Water quality estimation by optical remote sensing in boreal lakes

In lake-rich regions, the gathering of information about water quality is challenging because only a small proportion of the lakes can be assessed each year by conventional methods. One of the techniques for improving the spatial and temporal representativeness of lake monitoring is remote sensing from satellites and aircrafts. The experimental material included detailed optical measurements in 11 lakes, air- and spaceborne remote sensing measurements with concurrent field sampling, automatic raft measurements and a national dataset of routine water quality measurements from over 1100 lakes. The analyses of the spatially high-resolution airborne remote sensing data from eutrophic and mesotrophic lakes showed that one or a few discrete water quality observations using conventional monitoring can yield a clear over- or underestimation of the overall water quality in a lake. The use of TM-type satellite instruments in addition to routine monitoring results substantially increases the number of lakes for which water quality information can be obtained. The preliminary results indicated that coloured dissolved organic matter (CDOM) can be estimated with TM-type satellite instruments, which could possibly be utilised as an aid in estimating the role of lakes in global carbon budgets. Based on the results of reflectance modelling and experimental data, MERIS satellite instrument has optimal or near-optimal channels for the estimation of turbidity, chlorophyll a and CDOM in Finnish lakes. MERIS images with a 300 m spatial resolution can provide water quality information in different parts of large and medium-sized lakes, and in filling in the gaps resulting from conventional monitoring. Algorithms that would not require simultaneous field data for algorithm training would increase the amount of remote sensing-based information available for lake monitoring. The MERIS Boreal Lakes processor, trained with the optical data and concentration ranges provided by this study, enabled turbidity estimations with good accuracy without the need for algorithm correction with field measurements, while chlorophyll a and CDOM estimations require further development of the processor. The accuracy of interpreting chlorophyll a via semi empirical algorithms can be improved by classifying lakes prior to interpretation according to their CDOM level and trophic status. Optical modelling indicated that the spectral diffuse attenuation coefficient can be estimated with reasonable accuracy from the measured water quality concentrations. This provides more detailed information on light attenuation from routine monitoring measurements than is available through the Secchi disk transparency. The results of this study improve the interpretation of lake water quality by remote sensing and encourage the use of remote sensing in lake monitoring.

A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.

Microwave-assisted selective deoxygenation of layer- and chain-containing oxides

Very rapid (within 5 min), selective, single-step deoxygenation of layer- and chain-containing oxides, MoO3, CrO3, V2O5, alpha-VOPO4 . 2H(2)O and Ag6Mo10O33 has been accomplished using graphitic carbon in a microwave-assisted reaction. The products were found to be MoO2, Cr2O3, VO2, VPO4 and a mixture of (Ag + MoO2), respectively. Products were characterised by X-ray diffraction (XRD), differential scanning calorimetry (DSC), IR and electron paramagnetic resonance (EPR) spectroscopies. Although conventional methods of preparing these materials are tedious, the present method is simple, fast and yields very homogeneous products of good crystallinity. Our results reveal that while layer- and chain-containing oxides undergo rapid microwave-assisted carbothermal reduction, the non-layered materials do not. The high structural selectivity of these reactions is suggestive of the topochemical nature of the fast reduction process.

A facile route to synthesize silver nanoparticles in polyelectrolyte capsules

We are reporting a novel green approach to incorporate silver nanoparticles (NPs) selectively in the polyelectrolyte capsule shell for remote opening of polyelectrolyte capsules. This approach involves in situ reduction of silver nitrate to silver NPs using PEG as a reducing agent (polyol reduction method). These nanostructured capsules were prepared via layer by layer (LbL) assembly of poly(allylamine hydrochloride) (PAH) and dextran sulfate (DS) on silica template followed by the synthesis of silver NPs and subsequently the dissolution of the silica core. The size of silver nanoparticles synthesized was 60 +/- 20 nm which increased to 100 +/- 20 nm when the concentration of AgNO3 increased from 25 mM to 50 mM. The incorporated silver NPs induced rupture and deformation of the capsules under laser irradiation. This method has advantages over other conventional methods involving chemical agents that are associated with cytotoxicity in biological applications such as drug delivery and catalysis. (C) 2011 Elsevier B.V. All rights reserved.

Large deformation finite element analysis of laminated circular composite plates

A 48 d.o.f., four-noded quadrilateral laminated composite shell finite element is particularised to a sector finite element and is used for the large deformation analysis of circular composite laminated plates. The strain-displacement relationships for the sector element are obtained by reducing those of the quadrilateral shell finite element by substituting proper values for the geometric parameters. Subsequently, the linear and tangent stiffness matrices are formulated using conventional methods. The Newton-Raphson method is employed as the nonlinear solution technique. The computer code developed is validated by solving an isotropic case for which results are available in the literature. The method is then applied to solve problems of cylindrically orthotropic circular plates. Some of the results of cylindrically orthotropic case are compared with those available in the literature. Subsequently, application is made to the case of laminated composite circular plates having different lay-up schemes. The computer code can handle symmetric/unsymmetric lay-up schemes. The large displacement analysis is useful in estimating the damage in composite plates caused by low-velocity impact.

Synthesis of inorganic solids using microwaves

Use of microwaves in the synthesis of materials is gaining importance. Microwave-assisted synthesis is generally much faster, cleaner, and more economical than the conventional methods. A variety of materials such as carbides, nitrides, complex oxides, silicides, zeolites, apatite, etc. have been synthesized using microwaves. Many of these are of industrial and technological importance. An understanding of the microwave interaction with materials has been based on concepts of dielectric heating and of the resonance absorption due to rotational excitation. This review presents a summary of recent reports of microwave synthesis of inorganic materials. Various observations regarding microwave interaction with materials are also briefly discussed.

Regionalization of precipitation in data sparse areas using large scale atmospheric variables - A fuzzy clustering approach

Delineation of homogeneous precipitation regions (regionalization) is necessary for investigating frequency and spatial distribution of meteorological droughts. The conventional methods of regionalization use statistics of precipitation as attributes to establish homogeneous regions. Therefore they cannot be used to form regions in ungauged areas, and they may not be useful to form meaningful regions in areas having sparse rain gauge density. Further, validation of the regions for homogeneity in precipitation is not possible, since the use of the precipitation statistics to form regions and subsequently to test the regional homogeneity is not appropriate. To alleviate this problem, an approach based on fuzzy cluster analysis is presented. It allows delineation of homogeneous precipitation regions in data sparse areas using large scale atmospheric variables (LSAV), which influence precipitation in the study area, as attributes. The LSAV, location parameters (latitude, longitude and altitude) and seasonality of precipitation are suggested as features for regionalization. The approach allows independent validation of the identified regions for homogeneity using statistics computed from the observed precipitation. Further it has the ability to form regions even in ungauged areas, owing to the use of attributes that can be reliably estimated even when no at-site precipitation data are available. The approach was applied to delineate homogeneous annual rainfall regions in India, and its effectiveness is illustrated by comparing the results with those obtained using rainfall statistics, regionalization based on hard cluster analysis, and meteorological sub-divisions in India. (C) 2011 Elsevier B.V. All rights reserved.

On Selection of Features for Regional Hydrologic Studies

Feature selection is an important first step in regional hydrologic studies (RHYS). Over the past few decades, advances in data collection facilities have resulted in development of data archives on a variety of hydro-meteorological variables that may be used as features in RHYS. Currently there are no established procedures for selecting features from such archives. Therefore, hydrologists often use subjective methods to arrive at a set of features. This may lead to misleading results. To alleviate this problem, a probabilistic clustering method for regionalization is presented to determine appropriate features from the available dataset. The effectiveness of the method is demonstrated by application to regionalization of watersheds in conterminous United States for low flow frequency analysis. Plausible homogeneous regions that are formed by using the proposed clustering method are compared with those from conventional methods of regionalization using L-moment based homogeneity tests. Results show that the proposed methodology is promising for RHYS.

Colloidal calcium nanoparticles: digestive ripening in the presence of a capping agent and coalescence of particles under an electron beam

The nanochemistry of calcium remains unexplored, which is largely due to the inaccessibility of calcium nanoparticles in an easy to handle form by conventional methods of synthesis as well as its highly reactive and pyrophoric nature. The synthesis of colloidal Ca nanoparticles by the solvated metal atom dispersion (SMAD) method is described. The as-prepared Ca-THF nanoparticles, which are polydisperse, undergo digestive ripening in the presence of a capping agent, hexadecyl amine (HDA) to afford highly monodisperse colloids consisting of 2-3 nm sized Ca-HDA nanoparticles. These are quite stable towards precipitation for long periods of time, thereby providing access to the study of the nanochemistry of Ca. Particles synthesized in this manner were characterized by UV-visible spectroscopy, high resolution electron microscopy, and powder X-ray diffraction methods. Under an electron beam, two adjacent Ca nanoparticles undergo coalescence to form a larger particle.

Bufferless lysis of erythrocytes for isolation of hemoglobin using modified cellulose acetate membranes

This paper presents a modified cellulose acetate membrane prepared using a dry casting technique that can be used to perform lysis of erythrocytes and isolation of hemoglobin. Isolation of hemoglobin is thus achieved without the use of lysis buffers. Cellulose acetate (CA) membranes are embedded with ammonium chloride (NH4Cl) and potassium bicarbonate (KHCO3), which act as lysing agents. The presence of embedded salts is confirmed using EDS analysis. The pores in the CA membrane act as filters. The average pore size in these membranes is designed to be 1.5 mu M, as characterized by SEM analysis, so that they allow hemoglobin to pass through and block all other cells and unlysed erythrocytes present in blood. When a drop of blood is added to the membrane, the NH4Cl and KHCO3 embedded in the membrane dissolve in plasma and lyse the erythrocytes. The filtered hemoglobin is characterized using UV-Vis Spectroscopy. The results indicate extraction of higher concentration of hemoglobin compared with conventional methods.

HUMAN DETECTION USING SPARSE REPRESENTATION

The problem of human detection is challenging, more so, when faced with adverse conditions such as occlusion and background clutter. This paper addresses the problem of human detection by representing an extracted feature of an image using a sparse linear combination of chosen dictionary atoms. The detection along with the scale finding, is done by using the coefficients obtained from sparse representation. This is of particular interest as we address the problem of scale using a scale-embedded dictionary where the conventional methods detect the object by running the detection window at all scales.

Optimisation of an asymmetric polymerase chain reaction assay for the amplification of single-stranded DNA from Wuchereria bancrofti for electrochemical detection

Single-stranded DNA (ssDNA) is a prerequisite for electrochemical sensor-based detection of parasite DNA and other diagnostic applications. To achieve this detection, an asymmetric polymerase chain reaction method was optimised. This method facilitates amplification of ssDNA from the human lymphatic filarial parasite Wuchereria bancrofti. This procedure produced ssDNA fragments of 188 bp in a single step when primer pairs (forward and reverse) were used at a 100:1 molar ratio in the presence of double-stranded template DNA. The ssDNA thus produced was suitable for immobilisation as probe onto the surface of an Indium tin oxide electrode and hybridisation in a system for sequence-specific electrochemical detection of W. bancrofti. The hybridisation of the ssDNA probe and target ssDNA led to considerable decreases in both the anodic and the cathodic currents of the system's redox couple compared with the unhybridised DNA and could be detected via cyclic voltammetry. This method is reproducible and avoids many of the difficulties encountered by conventional methods of filarial parasite DNA detection; thus, it has potential in xenomonitoring.

Synthesis of substituted nitroolefins: a copper catalyzed nitrodecarboxylation of unsaturated carboxylic acids

A novel, mild and convenient method for the nitrodecarboxylation of substituted cinnamic acid derivatives to their nitroolefins is achieved using a catalytic amount of CuCl (10 mol%) and tert-butyl nitrite (2 equiv.) as a nitrating agent in the presence of air. This reaction provides a useful method for the synthesis of beta,beta-disubstituted nitroolefin derivatives, which are generally difficult to access from other conventional methods. Additionally, this reaction is selective as the E-isomer of the acid derivatives furnishes the corresponding E-nitroolefins. One more salient feature of the method is, unlike other methods, no metal nitrates or HNO3 are employed for the transformation.

Three-dimensional localization of multiple acoustic sources in shallow ocean with non-Gaussian noise

In this paper, a low-complexity algorithm SAGE-USL is presented for 3-dimensional (3-D) localization of multiple acoustic sources in a shallow ocean with non-Gaussian ambient noise, using a vertical and a horizontal linear array of sensors. In the proposed method, noise is modeled as a Gaussian mixture. Initial estimates of the unknown parameters (source coordinates, signal waveforms and noise parameters) are obtained by known/conventional methods, and a generalized expectation maximization algorithm is used to update the initial estimates iteratively. Simulation results indicate that convergence is reached in a small number of (<= 10) iterations. Initialization requires one 2-D search and one 1-D search, and the iterative updates require a sequence of 1-D searches. Therefore the computational complexity of the SAGE-USL algorithm is lower than that of conventional techniques such as 3-D MUSIC by several orders of magnitude. We also derive the Cramer-Rao Bound (CRB) for 3-D localization of multiple sources in a range-independent ocean. Simulation results are presented to show that the root-mean-square localization errors of SAGE-USL are close to the corresponding CRBs and significantly lower than those of 3-D MUSIC. (C) 2014 Elsevier Inc. All rights reserved.

Spatio-temporal correlations in aqueous systems: computational studies of static and dynamic heterogeneity by 2D-IR spectroscopy

Local heterogeneity is ubiquitous in natural aqueous systems. It can be caused locally by external biomolecular subsystems like proteins, DNA, micelles and reverse micelles, nanoscopic materials etc., but can also be intrinsic to the thermodynamic nature of the aqueous solution itself (like binary mixtures or at the gas-liquid interface). The altered dynamics of water in the presence of such diverse surfaces has attracted considerable attention in recent years. As these interfaces are quite narrow, only a few molecular layers thick, they are hard to study by conventional methods. The recent development of two dimensional infra-red (2D-IR) spectroscopy allows us to estimate length and time scales of such dynamics fairly accurately. In this work, we present a series of interesting studies employing two dimensional infra-red spectroscopy (2D-IR) to investigate (i) the heterogeneous dynamics of water inside reverse micelles of varying sizes, (ii) supercritical water near the Widom line that is known to exhibit pronounced density fluctuations and also study (iii) the collective and local polarization fluctuation of water molecules in the presence of several different proteins. The spatio-temporal correlation of confined water molecules inside reverse micelles of varying sizes is well captured through the spectral diffusion of corresponding 2D-IR spectra. In the case of supercritical water also, we observe a strong signature of dynamic heterogeneity from the elongated nature of the 2D-IR spectra. In this case the relaxation is ultrafast. We find remarkable agreement between the different tools employed to study the relaxation of density heterogeneity. For aqueous protein solutions, we find that the calculated dielectric constant of the respective systems unanimously shows a noticeable increment compared to that of neat water. However, the `effective' dielectric constant for successive layers shows significant variation, with the layer adjacent to the protein having a much lower value. Relaxation is also slowest at the surface. We find that the dielectric constant achieves the bulk value at distances more than 3 nm from the surface of the protein.