# thesIt

• #### Arif 05:41:53 am on August 24, 2010 | 0 | # | Tags: neural network, poor data, reference, Silvert 1998

Perhaps the greatest problem that is faced in most attempts to use artificial neural networks for ecological applications is that the quantity of data is often very limited. Although there are a few cases where large amounts of data are available, as in the case of remote sensing or observations based on automatic telemetry, it is far more common to have to deal with limited and irregularly spaced data, and the data may not always be strictly comparable due to variations in environmental conditions between sampling periods. In most situation the collection of field data is both time-consuming and expensive.

Since the training and testing of neural networks is very data-intensive, this poses serious obstacles to the development of neural network applications in ecology.

• #### Arif 01:03:43 pm on August 22, 2010 | 0 | # | Tags: Chan 1996, Levenberg-Marquardt, reference

Chan LW. 1996. Levenberg-Marquardt learning and regularization. Progress Neural Inform Processing 139-144.

iconip96.ps

• #### Arif 12:41:37 pm on August 22, 2010 | 0 | # | Tags: cross-validation, error, estimation, k-fold, Michaelsen 1987, reference, Stone 1974

Stone 1974 is referenced in:
Michaelsen J. 1987. Cross-validation in statistical climate forecast models. J Climate Applied Meteorology, 26:1589-1600

1520-0450(1987)026-1589-cviscf-2.0.co;2.pdf

Set $Z = z_{1}, z_{2},... , z_{1}$ consists of predictions and targets $z_{i}=(x_{i}, y_{i})$

A set of prediction rule $\eta (x,Z)$ will be used to predict y0 from $\eta (x_{0},Z)$

Let $Q(y_{i},\eta_{i})$ be the accuracy.
by least squares this will usually $(y_{i}-\eta_{i})^{2}$
in other words expected Err is
$Err= E[Q(y_{i},\eta(x_{0},Z))]$

## MSE

$MSE= \sum_{i=1}^{n}Q(y_{i},\eta(x_{i},Z))/n$

In cross validation
$MSE_{(CV)}= \sum_{i=1}^{n}Q(y_{i},\eta(x_{i},Z_{(i)}))/n$

• #### Arif 03:39:24 pm on February 27, 2010 | 0 | # | Tags: abstract, Danielli 1937, interfacial tension, pH, reference

Danielli JF. 1937. The Relations between Surface pH, Ion Concentrations and Interfacial Tension. Proceedings of the Royal Society of London. Series B, Biological Sciences, Vol. 122, No. 827 (Apr. 1, 1937), pp. 155-174

abstract here

• #### Arif 03:31:28 pm on February 27, 2010 | 0 | # | Tags: abstract, interfacial tension, Petelska 2002, pH, reference

Petelska AD, Naumowicz M, Figaszewski ZA. 2002. Interfacial tension of the bilayer lipid membrane. Cell Moll Biol Lett 7:212.

A regularity has been found: a larger hydrophilic head gives rise to a lower interfacial tension. A relationship was found between the size of the hydrophilic head of lipid and the isoelectric point pH value. With larger hydrophilic heads, the isoelectric point appears at lower pH.

abstract:
Vol7_suppl_05_39.pdf

• #### Arif 03:18:46 pm on February 27, 2010 | 0 | # | Tags: interfacial tension, Johlin 1930, pH, reference

Johlin JM. 1930. The influence of pH and solution concentration on the surface tension of gelatin solutions determined by the sessile bubble method. J Biol Chem 87:319-325.

obtained by the sessile bubble method of measuring surface tensions

J. Biol. Chem.-1930-Johlin-319-25.pdf

• #### Arif 02:46:05 pm on February 27, 2010 | 0 | # | Tags: abtract, interfacial tension, oil recovery, prediction, reference, Sharma 1983

A Thermodynamic Model for Low Interfacial Tensions in Alkaline Flooding. Sharma, Mukul M.; Yen, T.F. SPE Journal. Volume 23, Number 1. February 1983

Many experimental studies have been undertaken to measure interfacial tensions (IFT’s) as a function of pH, salinity, temperature, and divalent ion concentrations.
The molecular approach involves a statistical mechanical calculation of the intermolecular forces operating at the interfaces between two phases.

abstract here

This citing is probably too old (1983)

• #### Arif 01:11:16 pm on February 27, 2010 | 0 | # | Tags: abtract, interfacial tension, Petelska 2000, pH, reference

Effect of pH on the Interfacial Tension of Lipid Bilayer Membrane. Aneta D. Petelska and Zbigniew A. Figaszewski. Biophysical Journal, Volume 78, Issue 2, 812-817, 1 February 2000.

A theoretical equation is derived to describe the dependence of the interfacial tension of a lipid bilayer on the pH of the aqueous solution. Interfacial tension measurements of an egg phosphatidylcholine bilayer were carried out. The experimental results agreed with those derived from the theoretical equation obtained close to the isoelectric point within a range of three pH units. A maximum corresponding to the isoelectric point appears both in the theoretical equation and in the experiment.

abstract here

• #### Arif 01:01:10 pm on February 27, 2010 | 0 | # | Tags: abstract, Dandekar 2004, inaccuracy, interfacial tension, oil recovery, prediction, reference, sensitivity

Sensitivity analysis of interfacial tension predictions for hydrocarbon fluids. DANDEKAR Abhijit Y. Petroleum science and technology. 2004, vol. 22, no9-10, pp. 1161-1172 [12 page(s) (article)] (11 ref.)

The interfacial tension (IFT) of hydrocarbon fluids is commonly predicted by either the parachor method or the scaling law. The methods require equilibrium liquid and vapor phase composition and density. An equation of state would normally be required if experimental values are not available. However, the computation of density for simple hydrocarbons and reservoir fluids, despite the important advances achieved by cubic equations of state, still remains a weak link in these types of calculations. Thus, there exists a need to investigate the qualitative and quantitative effects, of such inaccuracies in the density, on IFT predictions. Moreover, the study presented in this work would be useful in reservoir engineering and enhanced oil recovery calculations. The results presented in this work indicate that the methods are highly sensitive to the inaccuracies in the density of both the liquid and the vapor phases. An error of around 10% in the liquid or the vapor density can result in an error of up to 200% in the estimated IFT. Two binary and one ternary mixture for which measured data on IFT, composition and density is reported in the literature form the basis of this study.

abstract here

• #### Arif 12:57:22 pm on February 27, 2010 | 0 | # | Tags: abstract, interfacial tension, KumarVasanth 2009, neural network, prediction, reference

Neural Network Prediction of Interfacial Tension at Crystal/Solution Interface. K. Vasanth Kumar. Ind. Eng. Chem. Res., 2009, 48 (8), pp 4160–4164

Using (1) solubility, (2) molecular weight, and (3) density, a three-layer feed-forward neural network was constructed and tested to predict the IFT at the crystal/solution interface. The concentration of solute in liquid phase, (1) concentration of solute in solid phase, (4) temperature, (3) density and (2) molecular weight of crystal were used as inputs to predict the interfacial tension at the crystal/liquid interface (σSL). The network was trained using the solubility information for 28 systems to predict the σSL value and was validated with 29 new systems. Despite the limited number of data used for training, the neural network was capable of predicting σSL successfully for the new inputs, which are kept unaware during the training process. The σSL value that is predicted by the artificial neural network during the training and testing process was compared with σSL predicted from the widely used empirical expression. For most of the systems, ANN better predicts IFT.

abstract here

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