Ultraviolet bands of mercury bromide

The ultraviolet bands of mercury bromide have been excited in uncondensed discharge and photographed with a quartz Littrow spectrograph. The class II system, lying between\lambda 2900 å to 2700 å, suggested byWieland as due to the triatomic molecule, has been studied in detail and ascribed to the diatomic molecule. The bands in the regionlambda 2900 å to 2770å have been analysed into two systems which may form the two components of a2 II –2 \sigma electronic transition with a2 II interval equal to 969·4 cm–1.Another system most probably due to2 \sigma–2 \sigma has been observed in the region\lambda 2770 to 2720.

Free convection heat transfer to mercury in vertical annuli

Data on free convection heat transfer to water and mercury are collected using a test rig in vertical annuli of three radii ratios, the walls of which are maintained at uniform temperatures. A theoretical analysis of the boundary layer equations has been attempted using local similarity transformation and double boundary layer approach. Correlations derived from the present theoretical analysis are compared with the analysis and the experimental data available in literature for non-metallic fluids and also with the present experimental data on water and mercury. Generalised correlations are set up for expressing the ratio of heat transferred by convection to the heat transferred by pure conduction and Nusselt's number, in terms of Grashof, Rayleigh and Prandtl numbers, based on the theoretical analysis and the present data on mercury and water. The present generalised correlations agree with the reported and present data for non-metallic fluids and liquid metals with an average deviation of 9% and maximum deviation of ± 13.7%.

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.

The generalized anomeric effect in the 1,3-thiazolidines: Evidence forboth sulphur and nitrogen as electron donors. Crystal structures of various N-acylthiazolidines including mercury(II) complexes. Possible relevance to penicillin action

Evidence for the generalized anomeric effect (GAE) in the N-acyl-1,3-thiazolidines, an important structural motif in the penicillins, was sought in the crystal structures of N-(4-nitrobenzoyl)-1,3-thiazolidine and its (2:1) complex with mercuric chloride, N-acetyl-2-phenyl-1,3-thiazolidine, and the (2:1) complex of N-benzoyl-1,3-thiazolidine with mercuric bromide. An inverse relationship was generally observed between the. C-2-N and C-2-S bond lengths of the thiazolidine ring, supporting the existence of the GAE. (Maximal bond length changes were similar to 0.04 angstrom for C-2-N-3, S-1-C-2, and similar to 0.08 angstrom for N-3-C-6.) Comparison with N-acylpyrrolidines and tetrahydrothiophenes indicates that both the nitrogen-to-sulphur and sulphur-to-nitrogen GAE's operate simultaneously in the 1,3-thiazolidines, the former being dominant. (This is analogous to the normal and exo-anomeric effects in pyranoses, and also leads to an interesting application of Baldwin's rules.) The nitrogen-to-sulphur GAE is generally enhanced in the mercury(II) complexes (presumably via coordination at the sulphur); a 'competition' between the GAE and the amide resonance of the N-acyl moiety is apparent. There is evidence for a 'push-pull' charge transfer between the thiazolidine moieties in the mercury(II) complexes, and for a 'back-donation' of charge from the bromine atoms to the thiazolidine moieties in the HgBr2 complex. (The sulphur atom appears to be sp(2) hybridised in the mercury(II) complexes, possibly for stereoelectronic reasons.) These results are apparently relevant to the mode of action of the penicillins. (c) 2006 Elsevier B.V. All rights reserved.

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.

Role of silicon carbide/mercury interface in ignitrons

A new model of ignition in an ignitron, based on the electrical breakdown of the junction between the ignitor (semiconductor) and the mercury (metal) is proposed. A method of evaluating some of the ignition characteristics is also developed. The paper gives a critical summary of the various characteristics of the ignition process. The new model is stated and used to explain all the ignition characteristics. The experiments conducted in support of the various aspects of this model are also given.

Ultra-violet bands of mercury iodide

The ultraviolet bands of mercury iodide have been excited in uncondensed discharge and photographed with a quartz Littrow spectrograph. The bands in the region λ 2658 to 2530 Å have been analysed into two systems which may form the two components of a2Π-2∑ electronic transition with a2Π interval equal to 858·9 cm-1. These systems and the systems in the region λ 3095 to 2647 Å have the same lower state.