Impact of Disturrance on Composition, Structure, And Floristics of Tropical Moist Forests in Uttara Kannada District, Western Ghats, India

Impact of disturbance on forest stand density, basal area, dbh class distribution of density and basal area, species richness, species diversity and similarity index was assessed through monitoring six, one-hectare, permanent forest plots after a period of 24 years in tropical moist forests of Uttara Kannada district, Western Ghats, India. It was observed that all sites lost trees due to removal by people and mortality. Loss of trees was more in sites that are easily accessible and closer to human habitation. In spite of a decrease in tree density, an increase in basal area was observed in some forest plots, which could be on account of stimulatory growth of surviving trees. Decrease in basal area in other sites indicates greater human pressure and overexploitation of trees. Preponderance of lower girth class trees, and a unimodal reverse `J-shaped' curve of density distribution as observed in majority of the sites in the benchmark year, was indicative of regenerating status of these forests. The decrease in number of species in all forest sites was due to indiscriminate removal of trees by people, without sparing species with only a few individuals, and also due to mortality of trees of rare species. Higher species richness and diversity in the lowest dbh class in most of the sites in the benchmark year is indicative of the existence of favorable conditions for sylvigenesis. The decrease in the similarity index suggests extirpation of species, favoring invasion and colonization by secondary species. To minimize human pressure on forests and to facilitate regeneration and growth, proper management planning and conservation measures are needed.

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static VAr compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static Var compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Knowledge-Based Approach Using Support Vector Machine for Transmission Line Distance Relay Co-ordination

In this paper, knowledge-based approach using Support Vector Machines (SVMs) are used for estimating the coordinated zonal settings of a distance relay. The approach depends on the detailed simulation studies of apparent impedance loci as seen by distance relay during disturbance, considering various operating conditions including fault resistance. In a distance relay, the impedance loci given at the relay location is obtained from extensive transient stability studies. SVMs are used as a pattern classifier for obtaining distance relay co-ordination. The scheme utilizes the apparent impedance values observed during a fault as inputs. An improved performance with the use of SVMs, keeping the reach when faced with different fault conditions as well as system power flow changes, are illustrated with an equivalent 265 bus system of a practical Indian Western Grid.

The Contribution of Rare Species to Community Phylogenetic Diversity across a Global Network of Forest Plots

Niche differentiation has been proposed as an explanation for rarity in species assemblages. To test this hypothesis requires quantifying the ecological similarity of species. This similarity can potentially be estimated by using phylogenetic relatedness. In this study, we predicted that if niche differentiation does explain the co-occurrence of rare and common species, then rare species should contribute greatly to the overall community phylogenetic diversity (PD), abundance will have phylogenetic signal, and common and rare species will be phylogenetically dissimilar. We tested these predictions by developing a novel method that integrates species rank abundance distributions with phylogenetic trees and trend analyses, to examine the relative contribution of individual species to the overall community PD. We then supplement this approach with analyses of phylogenetic signal in abundances and measures of phylogenetic similarity within and between rare and common species groups. We applied this analytical approach to 15 long-term temperate and tropical forest dynamics plots from around the world. We show that the niche differentiation hypothesis is supported in six of the nine gap-dominated forests but is rejected in the six disturbance-dominated and three gap-dominated forests. We also show that the three metrics utilized in this study each provide unique but corroborating information regarding the phylogenetic distribution of rarity in communities.

Simulation of length-preserving motions of flexible one dimensional oObjects using optimization

During the motion of one dimensional flexible objects such as ropes, chains, etc., the assumption of constant length is realistic. Moreover,their motion appears to be naturally minimizing some abstract distance measure, wherein the disturbance at one end gradually dies down along the curve defining the object. This paper presents purely kinematic strategies for deriving length-preserving transformations of flexible objects that minimize appropriate ‘motion’. The strategies involve sequential and overall optimization of the motion derived using variational calculus. Numerical simulations are performed for the motion of a planar curve and results show stable converging behavior for single-step infinitesimal and finite perturbations 1 as well as multi-step perturbations. Additionally, our generalized approach provides different intuitive motions for various problem-specific measures of motion, one of which is shown to converge to the conventional tractrix-based solution. Simulation results for arbitrary shapes and excitations are also included.

Natural motion of one-dimensional flexible objects using minimization approaches

For one-dimensional flexible objects such as ropes, chains, hair, the assumption of constant length is realistic for large-scale 3D motion. Moreover, when the motion or disturbance at one end gradually dies down along the curve defining the one-dimensional flexible objects, the motion appears ``natural''. This paper presents a purely geometric and kinematic approach for deriving more natural and length-preserving transformations of planar and spatial curves. Techniques from variational calculus are used to determine analytical conditions and it is shown that the velocity at any point on the curve must be along the tangent at that point for preserving the length and to yield the feature of diminishing motion. It is shown that for the special case of a straight line, the analytical conditions lead to the classical tractrix curve solution. Since analytical solutions exist for a tractrix curve, the motion of a piecewise linear curve can be solved in closed-form and thus can be applied for the resolution of redundancy in hyper-redundant robots. Simulation results for several planar and spatial curves and various input motions of one end are used to illustrate the features of motion damping and eventual alignment with the perturbation vector.

Analysis of voltage stability in multi-infeed HVDC systems with STATCOM

This paper presents the modelling and analysis of voltage stability at AC commutation bus in LCC (Line commutated converters) based multi-infeed HVDC system. The paper also presents the analysis of effects of various operating control modes in HVDC as well as location of disturbance on the voltage stability of the system under study. A new method of modelling the LCC converters as time varying admittance at the AC commutation bus is also presented in this paper. In this paper, the modelling of STATCOM for provision of dynamic voltage support at one of the AC buses of the HVDC system is presented. The reactive power injected by STATCOM is controlled by regulating the voltage of the AC bus to which STATCOM is connected. The case study also discusses the effects of various possible combinations of location of STATCOM and disturbance considered, on the voltage stability of the multi-infeed HVDC system.

CAVVA: Computational Affective Video-in-Video Advertising

Advertising is ubiquitous in the online community and more so in the ever-growing and popular online video delivery websites (e. g., YouTube). Video advertising is becoming increasingly popular on these websites. In addition to the existing pre-roll/post-roll advertising and contextual advertising, this paper proposes an in-stream video advertising strategy-Computational Affective Video-in-Video Advertising (CAVVA). Humans being emotional creatures are driven by emotions as well as rational thought. We believe that emotions play a major role in influencing the buying behavior of users and hence propose a video advertising strategy which takes into account the emotional impact of the videos as well as advertisements. Given a video and a set of advertisements, we identify candidate advertisement insertion points (step 1) and also identify the suitable advertisements (step 2) according to theories from marketing and consumer psychology. We formulate this two part problem as a single optimization function in a non-linear 0-1 integer programming framework and provide a genetic algorithm based solution. We evaluate CAVVA using a subjective user-study and eye-tracking experiment. Through these experiments, we demonstrate that CAVVA achieves a good balance between the following seemingly conflicting goals of (a) minimizing the user disturbance because of advertisement insertion while (b) enhancing the user engagement with the advertising content. We compare our method with existing advertising strategies and show that CAVVA can enhance the user's experience and also help increase the monetization potential of the advertising content.

Differential Action of Glycoprotein Hormones: Significance in Cancer Progression

Growth of multicellular organisms depends on maintenance of proper balance between proliferation and differentiation. Any disturbance in this balance in animal cells can lead to cancer. Experimental evidence is provided to conclude with special reference to the action of follicle-stimulating hormone (FSH) on Sertoli cells, and luteinizing hormone (LH) on Leydig cells that these hormones exert a differential action on their target cells, i.e., stimulate proliferation when the cells are in an undifferentiated state which is the situation with cancer cells and promote only functional parameters when the cell are fully differentiated. Hormones and growth factors play a key role in cell proliferation, differentiation, and apoptosis. There is a growing body of evidence that various tumors express some hormones at high levels as well as their cognate receptors indicating the possibility of a role in progression of cancer. Hormones such as LH, FSH, and thyroid-stimulating hormone have been reported to stimulate cell proliferation and act as tumor promoter in a variety of hormone-dependent cancers including gonads, lung, thyroid, uterus, breast, prostate, etc. This review summarizes evidence to conclude that these hormones are produced by some cancer tissues to promote their own growth. Also an attempt is made to explain the significance of the differential action of hormones in progression of cancer with special reference to prostate cancer.

Distribution, Demography, and Conservation of Lion-tailed Macaques (Macaca silenus) in the Anamalai Hills Landscape, Western Ghats, India

The status of the endemic and endangered lion-tailed macaque (Macaca silenus) has not been properly assessed in several regions of the Western Ghats of southern India. We conducted a study in Parambikulam Forest Reserve in the state of Kerala to determine the distribution, demography, and status of lion-tailed macaques. We laid 5km(2) grid cells on the map of the study area (644km(2)) and made four replicated walks in each grid cell using GPS. We gathered data on lion-tailed macaque group locations, demography, and site covariates including trail length, duration of walk, proportion of evergreen forest, height of tallest trees, and human disturbance index. We also performed occupancy modeling using PRESENCE ver. 3.0. We estimated a minimum of 17 groups of macaques in these hills. Low detection and occupancy probabilities indicated a low density of lion-tailed macaques in the study area. Height of the tallest trees correlated positively whereas human disturbance and proportion of evergreen forest correlated negatively with occupancy in grid cells. We also used data from earlier studies carried out in the surrounding Anamalai Tiger Reserve and Nelliyampathy Hills to discuss the conservation status in the large Anamalai Hills Landscape. This landscape harbors an estimated population of 1108 individuals of lion-tailed macaques, which is about one third of the entire estimated wild population of this species. A conservation plan for this landscape could be used as a model for conservation in other regions of the Western Ghats.

A Grid Simulator to Evaluate Control Performance of Grid-Connected Inverters

Grid simulators are used to test the control performance of grid-connected inverters under a wide range of grid disturbance conditions. In the present work, a three phase back-to-back connected inverter sharing a common dc bus has been programmed as a grid simulator. Three phase balanced disturbance voltages applied to three-phase balanced loads has been considered in the present work. The developed grid simulator can generate three phase balanced voltage sags, voltage swells, frequency deviations and phase jumps. The grid simulator uses a novel disturbance generation algorithm. The algorithm allows the user to reference the disturbance to any of the three phases at any desired phase angle. Further, the exit of the disturbance condition can be referenced to the desired phase angle of any phase by adjusting the duration of the disturbance. The grid simulator hardware has been tested with different loads – a linear purely resistive load, a non-linear diode-bridge load and a grid-connected inverter load.

Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data

Surface energy processes has an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. The research was undertaken to analyze the potential of Landsat and MODIS data in retrieving biophysical parameters in estimating land surface temperature & heat fluxes diurnally in summer and winter seasons of years 2000 and 2010 and understanding its effect on anthropogenic heat disturbance over Delhi and surrounding region. Results show that during years 2000-2010, settlement and industrial area increased from 5.66 to 11.74% and 4.92 to 11.87% respectively which in turn has direct effect on land surface temperature (LST) and heat fluxes including anthropogenic heat flux. Based on the energy balance model for land surface, a method to estimate the increase in anthropogenic heat flux (Has) has been proposed. The settlement and industrial areas has higher amounts of energy consumed and has high values of Has in all seasons. The comparison of satellite derived LST with that of field measured values show that Landsat estimated values are in close agreement within error of 2 degrees C than MODIS with an error of 3 degrees C. It was observed that, during 2000 and 2010, the average change in surface temperature using Landsat over settlement & industrial areas of both seasons is 1.4 degrees C & for MODIS data is 3.7 degrees C. The seasonal average change in anthropogenic heat flux (Has) estimated using Landsat & MODIS is up by around 38 W/m(2) and 62 W/m(2) respectively while higher change is observed over settlement and concrete structures. The study reveals that the dynamic range of Has values has increased in the 10 year period due to the strong anthropogenic influence over the area. The study showed that anthropogenic heat flux is an indicator of the strength of urban heat island effect, and can be used to quantify the magnitude of the urban heat island effect. (C) 2013 Elsevier Ltd. All rights reserved.