El pensamiento multiplicativo en los primeros niveles: una investigación en curso

Este artículo muestra los resultados parciales de un estudio empírico sobre el desarrollo de pensamiento multiplicativo en los primeros niveles escolares. Concretamente, se han realizado entrevistas a niños de tercer curso de segundo ciclo de Educación Infantil (cinco años) en las que se han planteado, mediante situaciones manipulables, varios problemas de división que no podían ser resueltos mediante reparto, así como algunas preguntas sobre pensamiento relacional (de tipo proporcional)

Interactive effects of increased CO2 levels and iron availability on the marine pelagic food web during a mesocosm experiment

IMBER-Congreso sobre el futuro de los océanos en el contexto de los ciclos biogeoquímicosA mesocosm experiment was carried out in the Raunefjord, off Bergen, Norway (June 2012) to investigate the interactive effects of increased CO2 and iron availability on the pelagic food web. Twelve mesocosms (11m3) covered by PAR and UVR transparent lids were used. The seawater carbonate system in the mesocosms was manipulated to achieve two different CO2 levels, corresponding to the present (390 ppmV, LC) and to levels predicted for year 2100 (900 ppmV, HC). Mesocosms were fertilised at the onset of the experiment, by addition of 10 μM nitrate and 0.3 μM phosphate to induce a bloom of the coccolithophore Emiliania huxleyi. On day 7 of the experiment, half of the mesocosms were amended with 70 nM (final concentration) of the siderophore desferoxamine B (DFB). The relevance of these results within a global biogeochemical perspective will be discussed.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

SOLID STATE MORPHOLOGY AND SIZE TUNING OF NANOSTRUCTURED PLATINUM USING MACROMOLECULAR COMPLEXES

Indexación: Web of Science; Scielo.The macromolecular complexes Chitosan●(PtCl2)n and PSP-co-4-PVP●(PtCl2)n, were prepared from the respective polymer and PtCl2 in metal:polymer molar ratios 1:1 and 1:5. Pyrolisis of the macromolecular complexes Chitosan●(PtCl2)n and PSP-co-4-PVP●(PtCl2)n at 800 °C under air affords cubic nanostructured Pt in the pure phase. The morphology of the pyrolityc products depends on the molar metal:polymer ratio; i.e. a "cotton" 3D shape for the 1:1 ratio and a 'foamy" 3D shape for the 1:5 ratio. On the other hand, the particle size depends on the polymer nature, obtaining Pt nanoparticles as small as 6 nm for the chitosan precursors in both molar ratio.http://ref.scielo.org/8p5h2

Ecological and epidemiological aspects of flaviviruses and their vectors in urban and rural areas of Paraguay

Objective of this study: Contribute to the knowledge of ecological and epidemiological aspects of flavivirus and its vectors in urban and rural areas of Paraguay.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI

Ecological and epidemiological aspects of flavivirus and its vectors in Paraguay

Presentación del proyecto en el Mosticaw workshop.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI

Learning sequences of rules using classifier systems with tags

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.The objective of this paper was to obtain an encoding structure that would allow the genetic evolution of rules in such a manner that the number of rules and relationship in a classifier system (CS) would be learnt in the evolution process. For this purpose, an area that allows the definition of rule groups has been entered into the condition and message part of the encoded rules. This area is called internal tag. This term was coined because the system has some similarities with natural processes that take place in certain animal species, where the existence of tags allows them to communicate and recognize each other. Such CS is called a tag classifier system (TCS). The TCS has been tested in the game of draughts and compared with the classical CS. The results show an improving of the CS performance

Applying classifier systems to learn the reactions in mobile robots

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier systems (CSs) have proven their ability of continuous learning, however, they have some problems in reactive systems. A modified CS, namely a reactive classifier system (RCS), is proposed to overcome those problems. Two special mechanisms are included in the RCS: the non-existence of internal cycles inside the CS (no internal cycles) and the fusion of environmental message with the messages posted to the message list in the previous instant (generation list through fusion). These mechanisms allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminate between rules and, second, the discovery of new rules to obtain a successful operation in dynamic environments. DiVerent experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations.Publicad

RTCS: a reactive with tags classifier system

In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.Publicad

Knowledge acquisition including tags in a classifier system

Congress on Evolutionary Computation. Washington, DC, 6-9 July 1999.One of the major problems related to classifier systems is the loss of rules. This loss is caused by the genetic algorithm being applied on the entire population of rules jointly. Obviously, the genetic operators discriminate rules by the strength value, such that evolution favours the generation of the stronger rules. When the learning system works in an environment in which it is possible to generate a complete training set, the strength of the rules of the CS will reflect the relative relationship between rules satisfactorily and, therefore, the application of the genetic algorithm will produce the desired effects. However, when the learning process presents individual cases and allows the system to learn gradually from these cases, each learning interval with a set of individual cases can lead the strength to be distributed in favour of a given type of rules that would in turn be favoured by the genetic algorithm. Basically, the idea is to divide rules into groups such that they are forced to remain in the system. This contribution is a method of learning that allows similar knowledge to be grouped. A field in which knowledge-based systems researchers have done a lot of work is concept classification and the relationships that are established between these concepts in the stage of knowledge conceptualization for later formalization. This job of classifying and searching relationships is performed in the proposed classifier systems by means of a mechanism. Tags, that allows the classification and the relationships to be discovered without the need for expert knowledge

Reactive with tags classifier system applied to real robot navigation

7th IEEE International Conference on Emerging Technologies and Factory Automation. Barcelona, 18-21 October 1999.A reactive with tags classifier system (RTCS) is a special classifier system. This system combines the execution capabilities of symbolic systems and the learning capabilities of genetic algorithms. A RTCS is able to learn symbolic rules that allow to generate sequence of actions, chaining rules among different time instants, and react to new environmental situations, considering the last environmental situation to take a decision. The capacity of RTCS to learn good rules has been prove in robotics navigation problem. Results show the suitability of this approximation to the navigation problem and the coherence of extracted rules