K. Satheesh Kumar

Micropropagation of Plumbago rosea Linn.

Multiple shoot formation from the medicinal plant Plumbago rosea Linn. was induced on callus from stem segments on Murashige & Skoog media containing auxin and cytokinin. 2,4-D (2.5 mg l-1) and kinetin (1.5 mg l-1) added to the media gave best callus production, while BAP (2 mg l-1) plus NAA (1.0 mg l-1) induced shoot formation from that callus. Numerous shoots with roots could be produced by transferring shoots to media containing IBA (1.5 mg l-1). Regenerated plantlets were transferred to pots and 60% survived.

Exploratory social network analysis of affiliation networks of Indian listed companies

Abstract Interlocking directorates play a crucial role in the corporate governance system. In this paper we analyse the structural characteristics of the network of the interlocking directorate of National Stock Exchange (NSE) listed Indian companies using the tools of social network analysis to examine the effects of the underlying network on the performance of companies and directors. A component analysis of the network shows that 78.5% of the companies fall under one giant component with the largest island containing 6 companies. The giant component was further analysed for clusters and centrality measures. The results show that the highly boarded directors who constitute just 2.25% of the director population are associated with 42% of the total listed companies which account for 65.5% of the total market capitalisation. The top central actors in both director as well as company networks have been identified. The calculated values of mean path length and global clustering coefficient provide evidence for the existence of small world structure in the Indian corporate field.

Learning from Bees: An Approach for Influence Maximization on Viral Campaigns.

Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign.

A statistical analysis of soil fertility of Thrissur district, Kerala

A statistical analysis was performed on three thousand and eight hundred soil sample data from Thrissur district. Soil pH, Electrical conductivity, Organic Carbon, Phosphorus, Potassium, Calcium, Magnesium, Sulfur, Zinc, Boron, Iron, Copper and Manganese data were analyzed. Correlation analysis, ANOVA and Principal Component analysis were performed on the data set. Analysis indicate that different soil components are significantly correlated with soil properties.

Empirical mode decomposition and chaos based prediction model for wind speed oscillations

Accurate short-term prediction of wind speed is one of the critical issues faced by wind farm industry so as to plan trading strategies and managing power distribution. In this paper, we demonstrate that empirical mode decomposition (EMD) of the wind speed time series significantly improves prediction accuracy of nonlinear prediction tools. While EMD technique is used to decompose the measured wind speed time series data into its basic components called intrinsic mode functions and residue, nonlinear prediction tool is used to model and forecast each component. Prediction result of each component is summed up to reconstruct the wind speed data into its original form. The Resultant prediction of this hybrid method is compared with the new reference forecast method (NRFM) and local first order method (LFO). The comparison results demonstrate that, prediction accuracy can be remarkably improved by combining EMD and nonlinear model.

Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach

In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed. Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables, with minimalistic preliminary data. The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation. Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories, and it further reduces the need for specialised background.

Modelling the Wind Speed Oscillation Dynamics

We present a detailed nonlinear time series analysis of the daily mean wind speed data measured at COCHIN/WILLINGDON (Latitude: +9.950, Longitude: +76.267 degrees, Elevation: 3 metres) from 2000 to 2010 using tools of non-linear dynamics. The results of the analysis strongly suggest that the underlying dynamics is deterministic, low-dimensional and chaotic indicating the possibility of accurate short term prediction. The chaotic behaviour of wind dynamics explains the presence of periodicities amidst random like fluctuations found in the wind speed data, which forced many researchers to model wind dynamics by stochastic models previously. While most of the chaotic systems reported in the literature are either confined to laboratories or theoretical models, this is another natural system showing chaotic behaviour.