On Implementing Joins, Aggregates and Universal Quantifier in Temporal Databases using SQL Standards

A feasible way of implementing a temporal database is by mapping temporal data model onto a conventional data model followed by a commercial database management system. Even though extensions were proposed to standard SQL for supporting temporal databases, such proposals have not yet come across standardization processes. This paper attempts to implement database operators such as aggregates and universal quantifier for temporal databases, implemented on top of relational database systems, using currently available SQL standards.

Universal behaviour in a ``modulated'' logistic map

We study the period-doubling bifurcations to chaos in a logistic map with a nonlinearly modulated parameter and show that the bifurcation structure is modified significantly. Using the renormalisation method due to Derrida et al. we establish the universal behaviour of the system at the onset of chaos.

Consequence modelling,vulnerability assessment and fuzzy fault tree analysis of hazardous storages in an industrial area

The hazards associated with major accident hazard (MAH) industries are fire, explosion and toxic gas releases. Of these, toxic gas release is the worst as it has the potential to cause extensive fatalities. Qualitative and quantitative hazard analyses are essential for the identitication and quantification of the hazards associated with chemical industries. This research work presents the results of a consequence analysis carried out to assess the damage potential of the hazardous material storages in an industrial area of central Kerala, India. A survey carried out in the major accident hazard (MAH) units in the industrial belt revealed that the major hazardous chemicals stored by the various industrial units are ammonia, chlorine, benzene, naphtha, cyclohexane, cyclohexanone and LPG. The damage potential of the above chemicals is assessed using consequence modelling. Modelling of pool fires for naphtha, cyclohexane, cyclohexanone, benzene and ammonia are carried out using TNO model. Vapor cloud explosion (VCE) modelling of LPG, cyclohexane and benzene are carried out using TNT equivalent model. Boiling liquid expanding vapor explosion (BLEVE) modelling of LPG is also carried out. Dispersion modelling of toxic chemicals like chlorine, ammonia and benzene is carried out using the ALOHA air quality model. Threat zones for different hazardous storages are estimated based on the consequence modelling. The distance covered by the threat zone was found to be maximum for chlorine release from a chlor-alkali industry located in the area. The results of consequence modelling are useful for the estimation of individual risk and societal risk in the above industrial area.Vulnerability assessment is carried out using probit functions for toxic, thermal and pressure loads. Individual and societal risks are also estimated at different locations. Mapping of threat zones due to different incident outcome cases from different MAH industries is done with the help of Are GIS.Fault Tree Analysis (FTA) is an established technique for hazard evaluation. This technique has the advantage of being both qualitative and quantitative, if the probabilities and frequencies of the basic events are known. However it is often difficult to estimate precisely the failure probability of the components due to insufficient data or vague characteristics of the basic event. It has been reported that availability of the failure probability data pertaining to local conditions is surprisingly limited in India. This thesis outlines the generation of failure probability values of the basic events that lead to the release of chlorine from the storage and filling facility of a major chlor-alkali industry located in the area using expert elicitation and proven fuzzy logic. Sensitivity analysis has been done to evaluate the percentage contribution of each basic event that could lead to chlorine release. Two dimensional fuzzy fault tree analysis (TDFFTA) has been proposed for balancing the hesitation factor invo1ved in expert elicitation .

Studies on the universal parameters and onset of chaos in dissipative systems

It has become clear over the last few years that many deterministic dynamical systems described by simple but nonlinear equations with only a few variables can behave in an irregular or random fashion. This phenomenon, commonly called deterministic chaos, is essentially due to the fact that we cannot deal with infinitely precise numbers. In these systems trajectories emerging from nearby initial conditions diverge exponentially as time evolves)and therefore)any small error in the initial measurement spreads with time considerably, leading to unpredictable and chaotic behaviour The thesis work is mainly centered on the asymptotic behaviour of nonlinear and nonintegrable dissipative dynamical systems. It is found that completely deterministic nonlinear differential equations describing such systems can exhibit random or chaotic behaviour. Theoretical studies on this chaotic behaviour can enhance our understanding of various phenomena such as turbulence, nonlinear electronic circuits, erratic behaviour of heart and brain, fundamental molecular reactions involving DNA, meteorological phenomena, fluctuations in the cost of materials and so on. Chaos is studied mainly under two different approaches - the nature of the onset of chaos and the statistical description of the chaotic state.

Studies on seed pathology and seedling diseases of some important indigenous tree species of kerala

In forestry, availability of healthy seeds is an important factor in raising planting stock. Initial seed health and storage conditions are the major factors governing the germinability of seeds. Like seeds of agricultural and horticultural crops, forest tree seeds are also liable to be affected by micro-organisms during storage, which affects the germination, and reduces the viability. Further introduction of seed-borne diseases into newly sown crops/areas on account of using unhealthy seeds is also not ruled out. Availability of healthy stock of seedlings is intrinsic for raising plantations and to meet this requirement elimination of nursery diseases by appropriate chemicals is of prime imortance. As exotic tree species may become susceptible to various native pathogens, it is generally considered better to select indigenous tree species for large scale plantations as they are well adapted to local environment. However, before taking up large scale afforestation progranme involving any indigenous tree species, it is essential to have knowledge about seed disorders and seedling diseases and their management. with a View to select appropriate tree species with fewer seed disorders and seedling disease problems for use in further plantation programme, four indigenous tree species such as Albizia odoratissima (L.f) Benth., Lagerstroemia microcazpa Wt., Pterocazpus marsupiwn Roxb. and Xylia xylocarpa (Roxb.) Taub. were evaluated to meet the above parameters

Automated synthesis of delay-reduced Reed-Muller universal logic module networks

This paper presents a new approach to implement Reed-Muller Universal Logic Module (RM-ULM) networks with reduced delay and hardware for synthesizing logic functions given in Reed-Muller (RM) form. Replication of single control line RM-ULM is used as the only design unit for defining any logic function. An algorithm is proposed that does exhaustive branching to reduce the number of levels and modules required to implement any logic function in RM form. This approach attains a reduction in delay, and power over other implementations of functions having large number of variables.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Characterization and applications of two protease enzymes obtained by culture dependent and independent approaches from mangrove sediments

Soil community genomics or metagenomics is employed in this study to analyze the evolutionary related - ness of mangrove microbial community. The metagenomic DNA was isolated from mangrove sediment and 16SrDNA was amplified using universal primers. The amplicons were ligated into pTZ57R/T cloning vector and transformed onto E. coli JM109 host cells. The recombinant plasmids were isolated from positive clones and the insert was confirmed by its reamplification. The amplicons were subjected to Amplified Ribosomal DNA Restriction Analysis (ARDRA) using three different tetra cutter restriction enzymes namely Sau3A1, Hha1 and HpaII. The 16SrDNA insert were sequenced and their identity was determined. The sequences were submitted to NCBI database and accession numbers obtained. The phylo - genetic tree was constructed based on Neighbor-Joining technique. Clones belonged to two major phyla of the bacterial domain, namely Firmicutes and Proteobacteria, with members of Firmicutes predominating. The microbial diversity of the mangrove sediment was explored in this manner.