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Point Values (point + value)

Distribution by Scientific Domains
Medical Sciences28%

Selected Abstracts

Geostatistical Prediction and Simulation of Point Values from Areal Data

GEOGRAPHICAL ANALYSIS, Issue 2 2005
Phaedon C. Kyriakidis
The spatial prediction and simulation of point values from areal data are addressed within the general geostatistical framework of change of support (the term support referring to the domain informed by each measurement or unknown value). It is shown that the geostatistical framework (i) can explicitly and consistently account for the support differences between the available areal data and the sought-after point predictions, (ii) yields coherent (mass-preserving or pycnophylactic) predictions, and (iii) provides a measure of reliability (standard error) associated with each prediction. In the case of stochastic simulation, alternative point-support simulated realizations of a spatial attribute reproduce (i) a point-support histogram (Gaussian in this work), (ii) a point-support semivariogram model (possibly including anisotropic nested structures), and (iii) when upscaled, the available areal data. Such point-support-simulated realizations can be used in a Monte Carlo framework to assess the uncertainty in spatially distributed model outputs operating at a fine spatial resolution because of uncertain input parameters inferred from coarser spatial resolution data. Alternatively, such simulated realizations can be used in a model-based hypothesis-testing context to approximate the sampling distribution of, say, the correlation coefficient between two spatial data sets, when one is available at a point support and the other at an areal support. A case study using synthetic data illustrates the application of the proposed methodology in a remote sensing context, whereby areal data are available on a regular pixel support. It is demonstrated that point-support (sub-pixel scale) predictions and simulated realizations can be readily obtained, and that such predictions and realizations are consistent with the available information at the coarser (pixel-level) spatial resolution. [source]

The effect of polymers and surfactants on the pour point of palm oil methyl esters

EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY, Issue 4 2007
Cheah Han Sern
Abstract The objective of this research was to find some additives suitable to reduce the pour point (PP) of palm oil methyl esters. The PP properties of palm oil methyl esters (biodiesel) were evaluated with commercially available polymeric and surfactant compounds with various polarities, molecular sizes and structures. The compounds under study were poly(ethylene glycol), poly(methyl methacrylate), poly(ethylene-co-vinyl acetate), poly(styrene-co-maleic anhydride), poly(ethylene glycol) distearate, poly-(octadecyl methacrylate), poly(1-decene), poly(maleic anhydride- alt -1-octadecene), caprylic acid sodium salt, N -lauroylsarcosine sodium salt, polyoxyethylene(2) cetyl ether and polyoxyethylene(10) cetyl ether. Seven out of the twelve polymeric compounds tested were miscible in palm oil methyl esters due to similar polarities of the solute and biodiesel. The blends of the resultant seven polymeric compounds in palm oil methyl esters were evaluated respectively for their effect on the PP property. Poly-(maleic anhydride- alt -1-octadecene) was able to improve the PP of palm oil methyl esters from 12 to 6,°C when 2,wt-% was added. The cloud point was reduced from 12.9 to 8.1,°C, and the cold filter plugging point was reduced from 12 to 7,°C, whilst the flash point value remained unchanged at 156,°C when 2,wt-% of poly(maleic anhydride- alt -1-octadecene) was added to the palm oil methyl esters. [source]

A Risk Scale for Predicting Extensive Subclinical Spread of Nonmelanoma Skin Cancer

DERMATOLOGIC SURGERY, Issue 2 2002
R. Sonia Batra MD
background. The clinical appearance of nonmelanoma skin cancer may represent only a portion of microscopic tumor invasion. objective. To develop a scale based on high-risk characteristics for predicting the probability of extensive subclinical spread of nonmelanoma skin cancer. methods. Retrospective analysis of 1095 Mohs micrographic surgical cases (MMS) yielded high-risk factors for extensive tumor spread, defined as requirement of ,3 MMS layers. Predictive characteristics included: any BCC on the nose, morpheaform BCC on the cheek, neck tumors and recurrent BCC in men, location on the eyelid, temple, or ear helix, and size>10 mm. Multivariate logistic regression was applied to develop a risk index. results. Tumor characteristics were assigned point values calculated from the respective odds of extension and categorized into six risk classes with probabilities of extensive subclinical spread ranging from 10% to 56%. conclusion. A risk scale simplifies and enhances prediction of extensive tumors. The associated probabilities can help to guide patient preparation and appropriate therapy. [source]

Geostatistical Prediction and Simulation of Point Values from Areal Data

GEOGRAPHICAL ANALYSIS, Issue 2 2005
Phaedon C. Kyriakidis
The spatial prediction and simulation of point values from areal data are addressed within the general geostatistical framework of change of support (the term support referring to the domain informed by each measurement or unknown value). It is shown that the geostatistical framework (i) can explicitly and consistently account for the support differences between the available areal data and the sought-after point predictions, (ii) yields coherent (mass-preserving or pycnophylactic) predictions, and (iii) provides a measure of reliability (standard error) associated with each prediction. In the case of stochastic simulation, alternative point-support simulated realizations of a spatial attribute reproduce (i) a point-support histogram (Gaussian in this work), (ii) a point-support semivariogram model (possibly including anisotropic nested structures), and (iii) when upscaled, the available areal data. Such point-support-simulated realizations can be used in a Monte Carlo framework to assess the uncertainty in spatially distributed model outputs operating at a fine spatial resolution because of uncertain input parameters inferred from coarser spatial resolution data. Alternatively, such simulated realizations can be used in a model-based hypothesis-testing context to approximate the sampling distribution of, say, the correlation coefficient between two spatial data sets, when one is available at a point support and the other at an areal support. A case study using synthetic data illustrates the application of the proposed methodology in a remote sensing context, whereby areal data are available on a regular pixel support. It is demonstrated that point-support (sub-pixel scale) predictions and simulated realizations can be readily obtained, and that such predictions and realizations are consistent with the available information at the coarser (pixel-level) spatial resolution. [source]

Predicting pressure ulcer risk: a multifactorial approach to assess risk factors in a large university hospital population

JOURNAL OF CLINICAL NURSING, Issue 1 2009
Michael Nonnemacher
Aims., The purpose of this study was: (1) to determine the combination of risk factors which best predicts the risk of developing pressure ulcers among inpatients in an acute care university hospital; (2) to determine the appropriate weight for each risk factor; and (3) to derive a concise and easy-to-use risk assessment tool for daily use by nursing staff. Background., Efficient application of preventive measures against pressure ulcers requires the identification of patients at risk. Adequate risk assessment tools are still needed because the predictive value of existing tools is sometimes unsatisfactory. Design., Survey. Methods., A sample of 34,238 cases admitted to Essen University Clinics from April 2003 and discharged up to and including March 2004, was enrolled into the study. Nursing staff recorded data on pressure ulcer status and potential risk factors on admission. Predictors were identified and weighted by multivariate logistic regression. We derived a risk assessment scale from the final logistic regression model by assigning point values to each predictor according to its individual weight. Results., The period prevalence rate of pressure ulcers was 1·8% (625 cases). The analysis identified 12 predictors for developing pressure ulcers. With the optimum cut-off point sensitivity and specificity were 83·4 and 83·1%, respectively, with a positive predictive value of 8·4% and a negative predictive value of 99·6%. The diagnostic probabilities of the derived scale were similar to those of the original regression model. Conclusions., The predictors mostly correspond to those used in established scales, although the use of weighted factors is a partly novel approach. Both the final regression model and the derived scale show good prognostic validity. Relevance to clinical practice., The derived risk assessment scale is an easy-to-understand, easy-to-use tool with good prognostic validity and can assist in effective application of preventive measures against pressure ulcer. [source]

A new method for scoring additive multi-attribute value models using pairwise rankings of alternatives

JOURNAL OF MULTI CRITERIA DECISION ANALYSIS, Issue 3-4 2008
Paul Hansen
Abstract We present a new method for determining the point values for additive multi-attribute value models with performance categories. The method, which we refer to as PAPRIKA (Potentially All Pairwise RanKings of all possible Alternatives), involves the decision-maker pairwise ranking potentially all undominated pairs of all possible alternatives represented by the value model. The number of pairs to be explicitly ranked is minimized by the method identifying all pairs implicitly ranked as corollaries of the explicitly ranked pairs. We report on simulations of the method's use and show that if the decision-maker explicitly ranks pairs defined on just two criteria at-a-time, the overall ranking of alternatives produced by the value model is very highly correlated with the true ranking. Therefore, for most practical purposes decision-makers are unlikely to need to rank pairs defined on more than two criteria, thereby reducing the elicitation burden. We also describe a successful real-world application involving the scoring of a value model for prioritizing patients for cardiac surgery in New Zealand. We conclude that although the new method entails more judgments than traditional scoring methods, the type of judgment (pairwise rankings of undominated pairs) is arguably simpler and might reasonably be expected to reflect the preferences of decision-makers more accurately. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Oxidative stability of palm- and soybean-based medium- and long-chain triacylglycerol (MLCT) oil blends

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 3 2009
Soo Peng Koh
Abstract BACKGROUND: Medium- and long-chain triacylglyerols (MLCT) enzymatically esterified using Lipozyme RM IM lipase has very low oxidative stability as it does not contain any antioxidants. The aim of this work was to study the ability of various antioxidants to increase the oxidative stability of palm- and soybean-based MLCT blends which assist to bring up the oxidative stability of both MLCT blends. In this study, the effectiveness of rosemary extracts, sage extracts, tert -butylhydroquinone (TBHQ) and mixtures of tert -butyl-4-hydroxyanisole (BHA) and tert -butyl- p -hydroxytoluene (BHT) in protecting against oxidation of various MLCT blends was investigated. RESULTS: Blending of MLCT oil with either palm olein or soybean oil improved its smoke point values and oxidative stability. TBHQ addition to both palm- and soybean-based MLCT blends increased oxidative stability. Combination of BHA and BHT showed no significant improvement (P > 0.05) in ability to protect blends from oxidation compared to natural antioxidants such as sage or rosemary extracts. CONCLUSION: Blended oils with 500 g kg,1 MLCT and 500 g kg,1 palm olein (MP5) were the most suitable for use at high temperature based on the fatty acid composition of the MLCT blends, which subsequently had an effect on thermal oxidative stability. In general, addition of either natural or synthetic antioxidant assisted in improving the antioxidative strength of both MLCT blends. MLCT blends with added TBHQ showed the highest thermal oxidative stability among the antioxidants used. Copyright © 2008 Society of Chemical Industry [source]