48,742 research outputs found
A review on the influence of drinking water quality towards human health
An adequate supply of safe drinking water is one of the major prerequisites for a healthy life. Inadequate of safe drinking water produce waterborne disease and a major cause of death in many parts of the world, particularly in children. Therefore, it must be treated properly before it can be used and consumed. This chapter provides the guidelines of important parameters for drinking water standard in order to ensure the safeness of drinking water. All the selected parameters were elaborated on the effect of high concentration if human consume the drinking water directly
A new taxonomy of country performance and risk based on economic and technological indicators
This paper proposes a new taxonomy for countries based on principal component analysis. The paper investigates 51 countries using a set of 13 indicators of economic and technological performance for the period 2000-2002. The methodology reduces the variables and groups the countries that show similar strategic behaviour in the global market. The taxonomy facilitates the identification of country performance and risk, and provides relevant information both to international investors and to policy-makers, who must decide about global investment strategies and economic policies.comparative country analysis, multivariate analysis, country taxonomy, countrymetrics
The innovation of cotton fiber from recycled cloth as coloring agent for polypropylene via injection moulding
Plastics can be coloured in many different ways. In the present application, the colouring agent that is used in the plastic industries are the dyes and pigments. Both methods are sustantially different and produce specific results. Dyes are defined as colourants that are (completely) soluble in a polymer at the processing temperature. Pigments are organic or inorganic solid particles that are insoluble in polymers..
Clustering South African households based on their asset status using latent variable models
The Agincourt Health and Demographic Surveillance System has since 2001
conducted a biannual household asset survey in order to quantify household
socio-economic status (SES) in a rural population living in northeast South
Africa. The survey contains binary, ordinal and nominal items. In the absence
of income or expenditure data, the SES landscape in the study population is
explored and described by clustering the households into homogeneous groups
based on their asset status. A model-based approach to clustering the Agincourt
households, based on latent variable models, is proposed. In the case of
modeling binary or ordinal items, item response theory models are employed. For
nominal survey items, a factor analysis model, similar in nature to a
multinomial probit model, is used. Both model types have an underlying latent
variable structure - this similarity is exploited and the models are combined
to produce a hybrid model capable of handling mixed data types. Further, a
mixture of the hybrid models is considered to provide clustering capabilities
within the context of mixed binary, ordinal and nominal response data. The
proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD).
The MFA-MD model is applied to the survey data to cluster the Agincourt
households into homogeneous groups. The model is estimated within the Bayesian
paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings
result, providing insight to the different socio-economic strata within the
Agincourt region.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS726 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.
The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making
A Generative Model of Natural Texture Surrogates
Natural images can be viewed as patchworks of different textures, where the
local image statistics is roughly stationary within a small neighborhood but
otherwise varies from region to region. In order to model this variability, we
first applied the parametric texture algorithm of Portilla and Simoncelli to
image patches of 64X64 pixels in a large database of natural images such that
each image patch is then described by 655 texture parameters which specify
certain statistics, such as variances and covariances of wavelet coefficients
or coefficient magnitudes within that patch.
To model the statistics of these texture parameters, we then developed
suitable nonlinear transformations of the parameters that allowed us to fit
their joint statistics with a multivariate Gaussian distribution. We find that
the first 200 principal components contain more than 99% of the variance and
are sufficient to generate textures that are perceptually extremely close to
those generated with all 655 components. We demonstrate the usefulness of the
model in several ways: (1) We sample ensembles of texture patches that can be
directly compared to samples of patches from the natural image database and can
to a high degree reproduce their perceptual appearance. (2) We further
developed an image compression algorithm which generates surprisingly accurate
images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how
our approach can be used for an efficient and objective evaluation of samples
generated with probabilistic models of natural images.Comment: 34 pages, 9 figure
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