Packet classification by multi-level cutting of the classification space

Traditionally, the Internet provides only a “best-effort” service, treating all packets going to the same destination equally. However, providing differentiated services for different users based on their quality requirements is increasingly becoming a demanding issue. For this, routers need to have the capability to distinguish and isolate traffic belonging to different flows. This ability to determine the flow each packet belongs to is called packet classification. Technology vendors are reluctant to support algorithmic solutions for classification due to their nondeterministic performance. Although content addressable memories (CAMs) are favoured by technology vendors due to their deterministic high-lookup rates, they suffer from the problems of high-power consumption and high-silicon cost. This paper provides a new algorithmic-architectural solution for packet classification that mixes CAMs with algorithms based on multilevel cutting of the classification space into smaller spaces. The provided solution utilizes the geometrical distribution of rules in the classification space. It provides the deterministic performance of CAMs, support for dynamic updates, and added flexibility for system designers.

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78-0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1-6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI. © Springer-Verlag London Limited 2008.

Industrial applications of ionic liquids

The potential to use Ionic Liquids (ILs) as novel solvents or fluids for a diverse range of applications has become increasingly rent as researchers in academia and try respond to challenges from atmospheric emissions and disposal of many common solvents by evaluating novel reaction media. The intrinsic non-volatile nature of ILs provides an opportunity to reduce, or even completely eliminate, hazardous and toxic emissions to the atmosphere, thus providing the promise for significant environmental benefits. In synthesis and catalysis, ILs have been used as solvents (or solvents and catalysts), with the greatest current effort on using the ILs as alternatives to VOCs. In contrast, electrochemical studies hove utills'ed the fact that ILs are liquid rather than solids to provide liquid electrolytes without needing to odd an additional solvent. is overview appraiso an appraisal of potential to use ILs in industrial applications, illustrating some areas where practical uses are being developed, and how, throuqh understanding ionic liquids in a conceptuo level, new opportunities ore continuing to evolve.

Nature as historical protagonist: environment and society in pre-industrial England

This article compares chronologies reconstructed from historical records of prices, wages, grain harvests, and population with corresponding chronologies of growing conditions and climatic variations derived from dendrochronology and Greenland ice-cores. It demonstrates that in pre-industrial, and especially late medieval, England, short-term environmental shocks and more enduring shifts in environmental conditions (sometimes acting in concert with biological agencies) exercised a powerful influence upon the balance struck between population and available resources via their effects upon the reproduction, health and life expectancy of humans, crops, and livestock. Prevailing socio-economic conditions and institutions, in turn, shaped society's susceptibility to these environmental shocks and shifts.