Selective N-monomethylation of aniline using Zn1-x CoxFe2O4( x=0, 0.2, 0.5, 0.8 and 1.0)type systems

A series of ferrites having the general formula Zn1-xCoxFe2O4 (x=0, 0.2, 0.5, 0.8 and 1.0)were prepared by soft chemical route. The materials were characterized by adopting various physico-chemical methods. The reaction of aniline with methanol was studied in a fixed-bed reactor system as a potential source for the production of various methyl anilines. It was observed that systems possessing low ‘ x’ values are highly selective and active for N-monoalkylation of aniline leading to N-methylaniline. Reaction parameters were properly varied to optimize the reaction conditions for obtaining N-methylaniline selectively and in better yield. Among the systems Zn0.8Co0.2Fe2O4 is remarkable due to its very high activity and excellent stability. Under the optimized conditions N-methylaniline selectivity exceeded 98%. Even at a methanol to aniline molar ratio of 2, the yield of N-methylaniline was nearly 50%, whereas its yield exceeded 71% at the molar ratio of 5. ZnFe2O4, though executed better conversion than Zn0.8Co0.2Fe2O4 in the initial period of the run, deactivates quickly as the reaction proceeds. The Lewis acidity of the catalysts is mainly responsible for the good performance. Cation distribution in the spinel lattice influences their acido-basic properties and, hence, these factors have been considered as helpful parameters to evaluate the activity of the systems.

Judicial Review of Academic Decisions

This thesis entitled “Judicial review of academic decisions.Education in India is being increasingly controlled and guided by the courts.This study makes an attempt to assess the involvement of the court in regulating education and its role or interference in the conventional concepts of ‘academic freedom’ and ‘university autonomy.The study mostly concentrates on the jurisdiction under Article 226 of the Constitution and its invocation in academic matters with particular reference to the decisions of the Kerala High Court.The concept of judicial review in the Constituent Assembly, initial approach of the Supreme Court of India towards the doctrine, gradual empowerment of Indian judiciary in this area and the resultant judicial activism.The study proceeds through the analysis of ‘academic freedom’ and ‘university autonomy’ in the 4"‘ chapter. This chapter attempts to probe academic freedom and university autonomy in India,England and United States and autonomy of Indian universities before and after independence.Basic principles and the jurisdictional parameters of judicial review in the area of academic decisions, as pronounced by the Apex Court, can be convincingly and consistently followed by the High Courts, which is possible only if special Academic Benches are constituted.

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.