Some aspects of history of Indian mathematics in 18th and early 19th century

This thesis is an attempt to throw light on the works of some Indian Mathematicians who wrote in Arabic or persian In the Introductory Chapter on outline of general history of Mathematics during the eighteenth Bnd nineteenth century has been sketched. During that period there were two streams of Mathematical activity. On one side many eminent scholers, who wrote in Sanskrit, .he l d the field as before without being much influenced by other sources. On the other side there were scholars whose writings were based on Arabic and Persian text but who occasionally drew upon other sources also.

Studies on some aspects of mangrove ecosystem in a tropical estuary"

The situation in the backwaters of Kerala, which reportedly had about 70,000 ha of mangroves, is unique in the sense that there has been a total conversion to other uses such as paddy cultivation, coconut plantation, aquaculture, harbour development and urban development In order to save and restore what is left over national and international organisations are mounting pressure on scientists and policy makers to work out ways and means conserving and managing the mangrove ecosystems. In this context, it has been observed in recent years that mangrove vegetation has remained intact in isolated pockets of undisturbed areas in the Cochin estuarine system and also that there is resurgence of mangroves in areas of accretion and silting. The candidate took up the present study with a view to make an inventory of the existing mangrove locations, the areas of resurgence, species composition, zonation and other ecological parameters to understand their dynamism and to suggest a mangement plan for this important coastal ecosystem

Crystal Structures and Spectral Aspects of Mixed Ligand Transition Metal Chelates Derived from Aldehyde based ONS Donor Thiosemicarbazones

Thiosemicarbazones have emerged as an important class of ligands over a period of time, for a variety of reasons, such as variable donor properties, structural diversity and biological applications. Interesting as the coordination chemistry may be, the driving force for the study of these ligands has undoubtedly been their biological properties and the majority of the 3000 or so publications on thiosemicarbazones since 2000 have alluded to this feature. Thiosemicarbazones with potential donor atoms in their structural skeleton fascinate coordination chemists with their versatile chelating behavior. The thiosemicarbazones of aromatic aldehydes and ketones form stable chelates with transition metal cations by utilizing both their sulfur and azomethine nitrogen as donor atoms. They have been shown to possess a diverse range of biological activities including anticancer, antitumor, antibacterial, antiviral, antimalarial and antifungal properties owing to their ability to diffuse through the semipermeable membrane of the cell lines. The enhanced effect may be attributed to the increased lipophilicity of the metal complexes compared to the ligand alone.

Meteorological aspects of an abnormal cooling event over Iran in April 2009

During the period from 12 to 15 April, 2009 nearly the entire Iran, apart from the southern border, experienced an advective cooling event. While winter freezing concerns are typical, the nature of this freezing event was unusual with respect to its date of occurrence and accompanying synoptic meteorological situation. To analyze the freezing event, the relevant meteorological data at multiple levels of the atmosphere were examined from the NCEP/ NCAR reanalysis dataset. The results showed that a polar vortex was responsible for the freezing event over the country extending southward extraordinarily in such a way that its ridge influenced most parts of Iran. This was recognized as an abnormal extension of a polar vortex in the recent years. The sea-level pressure fields indicated that a ridge of large-scale anticyclone centered over Black Sea extended southward and prevailed over most parts of Iran. This resulted in the formation of a severe cold air advection from high latitudes (Polar region) over Iran. During the study period, moisture pumping was observed from the Arabian Sea and Persian Gulf. The winds at 1000 hPa level blew with a magnitude of 10 m s-1 toward south in the region of convergence (between -2 9 10-6 s-1 and -12 9 10-6 s-1). The vertical profilesof temperature and humidity also indicated that the ICE structural icing occurred at multiple levels of the atmosphere, i.e, from 800 hPa through 400 hPa levels. In addition to the carburetor (or induction), icing occurred between 900 and 700 hPa levels in the selected radiosonde stations during the study period. In addition, the HYSPLIT backward trajectory model outputs were in quite good agreement with the observed synoptic features

Meteorological aspects of mud bank formation along south west coast of India

The study mainly intends to investigate the meteorological aspects associated with the formation of mud banks along southwest coast of India. During the formation of mud bank, the prominent monsoon organized convection is located in the equatorial region and relatively low clouding over Indian mainland. The wind core of the low level jet stream passes through the monsoon organized convection. When the monsoon organized convection is in the equatorial region, the low level wind over the southwest coast of India is parallel to the coastline and toward south. This wind along the coast gives rise to Ekman mass transport away from the coastline and subsequently formation of mud bank, if the high wind stress persists continuously for three or more days. As a result of the increased alongshore wind stress, the coastal upwelling increases. An increase in chlorophyll-a concentration and total chlorophyll can also be seen associated with mudbank formation

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

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.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Quantile based reliability aspects of partial moments

Partial moments are extensively used in literature for modeling and analysis of lifetime data. In this paper, we study properties of partial moments using quantile functions. The quantile based measure determines the underlying distribution uniquely. We then characterize certain lifetime quantile function models. The proposed measure provides alternate definitions for ageing criteria. Finally, we explore the utility of the measure to compare the characteristics of two lifetime distributions

Raman and FTIR studies of the structural aspects of Nasicon-type crystals; AFeTi(PO4)3 [A ¼ Ca, Cd]

Raman and FTIR spectra of CaFeTi(PO4)3 and CdFeTi(PO4)3 are recorded and analyzed. The observed bands are assigned in terms of vibrations of TiO6 octahedra and PO4 tetrahedra. The symmetry of TiO6 octrahedra and PO4 tetrahedra is lowered from their free ion symmetry. The presence of Fe3+ ion disrupts the Ti–O–P–O–Ti chain and leads to the distortion of TiO6 octrahedra and PO4 tetrahedra. The PO4 3 tetrahedra in both crystals are linearly distorted. The covalency bonding factor of PO4 3 polyanion of both the crystals are calculated from the Raman spectra and compared to that of other Nasicon-type systems. The numerical values of covalency bonding factor indicates that there is a reduction in redox energy and cell voltage and is attributed to strong covalency of PO4 3 polyanionin

Socio-Economic Aspects of Sustainable Ecotourism Development: The Case of Kerala

The paper is an attempt to shed light on the socio-economic aspects of the local communities on the development of ecotourism in Kerala. Most of the local communities in the ecotourism destinations are tribes who have been excluded from the mainstream society and are not a part of Kerala’s overall development setting. The paper also tries to situate the community perception on the sustainable livelihood of ecotourism sites of Kerala. Data for the study is obtained from a primary survey by dividing the ecotourism destinations in Kerala into three zones, 230 from south zone, 220 from central zone and 200 from north zone with a total sample size of 650 based on the notion of community based ecotourism initiatives of the state. The result of the study confirms that ecotourism has helped to enhance the livelihood of the marginalized community. With well-knit policies it is possible to tag ecotourism of Kerala as an important tourism destination in the global tourism map

Some Aspects of Domain Specific Conceptual Modeling for Simulation of Logistic Terminal Operations

When simulation modeling is used for performance improvement studies of complex systems such as transport terminals, domain specific conceptual modeling constructs could be used by modelers to create structured models. A two stage procedure which includes identification of the problem characteristics/cluster - ‘knowledge acquisition’ and identification of standard models for the problem cluster – ‘model abstraction’ was found to be effective in creating structured models when applied to certain logistic terminal systems. In this paper we discuss some methods and examples related the knowledge acquisition and model abstraction stages for the development of three different types of model categories of terminal systems