Effects of plant density and the number of emitters on plant growth and nitrate concentration in spinach cultivated in substrate

The effects of plant density and the number of emitters per Styrofoam box on plant growth and nitrate (NO3-) concentration were evaluated in spinach (Spinacia oleracea L. cv. Tapir). Spinach seedlings were transplanted at 45 days after emergence into Styrofoam boxes filled with the substrate and were grown during winter in an unheated greenhouse with no supplemental lighting. The experiment was carried out with four treatments, including two plant densities (160 and 280 plants/m2) and two number of emitters per Styrofoam box (4 and 8 emitters). Each planting box was irrigated daily and fertigated with a complete nutrient solution. Shoot dry weight was not affected by plant density. However, yield increased with plant density and emitter number. Leaf-blade NO3- concentration was not affected by the interaction between plant density and number of emitters, but petioles NO3- concentration was greater in treatment with 160 plants/m2 and 8 emitters. Although leaf-blade NO3- concentration was not affected by plant density, it decreased with the number of emitters. On the other hand, petiole NO3- concentration was not affected by plant density or number of emitters. Leaf-blade NO3- concentration ranged from 3.2 to 4.1 mg/g fresh weight, occurring the highest value in the treatment with 280 plants/m2 and 4 emitters. Petiole NO3- concentration ranged from 3.5 to 5.3 mg/g fresh weight, values that were higher than allowed by EU regulation.

Above ground biomass functions with vegetation indices for multiple use systems of two evergreen oaks

Remote sensing is a promising approach for above ground biomass estimation, as forest parameters can be obtained indirectly. The analysis in space and time is quite straight forward due to the flexibility of the method to determine forest crown parameters with remote sensing. It can be used to evaluate and monitoring for example the development of a forest area in time and the impact of disturbances, such as silvicultural practices or deforestation. The vegetation indices, which condense data in a quantitative numeric manner, have been used to estimate several forest parameters, such as the volume, basal area and above ground biomass. The objective of this study was the development of allometric functions to estimate above ground biomass using vegetation indices as independent variables. The vegetation indices used were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Simple Ratio (SR) and Soil-Adjusted Vegetation Index (SAVI). QuickBird satellite data, with 0.70 m of spatial resolution, was orthorectified, geometrically and atmospheric corrected, and the digital number were converted to top of atmosphere reflectance (ToA). Forest inventory data and published allometric functions at tree level were used to estimate above ground biomass per plot. Linear functions were fitted for the monospecies and multispecies stands of two evergreen oaks (Quercus suber and Quercus rotundifolia) in multiple use systems, montados. The allometric above ground biomass functions were fitted considering the mean and the median of each vegetation index per grid as independent variable. Species composition as a dummy variable was also considered as an independent variable. The linear functions with better performance are those with mean NDVI or mean SR as independent variable. Noteworthy is that the two better functions for monospecies cork oak stands have median NDVI or median SR as independent variable. When species composition dummy variables are included in the function (with stepwise regression) the best model has median NDVI as independent variable. The vegetation indices with the worse model performance were EVI and SAVI.

PRACTICAL RESOLUTION OF DIFFERENTIAL SYSTEMS

3. PRACTICAL RESOLUTION OF DIFFERENTIAL SYSTEMS by Marilia Pires, University of Évora, Portugal This practice presents the main features of a free software to solve mathematical equations derived from concrete problems: i.- Presentation of Scilab (or python) ii.- Basics (number, characters, function) iii.- Graphics iv.- Linear and nonlinear systems v.- Differential equations

Analysis of Dyscalculia Evidences through Artificial Intelligence Systems

Dyscalculia is usually perceived of as a specific learning difficulty for mathematics or, more appropriately, arithmetic. Because definitions and diagnoses of dyscalculia are in their infancy and sometimes are contradictory. However, mathematical learning difficulties are certainly not in their infancy and are very prevalent and often devastating in their impact. Co-occurrence of learning disorders appears to be the rule rather than the exception. Co-occurrence is generally assumed to be a consequence of risk factors that are shared between disorders, for example, working memory. However, it should not be assumed that all dyslexics have problems with mathematics, although the percentage may be very high, or that all dyscalculics have problems with reading and writing. Because mathematics is very developmental, any insecurity or uncertainty in early topics will impact on later topics, hence to need to take intervention back to basics. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work will focus on the development of a Decision Support System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, grounded on a Case-based approach to computing, that allows for the handling of incomplete, unknown, or even self-contradictory information.