Biomass Determination in Some Species Using a Geometric Equivalent Technique of Plankton Organisms

The plankton collected from three fish ponds shows a considerable variability within a pond, between the ponds and in the course of the year. The individual volumes or weights are (minimum—mean—maximum): Gleotrichia echinulata 41–252–879 μg³, Chlorella vulgaris 18–42–94 μm³, Oscillatoria amphigranulata 99–306–827 μm³, Brachionus plicatilis 1–4.918 μg, nauplia 0.5–1.25–2.35 μg, Mesocyclops hyalinus 4–45–369—μg, Heliodiaptomus viduus 41–202–288 μg, Daphnia carinata 186–1468–4578 μg.     

A study on the sensitivity of the radar scattering coefficient to oceanic winds

Sensitivity of the radar scattering coefficient to the oceanic wind vector in the midangular range for frequencies from L- to Ku- band is studied. This is based on computations of scattering coefficient via the two-scale scattering theory employing a semi-empirical model for the ocean spectrum suggested by Fung and Lee and the slope distribution by Cox and Munk. Higher frequency and incident angles of over 45 degrees seem to yield better wind sensitivity.     

Spectral P-wave magnitudes, Aki's ω-square model and source parameters of earthquakes

The earthquake magnitude is a quantity sampling the spectrum of the far-field radiation.With a suit of properly defined magnitudes in a sufficiently broad range of frequencies, the radiated spectrum can be restored and analyzed.A method is proposed for the extraction of stress drop, fault length and seismic moment from magnitudes on a routine basis. Thereby, the theoretical spectrum as predicted by the ω-square model of Aki is utilized.In applying the method to earthquakes which occurred in several parts of Asia over a time-span of 3 years, it is shown that in most cases earthquakes in a given region are characterized by the same stress drop, varying however from region to region. In one region a change of the stress drop with time is found, eventually indicating a change in the state of stress in the particular region during the time interval investigated.     

Forecasting prices of single family homes using GIS-defined neighborhoods

We estimate spatiotemporal models of average neighborhood single family home prices to use in predicting individual property prices. Average home-price variations are explained in terms of changes in average neighborhood house attributes, spatial attributes, and temporal economic variables. Models adopting three different definitions of neighborhoods are estimated with quarterly cross-sectional data over the period 2000–2004 from four cities in Southern California. Heteroscedasticity and autocorrelation problems are detected and adjusted for via a sequential routine. Results of these models suggest that forecasts obtained using city neighborhood average price equations may have advantage over forecasts obtained using city aggregated price equations.      

Space time evolution of sand bed topography and associated flow turbulence: experiments with statistical analysis

A series of flume experiments were conducted with varying the flow discharges at the Fluvial Mechanics Laboratory of Indian Statistical Institute (Kolkata) to understand the co-evolution patterns of generating bed forms and the corresponding flow turbulence. Instantaneous bed elevations and velocity components were recorded continuously for sufficient time using high resolution instruments, such as, Ultrasonic Ranging System and acoustic Doppler velocimeter, at some spatial location over the deformed bed for each flow discharge. Increase in mean bed elevations and bed-slopes was found to be increased in discharge. Heavy-tailed nature of the probability density functions of magnitude of bed elevation increments, magnitude of single continuous bed elevation increments and instantaneous Reynolds shear stresses along three planes were analyzed using Pareto and truncated Pareto distributions. The spectral analysis of bed elevations revealed that the slope of log–log linearity increased with increase in flow discharge. Wavelet cross-correlations depicted strong dependence of bed form evolution on the corresponding instantaneous Reynolds shear stress along xz-plane. A Gram–Charlier type of distribution was used to estimate the probability density function of fluctuating velocity components, instantaneous Reynolds shear stresses along three planes and the joint probability density functions of the fluctuating velocity components, which showed good fit with the experimental data.     

Relative Radiometric Normalisation - performance testing of selected techniques and impact analysis on vegetation and water bodies

In this paper, six image-based Relative Radiometric Normalization (RRN) techniques were applied to normalize the bi-temporal Landsat 5 TM data-set. RRN techniques do not require any atmospheric and ground information at the time of image acquisition. The target image for the year 2009 was normalized in such a way that it resembled the atmospheric and sensor conditions similar to those under which the reference image of the same season for the year 1990 was acquired. Among the selected methods applied, it was found that the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) method performed better, based on the error statistic. The IR-MAD technique was found to be advantageous as it identified a large set of true time-invariant pixels automatically from the change background using iterative canonical component analysis. The technique also stretches the values of Normalized Difference Vegetation Index and Normalized Difference Water Index and may help to distinguish different vegetation and water bodies better.     

Metrics for characterizing network structure and node importance in Spatial Social Networks

Social Network Analysis offers powerful tools to analyze the structure of relationships between a set of people. However, the addition of spatial information poses new challenges, as nodes are embedded simultaneously in network space and Euclidean space. While nearby nodes may not form social ties, ties may exist at a distance, a configuration ill-suited for traditional spatial metrics that assume adjacent objects are related. As such, there are relatively few metrics to describe these nuanced situations. We advance the burgeoning field of spatial social network analysis by introducing a set of new metrics. Specifically, we introduce the spatial social network schema, tuning parameter and the flattening ratio, each of which leverages the notion of ‘distance’ to augment insights obtained by relying on topology alone. These methods are used to answer the questions: What is the social and spatial structure of the network? Who are the key individuals at different spatial scales? We use two synthetic networks with properties mimicking the ones reported in the literature as validation datasets and a case study of employer–employee network. The methods characterize the employer–employee as spatially loose with predominantly local connections and identify key individuals responsible for keeping the network connected at different spatial scales.     

Seismic Hazard Assessment of Dhanbad City,India, by deterministic approach

Natural Hazards - Dhanbad is the largest producer of coal in the state of Jharkhand, India, and hence, it is one of the most important cities in the country. Considering the present-day importance...     

GIS based spatial data analysis for landslide susceptibility mapping

Landslide susceptibility map delineates the potential zones for landslides occurrence. The paper presents a statistical approach through spatial data analysis in GIS for landslide susceptibility mapping in parts of Sikkim Himalaya. Six important causative factors for landslide occurrences were selected and corresponding thematic data layers were prepared in GIS. Topographic maps,satellite image,field data and published maps constitute the input data for thematic layer preparation. Numerical weights for different categories of these factors were determined based on a statistical approach and the weighted thematic layers were integrated in GIS environment to generate the landslide susceptibility map of the area. The landslide susceptibility map classifies the area into five different landslide susceptible zones i.e.,very high,high,moderate,low and very low. This map was validated using the existing landslide distribution in the area.