Survey and brain storming studies about machines, constructions, human and environmental risk consideration in the careers of the Universidad Politécnica of Madrid

The Universidad Politécnica of Madrid (UPM) includes schools and faculties that were for engineering degrees, architecture and computer science, that are now in a quick EEES Bolonia Plan metamorphosis getting into degrees, masters and doctorate structures. They are focused towards action in machines, constructions, enterprises, that are subjected to machines, human and environment created risks. These are present in actions such as use loads, wind, snow, waves, flows, earthquakes, forces and effects in machines, vehicles behavior, chemical effects, and other environmental factors including effects of crops, cattle and beasts, forests, and varied essential economic and social disturbances. Emphasis is for authors in this session more about risks of natural origin, such as for hail, winds, snow or waves that are not exactly known a priori, but that are often considered with statistical expected distributions giving extreme values for convenient return periods. These distributions are known from measures in time, statistic of extremes and models about hazard scenarios and about responses of man made constructions or devices. In each engineering field theories were built about hazards scenarios and how to cover for important risks. Engineers must get that the systems they handle, such as vehicles, machines, firms or agro lands or forests, obtain production with enough safety for persons and with decent economic results in spite of risks. For that risks must be considered in planning, in realization and in operation, and safety margins must be taken but at a reasonable cost. That is a small level of risks will often remain, due to limitations in costs or because of due to strange hazards, and maybe they will be covered by insurance in cases such as in transport with cars, ships or aircrafts, in agro for hail, or for fire in houses or in forests. These and other decisions about quality, security for men or about business financial risks are sometimes considered with Decision Theories models, using often tools from Statistics or operational Research. The authors have done and are following field surveys about risk consideration in the careers in UPM, making deep analysis of curricula taking into account the new structures of degrees in the EEES Bolonia Plan, and they have considered the risk structures offered by diverse schools of Decision theories. That gives an aspect of the needs and uses, and recommendations about improving in the teaching about risk, that may include special subjects especially oriented for each career, school or faculty, so as to be recommended to be included into the curricula, including an elaboration and presentation format using a multi-criteria decision model.

Sum Insured Determination for Cereal, Citrus and Vineyards in the Spanish Agricultural Insurance System.

In general, insurance is a form of risk management used to hedge against a contingent loss. The conventional definition is the equitable transfer of a risk of loss from one entity to another in exchange for a premium or a guaranteed and quantifiable small loss to prevent a large and possibly devastating loss being agricultural insurance a special line of property insurance. Agriculture insurance, as actually are designed in the Spanish scenario, were established in 1978. At the macroeconomic insurance studies scale, it is necessary to know a basic element for the insurance actuarial components: sum insured. When a new risk assessment has to be evaluated in the insurance framework, it is essential to determinate venture capital in the total Spanish agriculture. In this study, three different crops (cereal, citrus and vineyards) cases are showed to determinate sum insured as they are representative of the cases found in the Spanish agriculture. Crop sum insured is calculated by the product of crop surface, unit surface production and crop price insured. In the cereal case, winter as spring cereal sowing, represents the highest Spanish crop surface, above to 6 millions of hectares (ha). Meanwhile, the four citrus species (oranges, mandarins, lemons and grapefruits) occupied an extension just over 275.000 ha. On the other hand, vineyard target to wine process shows almost one million of ha in Spain.

Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.

Comparison of fractal dimensions based on segmented NDVI fields obtained from different remote sensors.

Satellite image data have become an important source of information for monitoring vegetation and mapping land cover at several scales. Beside this, the distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Various vegetation indices have been developed for qualitative and quantitative assessment of vegetation using remote spectral measurements. In particular, sensors with spectral bands in the red (RED) and near-infrared (NIR) lend themselves well to vegetation monitoring and based on them [(NIR - RED) / (NIR + RED)] Normalized Difference Vegetation Index (NDVI) has been widespread used. Given that the characteristics of spectral bands in RED and NIR vary distinctly from sensor to sensor, NDVI values based on data from different instruments will not be directly comparable. The spatial resolution also varies significantly between sensors, as well as within a given scene in the case of wide-angle and oblique sensors. As a result, NDVI values will vary according to combinations of the heterogeneity and scale of terrestrial surfaces and pixel footprint sizes. Therefore, the question arises as to the impact of differences in spectral and spatial resolutions on vegetation indices like the NDVI. The aim of this study is to establish a comparison between two different sensors in their NDVI values at different spatial resolutions.