An alternative approach to weight function evaluation for an orthotropic crack

The use of the analytically decoupled near-tip displacement solutions as an alternative approach, is presented in this paper for the efficient finite element evaluation of the decoupled weight functions for an orthotropic 2-D crack. This alternative approach has been validated by directly comparing the prior weight function results with a symmetric mesh approach in the crack-tip neighborhood, and indirectly by comparing the calculated stress intensity factors (K_I(II)) values using the computed weight functions with available K_I(II) solutions of the 2-D mixed mode orthotropic cracks. In addition, this approach with analytically decoupled near-tip displacement solutions for calculating weight functions at all locations, can facilitate further extension of weight function evaluations to a more general 2-D anisotropic crack.     

News: Presidential "green chemistry" awards promote industrial pollution prevention

Technology.     

Power Dissipation in Spintronic Devices Out of Thermodynamic Equilibrium

Quantum limits of power dissipation in spintronic computing are estimated. A computing element composed of a single electron in a quantum dot is considered. Dynamics of its spin due to external magnetic field and interaction with adjacent dots are described via the Bloch equations. Spin relaxation due to magnetic noise from various sources is described as coupling to a reservoir. Resulting dissipation of energy is calculated and is shown to be much less than the thermal limit, ∼kT per bit, if the rate of spin relaxation is much slower than the switching rate. Clues on how to engineer an energy efficient spintronic device are provided.     

Simulation and optimization of tool-life in manufacturing centers

This paper presents the development of an optimization and a computer-simulation model to evaluate the process plans of a manufacturing center by analyzing the effect of tool failure on system performance. The GPSS/PC simulation program that is used in this study has been built with variables, functions and matrices so that many experiments could be conducted with the model. Sensitivity analysis is presented and the developed model has proven to be useful in determining optimum sequencing of parts for various operating policies.     

Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 2. Ice Classification and Mapping

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts.     

A novel fuzzy clustering algorithm for the analysis of axillary lymph node tissue sections

Recently Fourier Transform Infrared (FTIR) spectroscopic imaging has been used as a tool to detect the changes in cellular composition that may reflect the onset of a disease. This approach has been investigated as a mean of monitoring the change of the biochemical composition of cells and providing a diagnostic tool for various human cancers and other diseases. The discrimination between different types of tissue based upon spectroscopic data is often achieved using various multivariate clustering techniques. However, the number of clusters is a common unknown feature for the clustering methods, such as hierarchical cluster analysis, k-means and fuzzy c-means. In this study, we apply a FCM based clustering algorithm to obtain the best number of clusters as given by the minimum validity index value. This often results in an excessive number of clusters being created due to the complexity of this biochemical system. A novel method to automatically merge clusters was developed to try to address this problem. Three lymph node tissue sections were examined to evaluate our new method. These results showed that this approach can merge the clusters which have similar biochemistry. Consequently, the overall algorithm automatically identifies clusters that accurately match the main tissue types that are independently determined by the clinician.