number of grains per spike
    19. Number of productive tillers per meter
    20. Plant height (cm)
    21. Days to physiological maturity
    22. Days to heading
    23. Field observations
    1. T cell frequencies(Post vaccination response)
    2. Decreased CD3 ζ chain expression on CD8 T cells in HBsAgpositive newborns
    3. T cell phenotypic distribution in HBsAgPositive, HBsAgNegative from HBsAg positive mothers and healthy newborns.
    4. Clinical characteristics of the subjects
    1. Characterization of embelin isolated from E.ribes
    2. . Extraction and isolation of embelin from E. ribes
    3. High Performance Liquid Chromatography (HPLC) analysis
    4. High Performance Thin Layer Chromatography (HPTLC) analysis
    5. . Thin Layer Chromatography (TLC) analysis
    6. Preliminary phytochemical screening (Ali, 1998; Evans, 2002)
    7. Microbial Contamina
    8. Foaming Index
    9. pH values
    10. Ash values
    11. Extractive value
    12. . Physico-chemical standardization
    13. Standardization of ethanolic extract of E.ribes (WHO, 1998; IndianPharmacopoeia, 1996)
    14. Preparation of ethanolic extract of E.ribes
    15. PLANT MATERIAL
    1. Estimation of Stevioside
    2. Extraction from the plant material
    3. Extraction from the plant material
    4. Extraction from the plant material
    5. Qmmtification of stevioside
    6. Estimation of Steviol
    7. Extraction from plant material
    8. Quantification of steviol
    9. Estimation of free aminoacids
    10. Estimation of soluble proteins
    11. Estimation of sugars
    12. Estimation of total phenols
    13. Determination of moisture
    14. Analytical methods