Science as a hobby: how and why I came to study the social life of an Indian primitively eusocial wasp

For the most part, my research career has involved prying into the life of the locally abundant primitively eusocial paper wasp, Ropalidia marginata, with the aim of understanding the origin and evolution of social life in insects. My interest in this wasp species began as a hobby, but I was privileged to soon convert my hobby into my profession. Here I describe how this conversion came about, what it meant to pursue my hobby as a full-time activity, describe some examples from my research and end with some reflections about the process of doing modern science, especially in India.

Life-history of Neurospora intermedia in a sugar cane field

The life-history of Neurospora in nature has remained largely unknown. The present study attempts to remedy this. The following conclusions are based on observation of Neurospora on fire-scorched sugar cane in agricultural fields, and reconstruction experiments using a colour mutant to inoculate sugar cane burned in the laboratory. The fungus persists in soil as heat-resistant dormant ascospores. These are activated by a chemical(s) released into soil from the burnt substrate. The chief diffusible activator of ascospores is furfural and the germinating ascospores infect the scorched substrate. An invasive mycelium grows progressively upwards inside the juicy sugar cane and produces copious macroconidia externally through fire-induced openings formed in the plant tissue, or by the mechanical rupturing of the plant epidermal tissue by the mass of mycelium. The loose conidia are dispersed by wind and/or foraged by microfauna. It is suggested that the constant production of macroconidia, and their ready dispersal, serve a physiological role: to drain the substrate of minerals and soluble sugars, thereby creating nutritional conditions which stimulate sexual reproduction by the fungus. Sexual reproduction in the sugar-depleted cellulosic substrate occurs after macroconidiation has ceased totally and is favoured by the humid conditions prevailing during the monsoon rains. Profuse microconidiophores and protoperithecia are produced simultaneously in the pockets below the loosened epidermal tissue. Presumably protoperithecia are fertilized by microconidia which are possibly transmitted by nematodes active in the dead plant tissue. Mature perithecia release ascospores in situ which are passively liberated in the soil by the disintegration of the plant material and are, apparently, distributed by rain or irrigation water.

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.

Artificial intelligence approaches to network management: recent advances and a survey

With increased number of new services and users being added to the communication network, management of such networks becomes crucial to provide assured quality of service. Finding skilled managers is often a problem. To alleviate this problem and also to provide assistance to the available network managers, network management has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academia and industry. In this paper, a review of the management complexities in present day networks and artificial intelligence approaches to network management are presented. Published by Elsevier Science B.V.

Residual strength and fatigue life assessment of composite patch repaired specimens

Design, analysis and technology for the integrity enhancement of damaged or underdesigned structures continues to be an engineering challenge. Bonded composite patch repairs to metallic structures is receiving increased attention in the recent years. It offers various advantages over rivetted doubler, particularly for airframe repairs. This paper presents an experimental investigation of residual strength and fatigue crack-growth life of an edge-cracked aluminium specimen repaired using glass epoxy composite patch. The investigation begins with the evaluation of three different surface treatments from bond strength viewpoint. A simple thumb rule formula is employed to estimate the patch size. Cracked and repaired specimens are tested under static and fatigue loading. The patch appears to restore the original strength of the undamaged specimen and enhance the fatigue crack growth life by an order of magnitude. (C) 1999 Elsevier Science Ltd. All rights reserved.

Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.