Radiation exposure and Mission Strategies for Interplanetary Manned Missions (REMSIM)

Cosmic radiation is an important problem for human interplanetary missions. The “Radiation Exposure and Mission Strategies for Interplanetary Manned Missions–REMSIM” study is summarised here. They are related to current strategies and countermeasures to ensure the protection of astronauts from radiation during interplanetary missions, with specific reference to: radiation environment and its variability; radiation effects on the crew; transfer trajectories and associated fluences; vehicle and surface habitat concepts; passive and active shielding concepts; space weather monitoring and warning systems.     

On predicting and improving the quality of Volunteer Geographic Information projects

Initiatives that rely upon the contributions of volunteers to reach a specific goal are growing more and more with the success of Web 2.0–interactive applications. Also scientific projects are testing and exploiting volunteers' collaboration, but the quality of information obtained with this approach is often puzzling. This paper offers a rich overview of many scientific projects where geographic contributions are committed to volunteers, to the aim of defining strategies to improve information quality. By describing real examples of Volunteer Geographic Information (VGI), the contribution establishes a categorization based on the characteristics of the information, tasks, and scopes of the projects. After a discussion on the relationships of categories and VGI quality, the paper analyses techniques to improve the quality of volunteered information according to the moment of its assessment (i.e., ex ante, ex post, or both with respect to information creation). The paper outlines the main limitations of the different approaches and indicates some guidelines for future developments.     

Melted snow volume control in the snowmelt runoff model using a snow water equivalent statistically based model

Snowmelt is an important component of the river discharge in mountain environments. In the past 40 years, the snowmelt dynamics has been mostly evaluated using degree‐day‐based models like the snowmelt runoff model (SRM). This model has no control on the volume of the melting snow, even if SRM includes as data input the snow‐covered area. This lack explains why the application of SRM may lead to inaccurate snowmelt volume estimations, even if the discharge volumes are accurately reproduced. Here we introduce in SRM the control on the melted snow volume and consider it in the determination of SRM parameters. The total snow volume, accumulated at the end of winter season, is evaluated by a snow water equivalent statistically based model, SWE‐SEM, and used as an estimate of the melting snow during the summer season. The benefit derived from the introduction of the control on the melting snow volume was investigated in the Mallero basin (northern Italy) for the 2003 and 2004 snow melting seasons. The analysis compares the model's results adopting different parameter sets, both considering and ignoring the control on the melting snow volume. Copyright © 2011 John Wiley & Sons, Ltd.     

Slope Instability Zonation: a Comparison Between Certainty Factor and Fuzzy Dempster–Shafer Approaches

This paper presents a comparison between two methodologies for the evaluation of slope instability and the production of instability maps, using a probabilistic approach and a hybrid possibilistic and credibilistic approach. The first is the Certainty Factor method, and the second is based on Fuzzy Logic integrated with the Dempster–Shafer theory. These methodologies are applied to the 1 : 50,000 scale Fabriano (Marche, Italy) geological map sheet. The results are represented as histograms where the accuracy of the prediction is shown, and the comparison of the results of the methods is discussed.     

Prediction of Displacements in Unstable Areas Using a Neural Model

In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers.