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.

Mechanisms of formation of the Arabian Sea mini warm pool in a high-resolution Ocean General Circulation Model

An Ocean General Circulation Model of the Indian Ocean with high horizontal (0.25 degrees x 0.25 degrees) and vertical (40 levels) resolutions is used to study the dynamics and thermodynamics of the Arabian Sea mini warm pool (ASMWP), the warmest region in the northern Indian Ocean during January-April. The model simulates the seasonal cycle of temperature, salinity and currents as well as the winter time temperature inversions in the southeastern Arabian Sea (SEAS) quite realistically with climatological forcing. An experiment which maintained uniform salinity of 35 psu over the entire model domain reproduces the ASMWP similar to the control run with realistic salinity and this is contrary to the existing theories that stratification caused by the intrusion of low-salinity water from the Bay of Bengal into the SEAS is crucial for the formation of ASMWP. The contribution from temperature inversions to the warming of the SEAS is found to be negligible. Experiments with modified atmospheric forcing over the SEAS show that the low latent heat loss over the SEAS compared to the surroundings, resulting from the low winds due to the orographic effect of Western Ghats, plays an important role in setting up the sea surface temperature (SST) distribution over the SEAS during November March. During March-May, the SEAS responds quickly to the air-sea fluxes and the peak SST during April-May is independent of the SST evolution during previous months. The SEAS behaves as a low wind, heat-dominated regime during November-May and, therefore, the formation and maintenance of the ASMWP is not dependent on the near surface stratification.

Investigation of true remnant polarization response in heterostructured artificial biferroics

Epitaxial bilayered thin films composed of ferromagnetic La0.6Sr0.4MnO3 and ferroelectric 0.7Pb (Mg1/3Nb2/3)O3-0.3(PbTiO3) were fabricated on LaAlO3 (100) substrates by pulsed laser ablation. Ferroelectric, ferromagnetic and magneto-dielectric characterizations performed earlier indicated the possible existence of strain-mediated magneto-electric coupling in these biferroic heterostructures. In order to investigate their true remnant polarization characteristics, usable in devices, room-temperature polarization versus electric field, positive-up negative-down (PUND) pulse polarization studies and remnant hysteresis measurements were carried out. The PUND and remnant hysteresis measurements revealed the significant contribution of the non-remnant component in the observed polarization hysteresis response of these heterostructures. (C) 2010 Published by Elsevier Ltd

Comments on "Full-sphere simulations of circulation-dominated solar dynamo: Exploring the parity issue" - Reply

We respond to Dikpati et al.'s criticism of our recent solar dynamo model. A different treatment of the magnetic buoyancy is the most probable reason for their different results.

Blood proteins of the silkworm Bombyx mori L

The qualitative and quantitative aspects of the proteins of the silkworm blood were studied by the technique of agarophoresis. The blood of larvae at the final stage revealed the presence of six different protein zones. Considerable differences in the patterns were observed at different stages of growth. There was an increase in the total nitrogen of the blood up to the 5th instar and then came a sudden decrease in the one-day old pupae. Nitrogen concentration was at its highest in egg 1 stage and the electrophoretic pattern closely corresponded to the final larval pattern. Results indicate to the involvement of silk glands in the synthesis and breakdown of a protein designated as protein 5.

A fast time-domain algorithm for the assessment of tissue blood flow in laser-Doppler flowmetry

In this study, we derive a fast, novel time-domain algorithm to compute the nth-order moment of the power spectral density of the photoelectric current as measured in laser-Doppler flowmetry (LDF). It is well established that in the LDF literature these moments are closely related to fundamental physiological parameters, i.e. concentration of moving erythrocytes and blood flow. In particular, we take advantage of the link between moments in the Fourier domain and fractional derivatives in the temporal domain. Using Parseval's theorem, we establish an exact analytical equivalence between the time-domain expression and the conventional frequency-domain counterpart. Moreover, we demonstrate the appropriateness of estimating the zeroth-, first- and second-order moments using Monte Carlo simulations. Finally, we briefly discuss the feasibility of implementing the proposed algorithm in hardware.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Optimal Blood Glucose Regulation using Single Network Adaptive Critics

Diabetes is a serious disease during which the body's production and use of insulin is impaired, causing glucose concentration level toincrease in the bloodstream. Regulating blood glucose levels as close to normal as possible, leads to a substantial decrease in long term complications of diabetes. In this paper, an intelligent neural network on-line optimal feedback treatment strategy based on nonlinear optimal control theory is presented for the disease using subcutaneous treatment strategy. A simple mathematical model of the nonlinear dynamics of glucose and insulin interaction in the blood system is considered based on the Bergman's minimal model. A glucose infusion term representing the effect of glucose intake resulting from a meal is introduced into the model equations. The efficiency of the proposed controllers is shown taking random parameters and random initial conditions in presence of physical disturbances like food intake. A comparison study with linear quadratic regulator theory brings Out the advantages of the nonlinear control synthesis approach. Simulation results show that unlike linear optimal control, the proposed on-line continuous infusion strategy never leads to severe hypoglycemia problems.

Change in spectrum of Brownian fluctuations of optically trapped red blood cells due to malarial infection

We study the properties of single red blood cells (RBCs) held in an optical-tweezers trap. We observe a change in the spectrum of Brownian fluctuations between RBCs from normal and malaria-infected samples. The change, caused by infection-induced structural changes in the cell, appears as a statistical increase in the mean (by 25%) and standard deviation (by 200%) of the corner frequency measured over similar to 100 cells. The increase is observed even though the ensemble of cells being measured consists mostly of cells that do not actually host the parasite, but are from an infected pool. This bystander effect appears to vindicate other observations that infected cells can affect the biomechanical properties of uninfected cells. The change is also observed to be independent of the stage of infection and its duration, highlighting its potential for disease detection. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3427142].

Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations-uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations. (C) 2009 Elsevier B.V. All rights reserved.

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.