Comparison of theoretical heat transfer model with results from experimental monitoring installed in a refurbishment with ventilated facade

One of the main points to consider when a building is renovated is the improvement of its energy efficiency, minimizing the heat loss through the enclosures and its heating consumption. Under this scope idea a ventilated facade was designed and incorporated in an educational building located in the city of Burgos (Spain). The main objective of this document is a comparison between the theoretical model of heat transfer across the building envelope separating the environment and the interior space, and the heat intake through a linear regression model with installed experimental monitoring. For this it has been necessary to carry out an exhaustive study of the thermal transmission of each one of the materials that make up the thermal envelope of the building, as well as the linear thermal bridges that can be produced before and after the renovation. In addition, thanks to the monitoring installed in the demonstrator building, the interior and exterior temperatures and the heat consumption of each of the radiators is known. In this way expected and real energy savings have been compared

Analysis of Moon impact flashes detected during the 2012 and 2013 Perseids

We present the results of our Moon impact flashes detection campaigns performed around the maximum activity period of the Perseid meteor shower in 2012 and 2013. Just one flash produced by a Perseid meteoroid was detected in 2012 because of very unfavourable geometric conditions, but 12 of these were confirmed in 2013. The visual magnitude of the flashes ranged between 6.6 and 9.3. A luminous efficiency of 1.8 $\times$ 10$^{-3}$ has been estimated for meteoroids from this stream. According to this value, impactor masses would range between 1.9 and 190 g. In addition, we propose a criterion to establish, from a statistical point of view, the likely origin of impact flashes recorded on the lunar surface.Comment: Accepted for publication in Astronomy and Astrophysics on March 11, 201

How universal can an intelligence test be?

[EN] The notion of a universal intelligence test has been recently advocated as a means to assess humans, non-human animals and machines in an integrated, uniform way. While the main motivation has been the development of machine intelligence tests, the mere concept of a universal test has many implications in the way human intelligence tests are understood, and their relation to other tests in comparative psychology and animal cognition. From this diversity of subjects in the natural and artificial kingdoms, the very possibility of constructing a universal test is still controversial. In this paper we rephrase the question of whether universal intelligence tests are possible or not into the question of how universal intelligence tests can be, in terms of subjects, interfaces and resolutions. We discuss the feasibility and difficulty of universal tests depending on several levels according to what is taken for granted: the communication milieu, the resolution, the reward system or the agent itself. We argue that such tests must be highly adaptive, i.e., that tasks, resolution, rewards and communication have to be adapted according to how the evaluated agent is reacting and performing. Even so, the most general expression of a universal test may not be feasible (and, at best, might only be theoretically semi-computable). Nonetheless, in general, we can analyse the universality in terms of some traits that lead to several levels of universality and set the quest for universal tests as a progressive rather than absolute goal.This work was supported by the MEC/MINECO (projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02), the GVA (project PROMETEO/2008/051) and the COST-European Cooperation in the field of Scientific and Technical Research (project IC0801 AT).Dowe, DL.; Hernández Orallo, J. (2014). How universal can an intelligence test be?. Adaptive Behavior. 22(1):51-69. https://doi.org/10.1177/1059712313500502S516922

On potential cognitive abilities in the machine kingdom

The final publication is available at Springer via http://dx.doi.org/10.1007/s11023-012-9299-6Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different for the ‘machine kingdom’. While machines can be characterised by a set of cognitive abilities, and measuring them is already a big challenge, known as ‘universal psychometrics’, a more informative, and yet more challenging, goal would be to also determine the potential cognitive abilities of a machine. In this paper we investigate the notion of potential cognitive ability for machines, focussing especially on universality and intelligence. We consider several machine characterisations (non-interactive and interactive) and give definitions for each case, considering permanent and temporal potentials. From these definitions, we analyse the relation between some potential abilities, we bring out the dependency on the environment distribution and we suggest some ideas about how potential abilities can be measured. Finally, we also analyse the potential of environments at different levels and briefly discuss whether machines should be designed to be intelligent or potentially intelligent.We thank the anonymous reviewers for their comments, which have helped to significantly improve this paper. This work was supported by the MEC-MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT. Finally, we thank three pioneers ahead of their time(s). We thank Ray Solomonoff (1926-2009) and Chris Wallace (1933-2004) for all that they taught us, directly and indirectly. And, in his centenary year, we thank Alan Turing (1912-1954), with whom it perhaps all began.Hernández-Orallo, J.; Dowe, DL. (2013). On potential cognitive abilities in the machine kingdom. Minds and Machines. 23(2):179-210. https://doi.org/10.1007/s11023-012-9299-6S179210232Amari, S., Fujita, N., Shinomoto, S. (1992). Four types of learning curves. Neural Computation 4(4), 605–618.Aristotle (Translation, Introduction, and Commentary by Ross, W.D.) (1924). Aristotle’s Metaphysics. Oxford: Clarendon Press.Barmpalias, G. & Dowe, D. L. (2012). 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Forster (Eds), Handbook of the philosophy of science—Volume 7: Philosophy of statistics (pp. 901–982). Amsterdam: Elsevier.Dowe, D. L. & Hajek, A. R. (1997a). A computational extension to the turing test. Technical report #97/322, Dept Computer Science, Monash University, Melbourne, Australia, 9 pp, http://www.csse.monash.edu.au/publications/1997/tr-cs97-322-abs.html .Dowe, D. L. & Hajek, A. R. (1997b, September). A computational extension to the Turing Test. in Proceedings of the 4th conference of the Australasian Cognitive Science Society, University of Newcastle, NSW, Australia, 9 pp.Dowe, D. L. & Hajek, A. R. (1998, February). A non-behavioural, computational extension to the Turing Test. In: International conference on computational intelligence and multimedia applications (ICCIMA’98), Gippsland, Australia, pp 101–106.Dowe, D. L., Hernández-Orallo, J. (2012). IQ tests are not for machines, yet. Intelligence, 40(2), 77–81.Gallistel, C. R., Fairhurst, S., & Balsam, P. (2004). 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(Eds.), Proceedings of 3rd international conference on artificial general intelligence (pp. 25–30). New York: Atlantis Press.Hernández-Orallo, J., & Dowe, D. L. (2010). Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence, 174(18), 1508–1539.Hernández-Orallo, J. & Dowe, D. L. (2011, April). Mammals, machines and mind games. Who’s the smartest?. The conversation, http://theconversation.edu.au/mammals-machines-and-mind-games-whos-the-smartest-566 .Hernández-Orallo J., Dowe D. L., España-Cubillo S., Hernández-Lloreda M. V., & Insa-Cabrera J. (2011). On more realistic environment distributions for defining, evaluating and developing intelligence. In: J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), Artificial general intelligence 2011, volume 6830, LNAI series, pp. 82–91. New York: Springer.Hernández-Orallo, J., Dowe, D. L., & Hernández-Lloreda, M. V. (2012a, March). Measuring cognitive abilities of machines, humans and non-human animals in a unified way: towards universal psychometrics. Technical report 2012/267, Faculty of Information Technology, Clayton School of I.T., Monash University, Australia.Hernández-Orallo, J., Insa, J., Dowe, D. L., & Hibbard, B. (2012b). Turing tests with Turing machines. In A. Voronkov (Ed.), The Alan Turing centenary conference, Turing-100, Manchester, volume 10 of EPiC Series, pp 140–156.Hernández-Orallo, J., & Minaya-Collado, N. (1998). A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In Proceedings of the international symposium of engineering of intelligent systems (EIS’98) (pp 146–163). Switzerland: ICSC Press.Herrmann, E., Call, J., Hernández-Lloreda, M. V., Hare, B., & Tomasello, M. (2007). Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science, 317(5843), 1360–1366.Herrmann, E., Hernández-Lloreda, M. 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Springer, New York.Kearns, M. & Singh, S. (2002). Near-optimal reinforcement learning in polynomial time. Machine Learning, 49(2), 209–232.Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of information. Problems of Information Transmission, 1, 4–7.Legg, S. (2008, June). Machine super intelligence. Department of Informatics, University of Lugano.Legg, S. & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391–444.Legg, S., & Veness, J. (2012). An approximation of the universal intelligence measure. In Proceedings of Solomonoff 85th memorial conference. New York: Springer.Levin, L. A. (1973). Universal sequential search problems. Problems of Information Transmission, 9(3), 265–266.Li, M., Vitányi, P. (2008). An introduction to Kolmogorov complexity and its applications (3rd ed). New York: Springer.Little, V. L., & Bailey, K. G. (1972). Potential intelligence or intelligence test potential? A question of empirical validity. Journal of Consulting and Clinical Psychology, 39(1), 168.Mahoney, M. V. (1999). Text compression as a test for artificial intelligence. In Proceedings of the national conference on artificial intelligence, AAAI (pp. 486–502). New Jersey: Wiley.Mahrer, A. R. (1958). Potential intelligence: A learning theory approach to description and clinical implication. The Journal of General Psychology, 59(1), 59–71.Oppy, G., & Dowe, D. L. (2011). The Turing Test. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Stanford University. http://plato.stanford.edu/entries/turing-test/ .Orseau, L. & Ring, M. (2011). Self-modification and mortality in artificial agents. In AGI: 4th conference on artificial general intelligence—Lecture Notes in Artificial Intelligence (LNAI), volume 6830, pages 1–10. Springer, New York.Ring, M. & Orseau, L. (2011). Delusion, survival, and intelligent agents. 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Minimum message length and Kolmogorov complexity. Computer Journal 42(4), 270–283.Wallace, C. S., & Dowe, D. L. (1999b). Refinements of MDL and MML coding. Computer Journal, 42(4), 330–337.Woergoetter, F., & Porr, B. (2008). Reinforcement learning. Scholarpedia, 3(3), 1448.Zvonkin, A. K., & Levin, L. A. (1970). The complexity of finite objects and the development of the concepts of information and randomness by means of the theory of algorithms. Russian Mathematical Surveys, 25, 83–124

Un currículo para el estudio de la historia de la ciencia en Secundaria : la experiencia del Seminario Orotava de Historia de la Ciencia

The curricula of two disciplines on History of Science at the High School level have been elaborated as part of the activities of a singular experience, Seminario Orotava de Historia de la Ciencia. The philosophy of the design considers science as an essential part of culture and interdisciplinarity as the most fitted method of teaching and learnign in a significative way

A rapid procedure for the isolation of plasmid DNA from environmental bacteria

The INSTA-MINI-PREP method, a rapid protocol for plasmid DNA extraction, was originally developed to prepare plasmid DNA from 1 to 3 ml miniprep Escherichia coli cultures. Direct extraction of plasmid DNA is achieved by a two-phase solution which is separated by centrifugation in the presence of the INSTA-PREP gel barrier material. This method has been successfully tested on various environmental Salmonella strains, although it was not suitable for Pseudomonas aeruginosa and enterococci strains. The INSTA-MINI-PREP method is a new alternative procedure to screen plasmid contents of Salmonella and E. coli strains rapidly and easily

Quinolone Resistance: Much More than Predicted

Since quinolones are synthetic antibiotics, it was predicted that mutations in target genes would be the only mechanism through which resistance could be acquired, because there will not be quinolone-resistance genes in nature. Contrary to this prediction, a variety of elements ranging from efflux pumps, target-protecting proteins, and even quinolone-modifying enzymes have been shown to contribute to quinolone resistance. The finding of some of these elements in plasmids indicates that quinolone resistance can be transferable. As a result, there has been a developing interest on the reservoirs for quinolone-resistance genes and on the potential risks associated with the use of these antibiotics in non-clinical environments. As a matter of fact, plasmid-encoded, quinolone-resistance qnr genes originated in the chromosome of aquatic bacteria. Thus the use of quinolones in fish-farming might constitute a risk for the emergence of resistance. Failure to predict the development of quinolone resistance reinforces the need of taking into consideration the wide plasticity of biological systems for future predictions. This plasticity allows pathogens to deal with toxic compounds, including those with a synthetic origin as quinolones

Innermost stable circular orbits around magnetized rotating massive stars

In 1998, Shibata and Sasaki [Phys. Rev. D 58, 104011 (1998)] presented an approximate analytical formula for the radius of the innermost stable circular orbit (ISCO) of a neutral test particle around a massive, rotating and deformed source. In the present paper, we generalize their expression by including the magnetic dipole moment. We show that our approximate analytical formulas are accurate enough by comparing them with the six-parametric exact solution calculated by Pach\'on et. al. [Phys. Rev. D 73, 104038 (2006)] along with the numerical data presented by Berti and Stergioulas [MNRAS 350, 1416 (2004)] for realistic neutron stars. As a main result, we find that in general, the radius at ISCO exhibits a decreasing behavior with increasing magnetic field. However, for magnetic fields below 100GT the variation of the radius at ISCO is negligible and hence the non-magnetized approximate expression can be used. In addition, we derive approximate analytical formulas for angular velocity, energy and angular momentum of the test particle at ISCO.Comment: 8 pages, 3 figure