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.

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.

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.

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.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Microbial aspects of acid generation and bioremediation with relevance to indian mining

The role of Acidithiobacillus group of bacteria in acid generation and heavy metal dissolution was studied with relevance to some Indian mines. Microorganisms implicated in acid generation such as Acidithiobacillus Acidithicibacillus thiooxidans and Leptospirillum ferrooxidans were isolated from abandoned mines, waste rocks and tailing dumps. Arsenite oxidizing Thiomonas and Bacillus group of bacteria were isolated and their ability to oxidize As (111) to As (V) established. Mine isolated Sulfate reducing bacteria were used to remove dissolved copper, zinc, iron and arsenic from solutions.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

Automatic Road Extraction using High Resolution Satellite Image Based on Texture Progressive Analysis and Normalized Cut Method

In this paper the approach for automatic road extraction for an urban region using structural, spectral and geometric characteristics of roads has been presented. Roads have been extracted based on two levels: Pre-processing and road extraction methods. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, parking lots, vegetation regions and other open spaces). The road segments are then extracted using Texture Progressive Analysis (TPA) and Normalized cut algorithm. The TPA technique uses binary segmentation based on three levels of texture statistical evaluation to extract road segments where as, Normalizedcut method for road extraction is a graph based method that generates optimal partition of road segments. The performance evaluation (quality measures) for road extraction using TPA and normalized cut method is compared. Thus the experimental result show that normalized cut method is efficient in extracting road segments in urban region from high resolution satellite image.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Data mining approaches to software fault diagnosis

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.