Anti-SELE monoclonal antibody

This paper illustrates an index-based coastal risk assessment that was performed on a micro-tidal alluvial plain taking into account the relative sea level rise (RSLR) for the evaluation of coastal vulnerability and exposure. This process took into account both the inundation of inshore land and the beach retreat due to storm surge, calculated on the basis of geomorphological data (bathymetry, sedimentology and beach width) and wave climate. The evaluation process was conceived with reference to a low and high hazard, associated with a wave storm with 1 year and 50 years return period. For the latter case, the response to RSLR was calculated taking into account both isostatic response and ice cap melting due to global warming, while the vertical land movement was assessed taking into account the different its rates in the northern and southern coastal area. The exposure and the damage of the coastal assets were evaluated with a simplified conceptual framework, which uses land cover data and a statistical population dataset. The risk assessment procedure was applied to Sele coastal plain, which involves numerous properties and important infrastructures, and is strongly susceptible to marine inundations. A sensitivity analysis of the vulnerability and the risk relative to different hazards and to RSLR was performed. Moreover, the final risk assessment classification was validated with a conceptual framework based on the observed damage ranking related to the tested coastal area. The obtained results showed that the northern high density urban areas were characterized by the highest risk, followed by some central areas with strong localized erosive focus. On the contrary, the southern zones, with wider beaches and almost intact dunes, were characterized by the lowest risk level. The results of this study were used for the development of a coastal protection project which, in fact, provided a different scheme for the coastline northwards and southwards of the Sele river mouth, according to the different risk ranking established. (C) 2014 Elsevier Ltd. All rights reserved.

Contactless medical gait analysis plays an important role in assessing health conditions for ambient assisted living. Prior work on radar-based gait analysis has mainly focused on classification of different gaits or detecting asymmetry. We demonstrate that it is possible to estimate medically relevant gait characteristics based on features from the radar back-scatterings. Given a set of radar features, we predict the maximal knee angle during walking for the left and right leg. We present a new robust genetic algorithm (GA) based on a nonlinear regression method that simultaneously performs feature sele ction, parameter optimization for the support vector machine and outlier rejection by encoding these aspects into the chromosome design. Using genetic operations, the proposed algorithm significantly outperforms competing methods on a real-world data set recorded with a 24 GHz continuous-wave radar.