Adapting Existing Spatial Data Sets to New Uses: An Example from Energy Modeling

Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, and economic projections. These data are available at various spatial and temporal scales, which may be different from those needed by the energy modeling community. If the translation from the original format to the format required by the energy researcher is incorrect, then resulting models can produce misleading conclusions. This is of increasing importance, because of the fine resolution data required by models for new alternative energy sources such as wind and distributed generation. This paper addresses the matter by applying spatial statistical techniques which improve the usefulness of spatial data sets (maps) that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) imputing missing data and (3) merging spatial data sets

Uterine transplantation: Legal and regulatory implications in England.

Uterus transplantation (UTx) is fast evolving from an experimental to a clinical procedure, combining solid organ transplantation with assisted reproductive technology. The commencement of the first human uterus transplant trial in the United Kingdom leads us to examine and reflect upon the legal and regulatory aspects closely intertwined with UTx from the process of donation to potential implications on fertility treatment and the birth of the resultant child. As the world's first ephemeral transplant, the possibility of organ restitution requires consideration and is discussed herein

Fast Correlation Attacks based on Turbo Code Techniques

This paper describes new methods for fast correlation attacks on stream ciphers, based on techniques used for constructing and decoding the by now famous turbo codes. The proposed algorithm consists of two parts, a preprocessing part and a decoding part. The preprocessing part identifies several parallel convolutional codes, embedded in the code generated by the LFSR, all sharing the same information bits. The decoding part then finds the correct information bits through an iterative decoding procedure. This provides the initial state of the LFSR