Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

A computational study of heat transfer, soot production and transport in an acetylene-air laminar diffusion flame in the field of a line vortex

The present work involves a computational study of soot formation and transport in case of a laminar acetylene diffusion flame perturbed by a co nvecting line vortex. The topology of the soot contours (as in an earlier experimental work [4]) have been investigated. More soot was produced when vortex was introduced from the air si de in comparison to a fuel side vortex. Also the soot topography was more diffused in case of the air side vortex. The computational model was found to be in good agreement with the ex perimental work [4]. The computational simulation enabled a study of the various parameters affecting soot transport. Temperatures were found to be higher in case of air side vortex as compared to a fuel side vortex. In case of the fuel side vortex, abundance of fuel in the vort ex core resulted in stoichiometrically rich combustion in the vortex core, and more discrete so ot topography. Overall soot production too was low. In case of the air side vortex abundan ce of air in the core resulted in higher temperatures and more soot yield. Statistical techniques like probability density fun ction, correlation coefficient and conditional probability function were introduced to explain the transient dependence of soot yield and transport on various parameters like temperature, a cetylene concentration.

A computational study of flame dynamics on soot production and transport for an acetylene-air laminar diffusion flame in the field of a convecting line vortex

The present work involves a computational study of soot formation and transport in case of a laminar acetylene diffusion flame perturbed by a convecting line vortex. The topology of the soot contours (as in an earlier experimental work [4]) have been investigated. More soot was produced when vortex was introduced from the air side in comparison to a fuel side vortex. Also the soot topography was more diffused in case of the air side vortex. The computational model was found to be in good agreement with the experimental work [4]. The computational simulation enabled a study of the various parameters affecting soot transport. Temperatures were found to be higher in case of air side vortex as compared to a fuel side vortex. In case of the fuel side vortex, abundance of fuel in the vort ex core resulted in stoichiometrically rich combustion in the vortex core, and more discrete soot topography. Overall soot production too was low. In case of the air side vortex abundance of air in the core resulted in higher temperatures and more soot yield. Statistical techniques like probability density function, correlation coefficient and conditional probability function were introduced to explain the transient dependence of soot yield and transport on various parameters like temperature, a cetylene concentration.

Improved Detection of Remote Homologues Using Cascade PSI-BLAST: Influence of Neighbouring Protein Families on Sequence Coverage

Background: Development of sensitive sequence search procedures for the detection of distant relationships between proteins at superfamily/fold level is still a big challenge. The intermediate sequence search approach is the most frequently employed manner of identifying remote homologues effectively. In this study, examination of serine proteases of prolyl oligopeptidase, rhomboid and subtilisin protein families were carried out using plant serine proteases as queries from two genomes including A. thaliana and O. sativa and 13 other families of unrelated folds to identify the distant homologues which could not be obtained using PSI-BLAST. Methodology/Principal Findings: We have proposed to start with multiple queries of classical serine protease members to identify remote homologues in families, using a rigorous approach like Cascade PSI-BLAST. We found that classical sequence based approaches, like PSI-BLAST, showed very low sequence coverage in identifying plant serine proteases. The algorithm was applied on enriched sequence database of homologous domains and we obtained overall average coverage of 88% at family, 77% at superfamily or fold level along with specificity of similar to 100% and Mathew's correlation coefficient of 0.91. Similar approach was also implemented on 13 other protein families representing every structural class in SCOP database. Further investigation with statistical tests, like jackknifing, helped us to better understand the influence of neighbouring protein families. Conclusions/Significance: Our study suggests that employment of multiple queries of a family for the Cascade PSI-BLAST searches is useful for predicting distant relationships effectively even at superfamily level. We have proposed a generalized strategy to cover all the distant members of a particular family using multiple query sequences. Our findings reveal that prior selection of sequences as query and the presence of neighbouring families can be important for covering the search space effectively in minimal computational time. This study also provides an understanding of the `bridging' role of related families.

Fuzzy data envelopment analysis for performance evaluation of an irrigation system

Sixteen irrigation subsystems of the Mahi Bajaj Sagar Project, Rajasthan, India, are evaluated and selection of the most suitable/best is made using data envelopment analysis (DEA) in both deterministic and fuzzy environments. Seven performance-related indicators, namely, land development works (LDW), timely supply of inputs (TSI), conjunctive use of water resources (CUW), participation of farmers (PF), environmental conservation (EC), economic impact (EI) and crop productivity (CPR) are considered. Of the seven, LDW, TSI, CUW, PF and EC are considered inputs, whereas CPR and EI are considered outputs for DEA modelling purposes. Spearman rank correlation coefficient values are also computed for various scenarios. It is concluded that DEA in both deterministic and fuzzy environments is useful for the present problem. However, the outcome of fuzzy DEA may be explored for further analysis due to its simple, effective data and discrimination handling procedure. It is inferred that the present study can be explored for similar situations with suitable modifications.

Polyvinylidene fluoride film based nasal sensor to monitor human respiration pattern: An initial clinical study

Design and development of a piezoelectric polyvinylidene fluoride (PVDF) thin film based nasal sensor to monitor human respiration pattern (RP) from each nostril simultaneously is presented in this paper. Thin film based PVDF nasal sensor is designed in a cantilever beam configuration. Two cantilevers are mounted on a spectacle frame in such a way that the air flow from each nostril impinges on this sensor causing bending of the cantilever beams. Voltage signal produced due to air flow induced dynamic piezoelectric effect produce a respective RP. A group of 23 healthy awake human subjects are studied. The RP in terms of respiratory rate (RR) and Respiratory air-flow changes/alterations obtained from the developed PVDF nasal sensor are compared with RP obtained from respiratory inductance plethysmograph (RIP) device. The mean RR of the developed nasal sensor (19.65 +/- A 4.1) and the RIP (19.57 +/- A 4.1) are found to be almost same (difference not significant, p > 0.05) with the correlation coefficient 0.96, p < 0.0001. It was observed that any change/alterations in the pattern of RIP is followed by same amount of change/alterations in the pattern of PVDF nasal sensor with k = 0.815 indicating strong agreement between the PVDF nasal sensor and RIP respiratory air-flow pattern. The developed sensor is simple in design, non-invasive, patient friendly and hence shows promising routine clinical usage. The preliminary result shows that this new method can have various applications in respiratory monitoring and diagnosis.

Rapid determination of main constituents of packed juices by reverse phase-high performance liquid chromatography: an insight in to commercial fruit drinks

The present work reports the compositional analysis of thirteen different packed fruit juices using high performance liquid chromatography (HPLC). Vitamin C, organic acids (citric and malic) and sugars (fructose, glucose and sucrose) were separated, analyzed and quantified using different reverse phase methods. A new rapid reverse phase HPLC method was developed for routine analysis of vitamin C in fruit juices. The precision results of the methods showed that the relative standard deviations of the repeatability and reproducibility were < 0.05 and < 0.1 respectively. Correlation coefficient of the calibration models developed was found to be higher than 0.99 in each case. It has been found that the content of Vitamin C was less variable amongst different varieties involved in the study. It is also observed that in comparison to fresh juices, the packed juices contain lesser amounts of vitamin C. Citric acid was found as the major organic acids present in packed juices while maximum portion of sugars was of sucrose. Comparison of the amount of vitamin C, organic acids and sugars in same fruit juice of different commercial brands is also reported.

Copula-Based Modeling of TMI Brightness Temperature With Rainfall Type

Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique.

PROPENSITIES OF AMINO ACID RESIDUES IN PROTEINS FOR DIFFERENT REGIONS OF THE RAMACHANDRAN MAP

Using a dataset of 1164 crystal structures of largely non-homologous proteins defined at a resolution of 1.5 angstrom or better, we have investigated the (phi,psi) preferences of 20 residue types by considering the residues which occur in loops. Propensities of residue types to occur in the loops with (phi,psi) values in the aa region of the Ramachandran map has a poor correlation coefficient of 0.48 to the Chou-Fasman propensities of the residue types to occur in the a-helical segments. However the correlation coefficient between propensities of residues in loops to adopt beta conformations and those in beta-sheet is much higher (0.95). These observations suggest that a-helix formation is well influenced by the local amino acid sequence while intrinsic preference of residue types for beta-sheet plays a major role in the formation of beta-sheet. The main chain polar groups of residues in loops, that can affect the (phi,psi) values, can be involved in intra-molecular hydrogen bonding. Therefore we investigated further by considering subset of residues in loops with low (0 to 2) number of intra-molecular hydrogen bonds per residue involving main chain polar atoms. For this subset, the correlation coefficients between propensities for alpha-helix and alpha(R) region and between beta-sheet and beta-region are 0.26 and 0.64 respectively. This reiterates higher intrinsic tendency of beta-region favouring residues to adopt beta-sheet than alpha(R) region favouring residues to adopt alpha-helical structure.

Ranking of global climate models for India using multicriterion analysis

Eleven GCMs (BCCR-BCCM2.0, INGV-ECHAM4, GFDL2.0, GFDL2.1, GISS, IPSL-CM4, MIROC3, MRI-CGCM2, NCAR-PCMI, UKMO-HADCM3 and UKMO-HADGEM1) were evaluated for India (covering 73 grid points of 2.5 degrees x 2.5 degrees) for the climate variable `precipitation rate' using 5 performance indicators. Performance indicators used were the correlation coefficient, normalised root mean square error, absolute normalised mean bias error, average absolute relative error and skill score. We used a nested bias correction methodology to remove the systematic biases in GCM simulations. The Entropy method was employed to obtain weights of these 5 indicators. Ranks of the 11 GCMs were obtained through a multicriterion decision-making outranking method, PROMETHEE-2 (Preference Ranking Organisation Method of Enrichment Evaluation). An equal weight scenario (assigning 0.2 weight for each indicator) was also used to rank the GCMs. An effort was also made to rank GCMs for 4 river basins (Godavari, Krishna, Mahanadi and Cauvery) in peninsular India. The upper Malaprabha catchment in Karnataka, India, was chosen to demonstrate the Entropy and PROMETHEE-2 methods. The Spearman rank correlation coefficient was employed to assess the association between the ranking patterns. Our results suggest that the ensemble of GFDL2.0, MIROC3, BCCR-BCCM2.0, UKMO-HADCM3, MPIECHAM4 and UKMO-HADGEM1 is suitable for India. The methodology proposed can be extended to rank GCMs for any selected region.

Colorimetric detection of melamine based on the size effect of AuNPs

A simple colorimetric detection of melamine was studied using 15 nm (AuNPs-I), 30 nm (AuNPs-II), and 40 nm (AuNPs-III) citrate-capped gold nanoparticles (AuNPs). The AuNPs aggregated in aqueous solution in the presence of melamine, showing a visual color change from red to blue. This color change led to a shift in the absorption peak from 527 nm, 526 nm, and 525 nm to 638 nm, 626 nm, and 680 nm for AuNPs-I, AuNPs-II, and AuNPs-III, respectively. For all the three AuNPs, linearity was observed between the melamine concentration in aqueous solution and the absorbance ratios, A(638/527), A(626/525), and A(680/526), respectively. The limit of detection (LOD) for melamine for the AuNPs-II was found to be 2.37 x 10(-8) M (correlation coefficient R-2 = 0.9745), which showed better sensitivity as compared to the LOD of the AuNPs-I and AuNPs-III, which were 3.3 x 10(-8) M and 8.9 x 10(-8) M, respectively. The synthesis of AuNPs-II also involved a lower HAuCl4 concentration compared with the other two types of AuNPs, which may reduce the process cost. The AuNPs-II was selected to analyze melamine in pre-treated milk samples, and the recovery percentage was in the range of 91-106%. Thus, the efficient detection of melamine was possible using AuNPs-II for the on-site detection without the aid of expensive instruments.

Structural, thermal and quantum chemical studies of p-coumaric and caffeic acids

Two hydroxycinnamic acids viz., p-coumaric, and caffeic acids have been extracted and purified from Parthenium hysterophorus, subsequently characterized by elemental analysis, FT-IR, NMR, single crystal X-ray crystallography. The optimized structures of these acids were calculated in terms of density functional theory by Gaussian 09. The validation of experimental and theoretically obtained data for structural parameters such as bond lengths and bond angles has have been carried out to analyze the statistical significance by curve fitting analysis and the values of correlation coefficient found to be 0.985, 0.992, and 0.984, 0.975 in p-coumaric, and caffeic acids, respectively. The calculated HOMO and LUMO energies show the eventual charge transfer interaction within the molecule. Thermal studies were also carried out by thermogravimetry (TG), differential thermogravimetric analysis (DTA), and derivative thermogravimetry (DTG). (C) 2014 Elsevier B.V. All rights reserved.

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m(3)/m(3) for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m(3)/m(3) and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

Statistical Analysis of Wrist Pulse Signals Obtained Under Different Food Intake Conditions

It is well known that wrist pulse signals contain information about the status of health of a person and hence diagnosis based on pulse signals has assumed great importance since long time. In this paper the efficacy of signal processing techniques in extracting useful information from wrist pulse signals has been demonstrated by using signals recorded under two different experimental conditions viz. before lunch condition and after lunch condition. We have used Pearson's product-moment correlation coefficient, which is an effective measure of phase synchronization, in making a statistical analysis of wrist pulse signals. Contour plots and box plots are used to illustrate various differences. Two-sample t-tests show that the correlations show statistically significant differences between the groups. Results show that the correlation coefficient is effective in distinguishing the changes taking place after having lunch. This paper demonstrates the ability of the wrist pulse signals in detecting changes occurring under two different conditions. The study assumes importance in view of limited literature available on the analysis of wrist pulse signals in the case of food intake and also in view of its potential health care applications.

Fuzzy Approach to Rank Global Climate Models

Eleven coupled model intercomparison project 3 based global climate models are evaluated for the case study of Upper Malaprabha catchment, India for precipitation rate. Correlation coefficient, normalised root mean square deviation, and skill score are considered as performance indicators for evaluation in fuzzy environment and assumed to have equal impact on the global climate models. Fuzzy technique for order preference by similarity to an ideal solution is used to rank global climate models. Top three positions are occupied by MIROC3, GFDL2.1 and GISS with relative closeness of 0.7867, 0.7070, and 0.7068. IPSL-CM4, NCAR-PCMI occupied the tenth and eleventh positions with relative closeness of 0.4959 and 0.4562.