Interfacial effects on stress distribution in model composites

Raman mechanical spectroscopy was used to examine interfacial effects on the stress distribution in model polydiacetylene fiber/epoxy composites. Epoxy release agents were coated on fiber surfaces to modify the interfacial adhesion properties. The modified fiber surfaces were then characterized by scanning electron microscopy and x-ray photoelectron spectroscopy as well as optical microscopy. No difference in the maximum stress value or stress distribution was observed for the two types of fibers, coated or uncoated, used in composites. This suggests that adhesion properties at the composite interface do not affect tensile stress transfer efficiency nor, therefore, the composite tensile modulus along the fiber axis direction in uniaxial composites. Experimental data were also compared with theoretical calculations assuming perfect bonding between fiber and matrix, and idealized frictional force transfer mechanism at the fiber–matrix interface.     

Future pathways for combinatorial chemistry

Summary Investment in combinatorial chemistry (combichem) in the pharmaceutical industry is being driven by the need for increased efficiency. Results from pioneers in the field have demonstrated where mixture or discrete compound synthesis is useful, and what mixture sizes and compound concentrations are appropriate. To make the techniques of combichem of general utility in drug discovery, a broad range of advances is still required. Conversion of organic chemistry to solid phase conditions is key, as are developments in linkers and resins. Library design methodology requires further development. Combinatorial biosynthesis of focused libraries of natural products holds great promise for capitalising on hardwon natural product leads. Miniaturisation of screens is required to reduce the cost of screening combinatorial libraries. Developments in the processes preceding and following synthesis are required to enable the flow of increased numbers of compounds without new bottlenecks developing. The impact of combinatorial chemistry will be greatly enhanced by synergy with ongoing parallel developments in genetic technologies, screening technologies and bioinformatics.     

Asymptotics in the random assignment problem

Summary We show that, in the usual probabilistic model for the random assignment problem, the optimal cost tends to a limit constant in probability and in expectation. The method involves construction of an infinite limit structure, in terms of which the limit constant is defined. But we cannot improve on the known numerical bounds for the limit.Research supported by NSF Grant MCS90-01710     

Mesh selection for stiff two-point boundary value problems

This paper is concerned with the mesh selection algorithm of COLSYS, a well known collocation code for solving systems of boundary value problems. COLSYS was originally designed to solve non-stiff and mildly stiff problems only. In this paper we show that its performance for solving extremely stiff problems can be considerably improved by modifying its error estimation and mesh selection algorithms. Numerical examples indicate the superiority of the modified algorithm.Dedicated to John Butcher on the occasion of his sixtieth birthday     

Group valued measures with the lyapunoff property

Dopo aver dato una definizione di parte convessa di un gruppo topologico, si stabilisce una condizione affinchè il condominio di una misura sia convesso.     

Fragmentation during the formic acid/formaldehyde (eschweiler-clarke) methylation of polyamines

Eschweiler-Clarke methylations of both acyclic and cyclic polyamines can lead to methylation products of fragments of the original polyamine; thus 1,5,9,13-tetra-azatridecane yields 1,1,3,3-tetramethylpropanediamine exclusively and 1,5,9-triazacyclododecane gives 45% 1,5,9-trimethyl-1,5,9-triazacyclodecane and 45% 2,6,10-trimethyl-2,6,10-triazaundecane.     

A Maximum Entropy Approach for Collaborative Filtering

Collaborative filtering (CF) involves predicting the preferences of a user for a set of items given partial knowledge of the user's preferences for other items, while leveraging a database of profiles for other users. CF has applications e.g. in predicting Web sites a person will visit and in recommending products. Fundamentally, CF is a pattern recognition task, but a formidable one, often involving a huge feature space, a large data set, and many missing features. Even more daunting is the fact that a CF inference engine must be capable of predicting any (user-selected) items, given any available set of partial knowledge on the user's other preferences. In other words, the model must be designed to solve any of a huge (combinatoric) set of possible inference tasks. CF techniques include memory-based, classification-based, and statistical modelling approaches. Among these, modelling approaches scale best with large data sets and are the most adept at handling missing features. The disadvantage of these methods lies in the statistical assumptions (e.g. feature independence), which may be unjustified. To address this shortcoming we propose a new model-based CF method, based on the maximum entropy principle. For the MS Web application, the new method is demonstrated to outperform a number of CF approaches, including naive Bayes and latent variable (cluster) models, support vector machines (SVMs), and the (Pearson) correlation method.