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.

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

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.

A new ELISA plate based microtiter well assay for mycobacterial topoisomerase I for the direct screening of enzyme inhibitory monoclonal antibody supernatants

Antigen specific monoclonal antibodies present in crude hybridoma supernatants are normally screened by ELISA on plates coated with the relevant antigen. Screening for inhibitory monoclonals to enzymes would require the evaluation of purified antibodies or antibody containing supernatants for their inhibition of enzyme activity in a separate assay. However, screening for inhibitory antibodies against DNA transacting enzymes such as topoisomerase I (topo I) cannot be done using hybridoma supernatants due to the presence of nucleases in tissue culture media containing foetal calf serum which degrade the DNA substrates upon addition. We have developed a simple and rapid screening procedure for the identification of clones that secrete inhibitory antibodies against mycobacterial topo I using 96 well ELISA microtiter plates. The principle of the method is the selective capture of monoclonal antibodies from crude hybridoma supernatants by topo I that is tethered to the plate through the use of plate-bound polyclonal anti-topo I antibodies. This step allows the nucleases present in the medium to be washed off leaving the inhibitor bound to the tethered enzyme. The inhibitory activity of the captured antibody is assessed by performing an in situ DNA relaxation assay by the addition of supercoiled DNA substrate directly to the microtiter well followed by the analysis of the reaction products by agarose gel electrophoresis. The validity of this method was confirmed by purification of the identified inhibitory antibody and its evaluation in a DNA relaxation assay. Elimination of all enzyme-inhibitory constituents of the culture medium from the well in which the inhibitory antibody is bound to the tethered enzyme may make this method broadly applicable to enzymes such as DNA gyrases, restriction enzymes and other DNA transaction enzymes. Further, the method is simple and avoids the need of prior antibody purification for testing its inhibitory activity. (C) 2010 Elsevier B.V. All rights reserved.

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.

Passive immunization with an antibody to the beta-subunit of ovine luteinizing hormone as a method of early abortion--a feasibility study in monkeys (Macaca radiata)

Antiserum to the beta-subunit of ovine luteinizing hormone (oLH-beta) raised in monkeys (Macaca radiata) has been tested by a variety of criteria both in vivo and in vitro to establish its ability to neutralize oLH, hLH, and human chorionic gonadotropin (hCG). Passive administration of this antiserum caused inhibition of ovulation and termination of pregnancy in recipient monkeys as indicated by premature vaginal bleeding and a significant reduction in serum progesterone and estrogen levels. The results suggest that antiserum raised in monkeys against oLH-beta can neutralize monkey LH as well as monkey CG.

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.

Chemical approaches to penicillin allergy—V. Nature of reaction between rabbit antigen (CRP-penicillin conjugate) and rabbit antipenicillin antibody

The availability of an electrophoretically homogeneous rabbit penicillin carrier receptor protein (CRP) and rabbit antipenicillin antibody afforded an idealin vitro system to calculate the thermodynamic parameters of the binding of14C benzyl penicillin CRP conjugate (antigen) to the purified rabbit antipenicillin antibody. The thermodynamic parameters of this antigen-antibody reaction has been studied by radio-active assay method by using millipore filter. Equilibrium constant (K) of this reaction has been found to be 2·853×109M−2 and corresponding free energy (ΔG) at 4°C and 37°C has been calculated to be −12·02 and −13·5 kcal/mole, enthalpy (ΔH) and entropy (ΔS) has been found to be 361 kcal/mole and +30 eu/mole respectively. Competitive binding studies of CRP-analogue conjugates with the divalent rabbit antibody has been carried out in the presence of14C-penicilloyl CRP. It was found that 7-deoxy penicillin-CRP complex and 6-amino penicilloyl CRP conjugate binds to the antibody with energies stronger than that with the14C-penicilloyl CRP. All the other analogue conjugates are much weaker in interfering with the binding of the penicilloyl CRP with the antibody. The conjugate of methicillin,o-nitro benzyl penicillin and ticarcillin with CRP do not materially interfere in the process.

Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.

A monoclonal antibody recognizing the C-terminal region of chicken egg white riboflavin carrier protein terminates early pregnancy in mice

In order to identify the functionally relevant epitopes on chicken riboflavin carrier protein, we have raised monoclonal antibodies to the vitamin carrier. One of these, 6B2C12, was found to interact specifically with a synthetic oligopeptide corresponding to the C-terminal 17 amino acid residues of the chicken egg white riboflavin carrier protein, which is missing in part in the egg yolk riboflavin carrier protein. This epitope is conserved through evolution in mammals including humans. Administration of the ascites fluid of 6B2C12 to pregnant mice intraperitoneally, resulted in the termination of pregnancy indicating that this epitope is involved in or closely associated with the transplacental transport of the vitamin from the maternal circulation to the growing fetus.

Artificial database for character recognition research

This paper describes a technique for artificial generation of learning and test sample sets suitable for character recognition research. Sample sets of English (Latin), Malayalam, Kannada and Tamil characters are generated easily through their prototype specifications by the endpoint co-ordinates, nature of segments and connectivity.

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.