Tracking the evolution of riverbed morphology on the basis of uav photogrammetry

Unmanned aerial vehicle (UAV) photogrammetry has recently become a widespread technique to investigate and monitor the evolution of different types of natural processes. Fluvial geomorphology is one of such fields of application where UAV potentially assumes a key role, since it allows for overcoming the intrinsic limits of satellite and airborne-based optical imagery on one side, and in situ traditional investigations on the other. The main purpose of this paper was to obtain extensive products (digital terrain models (DTMs), orthophotos, and 3D models) in a short time, with low costs and at a high resolution, in order to verify the capability of this technique to analyze the active geomorphic processes on a 12 km long stretch of the French–Italian Roia River at both large and small scales. Two surveys, one year apart from each other, were carried out over the study area and a change detection analysis was performed on the basis of the comparison of the obtained DTMs to point out and characterize both the possible morphologic variations related to fluvial dynamics and modifications in vegetation coverage. The results highlight how the understanding of different fluvial processes may be improved by appropriately exploiting UAV-based products, which can thus represent a low-cost and non-invasive tool to crucially support decisionmakers involved in land management practices

Automatic Encoding of ADRS in Italian Spontaneous Reporting System

The aim of this study was to evaluate the performance of the NLP software in encoding adverse reaction description with MedDRA terms.The performance of the encoding software was very good leading to a faster and more accurate encoding of ADRs by the qualified person responsible for ADR report managemen

Reasoning about space and time: moving towards a Theory of Granularities

Publicado em "Computational science and its applications – ICCSA 2014 : proceedings...", Series title : Lecture notes in computer science, vol. 8579Nowadays, the massive amount of spatio-temporal data available exceeds the human capability to absorb them (i.e., to achieve insights). A possible approach to address this issue is through less detailed representations of phenomena so that the data complexity can be decreased making easier for the users to achieve meaningful insights. In this paper, we discuss the state of the art of modeling spatio-temporal phenomena at different levels of detail (LoDs). We found that granularities play an important role to hold spatio-temporal data at different LoDs. A novel granularity framework is proposed, allowing the defini- tion of a granularity over any domain (including spatial and temporal granularities) as well as it allows transposing knowledge from the original domains to granularities (i.e., known relationships and its properties on the domain). Finally, a granularities-based model is proposed, based on the proposed granularity framework, for dealing and relate different LoDs of spatio-temporal data