Classification of microwatersheds based on morphological characteristics

Three classification techniques, namely, K-means Cluster Analysis (KCA), Fuzzy Cluster Analysis (FCA), and Kohonen Neural Networks (KNN) were employed to group 25 microwatersheds of Kherthal watershed, Rajasthan into homogeneous groups for formulating the basis for suitable conservation and management practices. Ten parameters, mainly, morphological, namely, drainage density (D-d), bifurcation ratio (R-b), stream frequency (F-u), length of overland flow (L-o), form factor (R-f), shape factor (B-s), elongation ratio (R-e), circulatory ratio (R-c), compactness coefficient (C-c) and texture ratio (T) are used for the classification. Optimal number of groups is chosen, based on two cluster validation indices Davies-Bouldin and Dunn's. Comparative analysis of various clustering techniques revealed that 13 microwatersheds out of 25 are commonly suggested by KCA, FCA and KNN i.e., 52%; 17 microwatersheds out of 25 i.e., 68% are commonly suggested by KCA and FCA whereas these are 16 out of 25 in FCA and KNN (64%) and 15 out of 25 in KNN and CA (60%). It is observed from KNN sensitivity analysis that effect of various number of epochs (1000, 3000, 5000) and learning rates (0.01, 0.1-0.9) on total squared error values is significant even though no fixed trend is observed. Sensitivity analysis studies revealed that microwatershecls have occupied all the groups even though their number in each group is different in case of further increase in the number of groups from 5 to 6, 7 and 8. (C) 2010 International Association of Hydro-environment Engineering and Research, Asia Pacific Division. Published by Elsevier B.V. All rights reserved.

Regional Flood Frequency Analysis using Two-level Clustering Approach

Clustering techniques are used in regional flood frequency analysis (RFFA) to partition watersheds into natural groups or regions with similar hydrologic responses. The linear Kohonen's self‐organizing feature map (SOFM) has been applied as a clustering technique for RFFA in several recent studies. However, it is seldom possible to interpret clusters from the output of an SOFM, irrespective of its size and dimensionality. In this study, we demonstrate that SOFMs may, however, serve as a useful precursor to clustering algorithms. We present a two‐level. SOFM‐based clustering approach to form regions for FFA. In the first level, the SOFM is used to form a two‐dimensional feature map. In the second level, the output nodes of SOFM are clustered using Fuzzy c‐means algorithm to form regions. The optimal number of regions is based on fuzzy cluster validation measures. Effectiveness of the proposed approach in forming homogeneous regions for FFA is illustrated through application to data from watersheds in Indiana, USA. Results show that the performance of the proposed approach to form regions is better than that based on classical SOFM.

Validation of processing maps for 304L stainless steel using hot forging, rolling and extrusion

The development of a microstructure in 304L stainless steel during industrial hot-forming operations, including press forging (mean strain rate of 0.15 s(-1)), rolling/extrusion (2-5 s(-1)), and hammer forging (100 s(-1)) at different temperatures in the range 600-1200 degrees C, was studied with a view to validating the predictions of the processing map. The results have shown that excellent correlation exists between the regimes exhibited by the map and the product microstructures. 304L stainless steel exhibits instability bands when hammer forged at temperatures below 1100 degrees C, rolled/extruded below 1000 degrees C, or press forged below 800 degrees C. All of these conditions must be avoided in mechanical processing of the material. On the other hand, ideally, the material may be rolled, extruded, or press forged at 1200 degrees C to obtain a defect-free microstructure.

Cluster model of the glass transition

A cluster model of the glass transition has been developed, treating the relative size of the cluster as an order parameter. The model accounts for some of the features of the glass transition.

Cluster analysis of rainfall stations of the Indian peninsula

Principal component analysis is applied to derive patterns of temporal variation of the rainfall at fifty-three stations in peninsular India. The location of the stations in the coordinate space determined by the amplitudes of the two leading eigenvectors is used to delineate them into eight clusters. The clusters obtained seem to be stable with respect to variations in the grid of stations used. Stations within any cluster occur in geographically contiguous areas.

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.

Validation-Based Sparse Gaussian Process Classifier Design

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better orcomparable generalization performance over existing methods.

Influence of validation schemes in a laser Doppler anemometer period timing device for frequency measurement

The short duration of the Doppler signal and noise content in it necessitate a validation scheme to be incorporated in the electronic processor used for frequency measurement, There are several different validation schemes that can be employed in period timing devices. A detailed study of the influence of these validation schemes on the measured frequency has been reported here. These studies were carried out by using a combination of a fast A/D converter and computer. Doppler bursts obtained from an air flow were digitised and stored on magnetic discs. Suitable computer programs were then used to simulate the performance of period timing devices with different validation schemes and the frequency of the stored bursts were evaluated. It is found that best results are obtained when the validation scheme enables frequency measurement to be made over a large number of cycles within the burst.

High pressure transport behaviour of AgI-Ag2O-MoO3 glasses and the cluster model

The effect of pressure on the conductivity of fast ion conducting AgI-Ag2O-MoO3 glasses has been investigated down to 150 K. The observed variation of conductivities appears to support the application of cluster model to the ionic glasses.

Glass transition. A new approach based on cluster model of glasses

The structure of real glasses has been considered to be microheterogeneous, composed of clusters and connective tissue. Particles in the cluster are assumed to be highly correlated in positions. The tissue is considered to have a truly amorphous structure with its particles vibrating in highly anharmonic potentials. Glass transition is recognized as corresponding to the melting of clusters. A simple mathematical model has been developed which accounts for various known features associated with glass transition, such as range of glass transition temperature,T g, variation ofT g with pressure, etc. Expressions for configurational thermodynamic properties and transport properties of glass forming systems are derived from the model. The relevence and limitations of the model are also discussed.

Synthesis and characterization of a rare hemiacetalate bridged incomplete View the MathML source dicubane cluster

Reaction of 2-pyridinecarboxaldehyde [(Py)CHO] with Cu(NO3)2·2.5H2O in the presence of 4-aminopyridine and NaN3 in MeOH lead to an incomplete double-cubane [Cu4{PyCH(O)(OMe)}4(N3)4] (1) in 87% isolated yield, representing a rare type of metal cluster containing bridging hemiacetalate ligand [pyCH(O)(OMe)]−1 which was characterized by single crystal structure analysis and variable temperature magnetic behavior.

Electrochemisorption - A Cluster Approach

A simple semiempirical quantum chemical approach (Extended Huckel Theory) is shown to give a reasonable description of the electronic structural aspects of chemisorption on the mercury model surface. Chemisorptive interaction of alkali metal atoms and cations, halogen atoms and anions, and water molecules with a charge-neutralized hexagonal close-packed cluster of seven Hg atoms is studied. Adsorption of H, C, N and O atoms on the same model cluster is studied for comparison with earlier work. Chemisorption energies, charge transfer, interaction distance and hydration effects are discussed and compared with experimental results where available.

An HI study of three long-tailed irregular galaxies in the cluster Abell 1367

We present the results on the distribution and kinematics of HI gas with higher sensitivity and in one case of higher spectral resolution as well than reported earlier, of three irregular galaxies CGCG 097073, 097079 and 097087 (UGC 06697) in the cluster Abell 1367. These galaxies are known to exhibit long (50 - 75 kpc) tails of radio continuum and optical emission lines (H alpha) pointing away from the cluster centre and arcs of starformation on the opposite sides of the tails, These features as well as the HI properties, with two of the galaxies (CGCG 097073 and 097079) exhibiting sharper gradients in HI intensity on the side of the tails, are consistent with the HI gas being affected by the ram pressure of the intracluster medium. However the HI emission in all the three galaxies extends to much smaller distances than the radio-continuum and H alpha tails, and are possibly still bound to the parent galaxies. Approximately 20 - 30 per cent of the HI mass is seen to accumulate on the downstream side due to the effects of ram pressure.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.