Non-linear regression model for spatial variation in precipitation chemistry for South India

Chemical composition of rainwater changes from sea to inland under the influence of several major factors - topographic location of area, its distance from sea, annual rainfall. A model is developed here to quantify the variation in precipitation chemistry under the influence of inland distance and rainfall amount. Various sites in India categorized as 'urban', 'suburban' and 'rural' have been considered for model development. pH, HCO3, NO3 and Mg do not change much from coast to inland while, SO4 and Ca change is subjected to local emissions. Cl and Na originate solely from sea salinity and are the chemistry parameters in the model. Non-linear multiple regressions performed for the various categories revealed that both rainfall amount and precipitation chemistry obeyed a power law reduction with distance from sea. Cl and Na decrease rapidly for the first 100 km distance from sea, then decrease marginally for the next 100 km, and later stabilize. Regression parameters estimated for different cases were found to be consistent (R-2 similar to 0.8). Variation in one of the parameters accounted for urbanization. Model was validated using data points from the southern peninsular region of the country. Estimates are found to be within 99.9% confidence interval. Finally, this relationship between the three parameters - rainfall amount, coastline distance, and concentration (in terms of Cl and Na) was validated with experiments conducted in a small experimental watershed in the south-west India. Chemistry estimated using the model was in good correlation with observed values with a relative error of similar to 5%. Monthly variation in the chemistry is predicted from a downscaling model and then compared with the observed data. Hence, the model developed for rain chemistry is useful in estimating the concentrations at different spatio-temporal scales and is especially applicable for south-west region of India. (C) 2008 Elsevier Ltd. All rights reserved.

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM+ data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions.

Gaussian Schell-model beams and general shape invariance

We present two six-parameter families of anisotropic Gaussian Schell-model beams that propagate in a shape-invariant manner, with the intensity distribution continuously twisting about the beam axis. The two families differ in the sense or helicity of this beam twist. The propagation characteristics of these shape-invariant beams are studied, and the restrictions on the beam parameters that arise from the optical uncertainty principle are brought out. Shape invariance is traced to a fundamental dynamical symmetry that underlies these beams. This symmetry is the product of spatial rotation and fractional Fourier transformation.

Standardization and validation of an induced ovulation model system in buffalo cows: Characterization of gene expression changes in the periovulatory follicle

In bovines characterization of biochemical and molecular determinants of the dominant follicle before and during different time intervals after gonadotrophin surge requires precise identification of the dominant follicle from a follicular wave. The objectives of the present study were to standardize an experimental model in buffalo cows for accurately identifying the dominant follicle of the first wave of follicular growth and characterize changes in follicular fluid hormone concentrations as well as expression patterns of various genes associated with the process of ovulation. From the day of estrus (day 0), animals were subjected to blood sampling and ultrasonography for monitoring circulating progesterone levels and follicular growth. On day 7 of the cycle, animals were administered a PGF2α analogue (Tiaprost Trometamol, 750 μg i.m.) followed by an injection of hCG (2000 IU i.m.) 36 h later. Circulating progesterone levels progressively increased from day 1 of the cycle to 2.26 ± 0.17 ng/ml on day 7 of the cycle, but declined significantly after PGF2α injection. A progressive increase in the size of the dominant follicle was observed by ultrasonography. The follicular fluid estradiol and progesterone concentrations in the dominant follicle were 600 ± 16.7 and 38 ± 7.6 ng/ml, respectively, before hCG injection and the concentration of estradiol decreased to 125.8 ± 25.26 ng/ml, but concentration of progesterone increased to 195 ± 24.6 ng/ml, 24 h post-hCG injection. Inh-α and Cyp19A1 expressions in granulosa cells were maximal in the dominant follicle and declined in response to hCG treatment. Progesterone receptor, oxytocin and cycloxygenase-2 expressions in granulosa cells, regarded as markers of ovulation, were maximal at 24 h post-hCG. The expressions of genes belonging to the super family of proteases were also examined; Cathepsin L expression decreased, while ADAMTS 3 and 5 expressions increased 24 h post-hCG treatment. The results of the current study indicate that sequential treatments of PGF2α and hCG during early estrous cycle in the buffalo cow leads to follicular growth that culminates in ovulation. The model system reported in the present study would be valuable for examining temporo-spatial changes in the periovulatory follicle immediately before and after the onset of gonadotrophin surge.

Indian Ocean sea surface salinity variations in a coupled model

The variability of the sea surface salinity (SSS) in the Indian Ocean is studied using a 100-year control simulation of the Community Climate System Model (CCSM 2.0). The monsoon-driven seasonal SSS pattern in the Indian Ocean, marked by low salinity in the east and high salinity in the west, is captured by the model. The model overestimates runoff int the Bay of Bengal due to higher rainfall over the Himalayan-Tibetan regions which drain into the Bay of Bengal through Ganga-Brahmaputra rivers. The outflow of low-salinity water from the Bay of Bengal is to strong in the model. Consequently, the model Indian Ocean SSS is about 1 less than that seen in the climatology. The seasonal Indian Ocean salt balance obtained from the model is consistent with the analysis from climatological data sets. During summer, the large freshwater input into the Bay of Bengal and its redistribution decide the spatial pattern of salinity tendency. During winter, horizontal advection is the dominant contributor to the tendency term. The interannual variability of the SSS in the Indian Ocean is about five times larger than that in coupled model simulations of the North Atlantic Ocean. Regions of large interannual standard deviations are located near river mouths in the Bay of Bengal and in the eastern equatorial Indian Ocean. Both freshwater input into the ocean and advection of this anomalous flux are responsible for the generation of these anomalies. The model simulates 20 significant Indian Ocean Dipole (IOD) events and during IOD years large salinity anomalies appear in the equatorial Indian Ocean. The anomalies exist as two zonal bands: negative salinity anomalies to the north of the equator and positive to the south. The SSS anomalies for the years in which IOD is not present and for ENSO years are much weaker than during IOD years. Significant interannual SSS anomalies appear in the Indian Ocean only during IOD years.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Analysis of Dynamic Heterogeneity in a Glass Former from the Spatial Correlations of Mobility

The growth of characteristic length scales associated with dynamic heterogeneity in glass-forming liquids is investigated in an extensive computational study of a four-point, time-dependent structure factor defined from spatial correlations of mobility, for a model liquid for system sizes extending up to 351 232 particles, in constant-energy and constant-temperature ensembles. Our estimates for dynamic correlation lengths and susceptibilities are consistent with previous results from finite size scaling. We find scaling exponents that are inconsistent with predictions from inhomogeneous mode coupling theory and a recent simulation confirmation of these predictions.

An Accurate Model For Interference From Spatially Distributed Shadowed Users in CDMA Uplinks

A detailed characterization of interference power statistics in CDMA systems is of considerable practical and theoretical interest. Such a characterization for uplink inter-cell interference has been difficult because of transmit power control, randomness in the number of interfering mobile stations, and randomness in their locations. We develop a new method to model the uplink inter-cell interference power as a lognormal distribution, and show that it is an order of magnitude more accurate than the conventional Gaussian approximation even when the average number of mobile stations per cell is relatively large and even outperforms the moment-matched lognormal approximation considered in the literature. The proposed method determines the lognormal parameters by matching its moment generating function with a new approximation of the moment generating function for the inter-cell interference. The method is tractable and exploits the elegant spatial Poisson process theory. Using several numerical examples, the accuracy of the proposed method in modeling the probability distribution of inter-cell interference is verified for both small and large values of interference.

Imaging in vivo Neuronal Transport in Genetic Model Organisms Using Microfluidic Devices

Microfluidic devices have been developed for imaging behavior and various cellular processes in Caenorhabditis elegans, but not subcellular processes requiring high spatial resolution. In neurons, essential processes such as axonal, dendritic, intraflagellar and other long-distance transport can be studied by acquiring fast time-lapse images of green fluorescent protein (GFP)-tagged moving cargo. We have achieved two important goals in such in vivo studies namely, imaging several transport processes in unanesthetized intact animals and imaging very early developmental stages. We describe a microfluidic device for immobilizing C. elegans and Drosophila larvae that allows imaging without anesthetics or dissection. We observed that for certain neuronal cargoes in C. elegans, anesthetics have significant and sometimes unexpected effects on the flux. Further, imaging the transport of certain cargo in early developmental stages was possible only in the microfluidic device. Using our device we observed an increase in anterograde synaptic vesicle transport during development corresponding with synaptic growth. We also imaged Q neuroblast divisions and mitochondrial transport during early developmental stages of C. elegans and Drosophila, respectively. Our simple microfluidic device offers a useful means to image high-resolution subcellular processes in C. elegans and Drosophila and can be readily adapted to other transparent or translucent organisms.

Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics

Beavers are often found to be in conflict with human interests by creating nuisances like building dams on flowing water (leading to flooding), blocking irrigation canals, cutting down timbers, etc. At the same time they contribute to raising water tables, increased vegetation, etc. Consequently, maintaining an optimal beaver population is beneficial. Because of their diffusion externality (due to migratory nature), strategies based on lumped parameter models are often ineffective. Using a distributed parameter model for beaver population that accounts for their spatial and temporal behavior, an optimal control (trapping) strategy is presented in this paper that leads to a desired distribution of the animal density in a region in the long run. The optimal control solution presented, imbeds the solution for a large number of initial conditions (i.e., it has a feedback form), which is otherwise nontrivial to obtain. The solution obtained can be used in real-time by a nonexpert in control theory since it involves only using the neural networks trained offline. Proper orthogonal decomposition-based basis function design followed by their use in a Galerkin projection has been incorporated in the solution process as a model reduction technique. Optimal solutions are obtained through a "single network adaptive critic" (SNAC) neural-network architecture.

Investigation of the characteristics of an SF6 rotating arc by a mathematical model

A mathematical model has been developed for predicting the performance of rotating arcs in SF6 gas by considering the energy balance and force balance equations. The finite difference technique has been adopted for the computer simulation of the arc characteristics. This method helps in considering the spatial variation of the transport and radiative properties of the arc. All the three heat loss mechanisms-conduction, convection, and radiation-have been considered. Results obtained over a 10 ms (half cycle of 50 Hz wave) current flow period for 1.4 kA (peak) and 4.2 kA (peak), show that the proposed arc model gives the expected behavior of the arc over the range of currents studied.

Autocalibration of dynamic bacterial growth model for water distribution system using GA

The presence of residual chlorine and organic matter govern the bacterial regrowth within a water distribution system. The bacterial growth model is essential to predict the spatial and temporal variation of all these substances throughout the system. The parameters governing the bacterial growth and biodegradable dissolved organic carbon (BDOC) utilization are difficult to determine by experimentation. In the present study, the estimation of these parameters is addressed by using simulation-optimization procedure. The optimal solution by genetic algorithm (GA) has indicated that the proper combination of parameter values are significant rather than correct individual values. The applicability of the model is illustrated using synthetic data generated by introducing noise in to the error-free measurements. The GA was found to be a potential tool in estimating the parameters controlling the bacterial growth and BDOC utilization. Further, the GA was also used for evaluating the sensitivity issues relating parameter values and objective function. It was observed that mu and k(cl) are more significant and dominating compared to the other parameters. But the magnitude of the parameters is also an important issue in deciding the dominance of a particular parameter. GA is found to be a useful tool in autocalibration of bacterial growth model and a sensitivity study of parameters.