Synthesis of acridines from diphenylamine-2-carboxaldehydes prepared via the mcfadyen-stevens reaction

A general synthesis of acridines has been developed using diphenylamine-2-carboxaldehydes. Diphenylamine-2-carboxylic acids are converted to their p-toluenesulfonylhydrazides which are decomposed using a modified McFadyen-Stevens reaction to yield an aldehyde derivative which affords the acridine upon treatment with mineral acid.     

Processes of <Emphasis Type="Italic">r</Emphasis><Superscript><Emphasis Type="Italic">t</Emphasis><Emphasis Type="Italic">h</Emphasis></Superscript> largest

For integers n ≥ r, we treat the rth largest of a sample of size n as an \(\mathbb {R}^{\infty }\)-valued stochastic process in r which we denote as M^(r). We show that the sequence regarded in this way satisfies the Markov property. We go on to study the asymptotic behavior of M^(r) as r → ∞, and, borrowing from classical extreme value theory, show that left-tail domain of attraction conditions on the underlying distribution of the sample guarantee weak limits for both the range of M^(r) and M^(r) itself, after norming and centering. In continuous time, an analogous process Y^(r) based on a two-dimensional Poisson process on \(\mathbb {R}_{+}\times \mathbb {R}\) is treated similarly, but we note that the continuous time problems have a distinctive additional feature: there are always infinitely many points below the rth highest point up to time t for any t >?0. This necessitates a different approach to the asymptotics in this case.     

Hidden regular variation under full and strong asymptotic dependence

Data exhibiting heavy-tails in one or more dimensions is often studied using the framework of regular variation. In a multivariate setting this requires identifying specific forms of dependence in the data; this means identifying that the data tends to concentrate along particular directions and does not cover the full space. This is observed in various data sets from finance, insurance, network traffic, social networks, etc. In this paper we discuss the notions of full and strong asymptotic dependence for bivariate data along with the idea of hidden regular variation in these cases. In a risk analysis setting, this leads to improved risk estimation accuracy when regular methods provide a zero estimate of risk. Analyses of both real and simulated data sets illustrate concepts of generation and detection of such models.     

Synthetic control over magnetic moment and exchange bias in all-oxide materials encapsulated within a spherical protein cage

This work focuses on the synthetic control of magnetic properties of mixed oxide magnetic nanoparticles of the general formula Fe(3-x)Co(x)O(4) (x < or = 0.33) in the protein cage ferritin. In this biomimetic approach, variations in the chemical synthesis result in the formation of single-phase Fe(3-x)Co(x)O(4) alloys or intimately mixed binary phase Fe/Co oxides, modifying the chemical structure and magnetic behavior of these particles, as characterized by static and dynamic magnetization measurements and X-ray absorption spectroscopy.