Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R

Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underlying matching algorithm is written in C++, makes extensive use of system BLAS and scales efficiently with dataset size. The genetic algorithm which finds optimal balance is parallelized and can make use of multiple CPUs or a cluster of computers. A large number of options are provided which control exactly how the matching is conducted and how balance is evaluated.

Applications of heat pipes to cool PWBS and hybrid microcircuits

Some of the advanced thermal management techniques used to reduce operating junction temperature under extreme environmental temperature conditions are discussed. Heat pipes in actual electronic packaging applications, and those under development, are discussed. Performance characteristics of heat pipes are given, and examples are described of how thermal problems in electronic packaging are solved through the use of heat pipes

Using HCMM Thermal Data to Improve Classification of MSS Data

Spectral overlap between urban and rural land use/land cover categories can lead to unacceptable map accuracy levels in the classification of LANDSAT multispectral scanner (MSS) data. The four MSS bands used alone are not always adequate to distinguish among various land uses and cover types having similar spectral responses. The use of thermal data from the Heat Capacity Mapping Mission (HCMM) satellite as a means of improving MSS land cover classification accuracies for urban versus rural categories was investigated. The approaches used to integrate the HCMM data are described

Genetic Optimization Using Derivatives: The rgenoud Package for R

genoud is an R function that combines evolutionary algorithm methods with a derivative-based (quasi-Newton) method to solve difficult optimization problems. genoud may also be used for optimization problems for which derivatives do not exist. genoud solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When the function to be optimized (for example, a log-likelihood) is nonlinear in the model's parameters, the function will generally not be globally concave and may have irregularities such as saddlepoints or discontinuities. Optimization methods that rely on derivatives of the objective function may be unable to find any optimum at all. Multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. On the other hand, algorithms that do not use derivative information (such as pure genetic algorithms) are for many problems needlessly poor at local hill climbing. Most statistical problems are regular in a neighborhood of the solution. Therefore, for some portion of the search space, derivative information is useful. The function supports parallel processing on multiple CPUs on a single machine or a cluster of computers.