Self-assembled Transition Metal Coordination Frameworks of Carbohydrazone and Thiocarbohydrazone Ligands: Structural, Spectral, Magnetic and Anticancer Properties

Chemistry occupies a unique middle position in the scientific arena, between physics and mathematics on the one side and biology, ecology, sociology and economics on the other [1]. Chemistry is the science of matter and of its transformations, and life is its highest expression [2]. According to reductionist thinking biology is reducible into chemistry, chemistry into physics, and ultimately physics into mathematics. Reductionism implies the ease of understanding one level in terms of another.The work presented this thesis comprises synthesis and characterization of suitably substituted thiocarbohydrazone and carbohydrazone ligand building blocks, self-assembled metallosupramolecular square grid complexes as well as some di/multinuclear complexes. The primary aim was the deliberate syntheses of some novel transition metal framework complexes, mainly metallosupramolecular coordination square grids by self-assembly and their physico-chemical characterization. The work presented, however, also include synthesis and characterization of four mononuclear Ni(II) complexes of two thiosemicarbazones, which we carried out as a preliminary and supporting study. Based on the present work we would like to conclude that the carbohydrazones, thiocarbohydrazones and their coordination framework complexes of transition metals are promising systems for wide application in science and technology varied from physics to biotechnology. Novel classes of materials and biologically important potential compounds open up further scope of researches and we hopefully welcome any sort of related research to make this work more valuable.

Design Of Metal Architectures: Synthesis And Study Of Their Photophysical And Biomolecular Recognition Properties

Design and study of molecular receptors capable of mimicking natural processes has found applications in basic research as well as in the development of potentially useful technologies. Of the various receptors reported, the cyclophanes are known to encapsulate guest molecules in their cavity utilizing various non–covalent interactions resulting in significant changes in their optical properties. This unique property of the cyclophanes has been widely exploited for the development of selective and sensitive probes for a variety of guest molecules including complex biomolecules. Further, the incorporation of metal centres into these systems added new possibilities for designing receptors such as the metallocyclophanes and transition metal complexes, which can target a large variety of Lewis basic functional groups that act as selective synthetic receptors. The ligands that form complexes with the metal ions, and are capable of further binding to Lewis-basic substrates through open coordination sites present in various biomolecules are particularly important as biomolecular receptors. In this context, we synthesized a few anthracene and acridine based metal complexes and novel metallocyclophanes and have investigated their photophysical and biomolecular recognition properties.

Econometrics as an Academic Discipline in Higher Education: An Exploratory Study of the Need, Relevance and Significance

Econometrics is a young science. It developed during the twentieth century in the mid-1930’s, primarily after the World War II. Econometrics is the unification of statistical analysis, economic theory and mathematics. The history of econometrics can be traced to the use of statistical and mathematics analysis in economics. The most prominent contributions during the initial period can be seen in the works of Tinbergen and Frisch, and also that of Haavelmo in the 1940's through the mid 1950's. Right from the rudimentary application of statistics to economic data, like the use of laws of error through the development of least squares by Legendre, Laplace, and Gauss, the discipline of econometrics has later on witnessed the applied works done by Edge worth and Mitchell. A very significant mile stone in its evolution has been the work of Tinbergen, Frisch, and Haavelmo in their development of multiple regression and correlation analysis. They used these techniques to test different economic theories using time series data. In spite of the fact that some predictions based on econometric methodology might have gone wrong, the sound scientific nature of the discipline cannot be ignored by anyone. This is reflected in the economic rationale underlying any econometric model, statistical and mathematical reasoning for the various inferences drawn etc. The relevance of econometrics as an academic discipline assumes high significance in the above context. Because of the inter-disciplinary nature of econometrics (which is a unification of Economics, Statistics and Mathematics), the subject can be taught at all these broad areas, not-withstanding the fact that most often Economics students alone are offered this subject as those of other disciplines might not have adequate Economics background to understand the subject. In fact, even for technical courses (like Engineering), business management courses (like MBA), professional accountancy courses etc. econometrics is quite relevant. More relevant is the case of research students of various social sciences, commerce and management. In the ongoing scenario of globalization and economic deregulation, there is the need to give added thrust to the academic discipline of econometrics in higher education, across various social science streams, commerce, management, professional accountancy etc. Accordingly, the analytical ability of the students can be sharpened and their ability to look into the socio-economic problems with a mathematical approach can be improved, and enabling them to derive scientific inferences and solutions to such problems. The utmost significance of hands-own practical training on the use of computer-based econometric packages, especially at the post-graduate and research levels need to be pointed out here. Mere learning of the econometric methodology or the underlying theories alone would not have much practical utility for the students in their future career, whether in academics, industry, or in practice This paper seeks to trace the historical development of econometrics and study the current status of econometrics as an academic discipline in higher education. Besides, the paper looks into the problems faced by the teachers in teaching econometrics, and those of students in learning the subject including effective application of the methodology in real life situations. Accordingly, the paper offers some meaningful suggestions for effective teaching of econometrics in higher education

Product autoregressive models for non-negative variables

When variables in time series context are non-negative, such as for volatility, survival time or wave heights, a multiplicative autoregressive model of the type Xt = Xα t−1Vt , 0 ≤ α < 1, t = 1, 2, . . . may give the preferred dependent structure. In this paper, we study the properties of such models and propose methods for parameter estimation. Explicit solutions of the model are obtained in the case of gamma marginal distribution

Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models

The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis