Solvent effect in molecular recognition: Determining binding constants in different solvents following an extraction based protocol

Semi-rigid molecular tweezers 1, 3 and 4 bind picric acid with more than tenfold increment in tetrachloromethane as compared to chloroform.

Impact of Grazing, Fire and Extraction on the Bamboo (Dendrocalamus-Strictus and Bambusa-Arundinacea) Populations of Karnataka

Dendrocalamus strictus and Bambusa arundinacea are monocarpic, gregariously flowering species of bamboo, common in the deciduous forests of the State of Karnataka in India. Their populations have significantly declined, especially since the last flowering. This decline parelleis increasing incidence of grazing, fire and extraction in recent decades. Results of an experiment in which the intensities of grazing and fire were varied, indicate that while grazing significantly depresses the survival of seedlings and the recruitment of new eulms of bamboo clumps, fire appeared to enhance seedling survival, presumably by reducing competition of lass fire-resistant species. New shoots of bamboo are destroyed by insects and a variety of herbivorous mammals. In areas of intense herbivore pressure, a bamboo clump initiates the production of a much larger number of new culrm, but results in many fewer and shorter intact culms. Extraction renders the new shoots more susceptible to herbivore pressure by removal of the protective covering of branches at the base of a bamboo clump. Hence, regular and extensive extraction by the paper mills in conjuction with intense grazing pressure strongly depresses the addition of new culms to bamboo clumps. Regulation of grazing in the forest by domestic livestock along with maintenance of the cover at the base of the clumps by extracting the culms at a higher level should reduce the rate of decline of the bamboo stocks.

Ion extraction from a plasma through a conical orifice

A method of ion extraction from plasmas is reported in which the interference of field lines due to the extraction system in the plasma region is avoided by proper shaping of the extractor electrode and is supported by field plots.

A Fuzzy Approximation Scheme for Sequential Learning in Pattern Recognition

An adaptive learning scheme, based on a fuzzy approximation to the gradient descent method for training a pattern classifier using unlabeled samples, is described. The objective function defined for the fuzzy ISODATA clustering procedure is used as the loss function for computing the gradient. Learning is based on simultaneous fuzzy decisionmaking and estimation. It uses conditional fuzzy measures on unlabeled samples. An exponential membership function is assumed for each class, and the parameters constituting these membership functions are estimated, using the gradient, in a recursive fashion. The induced possibility of occurrence of each class is useful for estimation and is computed using 1) the membership of the new sample in that class and 2) the previously computed average possibility of occurrence of the same class. An inductive entropy measure is defined in terms of induced possibility distribution to measure the extent of learning. The method is illustrated with relevant examples.

Oxidative extraction and ion-exchange of lithium in Li2MoO3: synthesis of Li2âxMoO3 and H2MoO3

It is shown that lithium can be oxidatively extracted from Li2MoO3 at room temperature using Br2 in CHCl3. The delithiated oxides, Li2â��xMoO3 (0 < x â�¤ 1.5) retain the parent ordered rocksalt structure. Complete removal of lithium from Li2MoO3 using Br2 in CH3CN results in a poorly crystalline MoO3 that transforms to the stable structure at 280�°C. Li2MoO3 undergoes topotactic ion-exchange in aqueous H2SO4 to yield a new protonated oxide, H2MoO3.

Kinetics of Extraction of Iron(II1) by Bis(2-ethylhexyl Phosphate in a Laminar Liquid-Liquid Jet Reactor

The kinetics of iron(II1) extraction by bis(Zethylhexy1) phosphate (HDEHP, HA) in kerosene from sulfuric acid solutions has been studied in a liquid-liquid laminar jet reactor. The contact time of the interface in this reacting device is of the same order of magnitude as the surface renewal time in dispersion mixing and much less than that obtained in the relatively quiescent condition of the Lewis cell. Yet the analysis of the data in this study suggested a rate-controlling step involving surface saturation quite in conformity with that obtained in the Lewis cell and not with that in dispersion mixing as reported in the literature. Further, the mechanism suggested a weaker dependence of the rate on hydrogen ion concentration which was reported by other workers.

Enhancement of organic phase controlled extraction of copper by activated carbon

The addition of activated carbon particles (Darco-G, average size 4.3,μm) is shown to enhance the initial rate of extraction of copper in a Lewis cell by a mixture of α- and β-hydroxyoximes, when the rate of extraction is controlled by resistances in the organic phase. It is likely that the copper complex is adsorbed by carbon near the interace and partially released in the bulk. The enhancing effect of carbon vanishes when toluene is used as a diluent instead of heptane, presumably because toluene preferentially adsorbs on its surface.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Minimization of Incompletely Specified Sequential Machines

A simple yet efficient method for the minimization of incompletely specified sequential machines (ISSMs) is proposed. Precise theorems are developed, as a consequence of which several compatibles can be deleted from consideration at the very first stage in the search for a minimal closed cover. Thus, the computational work is significantly reduced. Initial cardinality of the minimal closed cover is further reduced by a consideration of the maximal compatibles (MC's) only; as a result the method converges to the solution faster than the existing procedures. "Rank" of a compatible is defined. It is shown that ordering the compatibles, in accordance with their rank, reduces the number of comparisons to be made in the search for exclusion of compatibles. The new method is simple, systematic, and programmable. It does not involve any heuristics or intuitive procedures. For small- and medium-sized machines, it canle used for hand computation as well. For one of the illustrative examples used in this paper, 30 out of 40 compatibles can be ignored in accordance with the proposed rules and the remaining 10 compatibles only need be considered for obtaining a minimal solution.

Feature extraction for fingerprint classification

This paper presents two algorithms for smoothing and feature extraction for fingerprint classification. Deutsch's(2) Thinning algorithm (rectangular array) is used for thinning the digitized fingerprint (binary version). A simple algorithm is also suggested for classifying the fingerprints. Experimental results obtained using such algorithms are presented.

State Minimization of Incompletely Specified Sequential Machines

A simple procedure for the state minimization of an incompletely specified sequential machine whose number of internal states is not very large is presented. It introduces the concept of a compatibility graph from which the set of maximal compatibles of the machine can be very conveniently derived. Primary and secondary implication trees associated with each maximal compatible are then constructed. The minimal state machine covering the incompletely specified machine is then obtained from these implication trees.

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

Decentralized sequential change detection using physical layer fusion

We study the problem of decentralized sequential change detection with conditionally independent observations. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. Simulations demonstrate that the proposed techniques have lower detection delays when compared with existing schemes. Moreover we demonstrate that the energy-constrained formulation enables better use of the total available energy than a power-constrained formulation.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.