Symplectic Regularization of Binary Collisions in the Circular N+2 Sitnikov Problem

We present a brief overview of the regularizing transformations of the Kepler problem and we relate the Euler transformation with the symplectic structure of the phase space of the N-body problem. We show that any particular solution of the N-body problem where two bodies have rectilinear dynamics can be regularized by a linear symplectic transformation and the inclusion of the Euler transformation into the group of symplectic local diffeomorphisms over the phase space. As an application we regularize a particular configuration of the circular N+2 Sitnikov problem.Comment: 23 pages, 5 figures. References to algorithmic regularization included, changes in References and small typographic corrections. Accepted in J. of Phys. A: Math. Theor 44 (2011) 265204 http://stacks.iop.org/1751-8121/44/26520

Effect of filler nature and content on the bituminous mastic behaviour under cyclic loads

The role of the filler in asphalt mixtures is particularly important because of its influence on mastic behaviour. The filler improves the resistance properties of bitumen against the action of traffic loads and temperature. However, the filler can also adversely affect bitumen in mastics excessively brittle and stiff due to inappropriate design. For these reasons, it is interesting to investigate the effect of filler type and content on mastic composition. This paper presents results from a strain sweep test applied to bituminous mastics prepared with different filler types and contents at several temperatures. The obtained stiffness modulus and failure strain results provide information to assess the fatigue behaviour of the analysed mastics.Peer ReviewedPostprint (author's final draft

Unsupervised machine learning approach for building composite indicators with fuzzy metrics

[EN] This study aims at developing a new methodological approach for building composite indicators, focusingon the weight schemes through an unsupervised machine learning technique. The composite indicatorproposed is based on fuzzy metrics to capture multidimensional concepts that do not have boundaries, suchas competitiveness, development, corruption or vulnerability. This methodology is designed for formativemeasurement models using a set of indicators measured on different scales (quantitative, ordinal and binary)and it is partially compensatory. Under a benchmarking approach, the single indicators are synthesized.The optimization method applied manages to remove the overlapping information provided for the singleindicators, so that the composite indicator provides a more realistic and faithful approximation to the conceptwhich would be studied. It has been quantitatively and qualitatively validated with a set of randomizeddatabases covering extreme and usual cases.This work was supported by the project FEDER-University of Granada (B-SEJ-242.UGR20), 2021-2023: An innovative methodological approach for measuring multidimensional poverty in Andalusia (COMPOSITE). Eduardo Jimenez-Fernandez would also like to thank the support received from Universitat Jaume I under the grant E-2018-03.Jiménez Fernández, E.; Sánchez, A.; Sánchez Pérez, EA. (2022). Unsupervised machine learning approach for building composite indicators with fuzzy metrics. Expert Systems with Applications. 200:1-11. https://doi.org/10.1016/j.eswa.2022.11692711120

An Optimal Frontier of the Efficiency of Tissue P Systems with Cell Division

In the framework of tissue P systems with cell division, the length of communication rules provides a frontier for the tractability of decision problems. On the one hand, the limitation on the efficiency of tissue P systems with cell division and communication rules of length 1 has been established. On the other hand, polynomial time solutions to NP–complete problems by using families of tissue P systems with cell division and communication rules of length at most 3 has been provided. In this paper, we improve the previous result by showing that the HAM-CYCLE problem can be solved in polynomial time by a family of tissue P systems with cell division by using communication rules with length at most 2. Hence, a new tractability boundary is given: passing from 1 to 2 amounts to passing from non–efficiency to efficiency, assuming that P ̸= NP.Ministerio de Ciencia e Innovación TIN2009-13192Junta de Andalucía P08 – TIC 0420

Short-term microbial response after laboratory heating and ground mulching adition.

Fire alters soil organic matter inducing quantitative and qualitative changes that presumably will affect post-fire soil microbial recolonisation. Several studies have evidenced marked soil organic carbon reduction after moderate and high intensity fire, which limit the total recovery of microbial biomass during years. In order to evaluate the role of soil organic matter alteration in short-term microbial colonization process, we perform a preliminary experiment where unaltered soil from Sierra Nevada Natural Park was heated at 300 ºC during 20 minutes in a muffle furnace (H300) to simulate a medium-high intensity fire. After heating, soil samples were inoculated with unaltered fresh soil, rewetted at 55-65% of water holding capacity and incubated during 3 weeks. At the same time, unheated soil samples were incubated under the same conditions as control (UH). In addition, trying to partially alleviate soil organic matter fire-induced alterations effects on microbial colonization, we include an organic amendment treatment (M+). So, part of heated and unheated samples were amended with a mix of ground alfalfa:straw (1:1) and soil microbial abundance and activity were monitored together with soil organic matter changes. Heating process reduces total organic carbon content. After one week of incubation carbon content in heated samples was lower than the control one, in both, amended and un-amended samples. Microbial biomass and respiration were negatively affected by heating. Ground mulching addition increase microbial biomass and respiration but was not enough to reach control values during the whole study. Nevertheless, viable and cultivable fungi and bacteria showed different pattern. After two weeks of incubation both, fungi and bacteria were higher in heated samples. Ground mulching addition appears to stimulate fungal response in both, heated and unheated samples. Preliminary results of this experiment evidence the transcendence of soil organic matter fire-induced changes on microbial colonization process and the importance to determine several microbial parameters to obtain a more faithful conclusion about microbial response. The organic amendment appears to alleviate partially heated-induced damage, highlighting the positive stimulation on fungal abundance in both, heated and unheated samples.This research has been funded by the Spanish Ministry of Economy and Competitiveness, through research projects POSTFIRE (CGL2013-47862-C2-1-R) and GEOFIRE (CGL2012-38655-C04-01)Peer Reviewe

Spanish name indexing errors in international databases.

Problems about spanish name normalisation in bibliographic database are presented

Isolation and characterization of phenanthrene degrading bacteria from diesel fuel-contaminated Antarctic soils

Indexación: Scopus.Antarctica is an attractive target for human exploration and scientific investigation, however the negative effects of human activity on this continent are long lasting and can have serious consequences on the native ecosystem. Various areas of Antarctica have been contaminated with diesel fuel, which contains harmful compounds such as heavy metals and polycyclic aromatic hydrocarbons (PAH). Bioremediation of PAHs by the activity of microorganisms is an ecological, economical, and safe decontamination approach. Since the introduction of foreign organisms into the Antarctica is prohibited, it is key to discover native bacteria that can be used for diesel bioremediation. By following the degradation of the PAH phenanthrene, we isolated 53 PAH metabolizing bacteria from diesel contaminated Antarctic soil samples, with three of these isolates exhibiting a high phenanthrene degrading capacity. In particular, the Sphingobium xenophagum D43FB isolate showed the highest phenanthrene degradation ability, generating up to 95% degradation of initial phenanthrene. D43FB can also degrade phenanthrene in the presence of its usual co-pollutant, the heavy metal cadmium, and showed the ability to grow using diesel-fuel as a sole carbon source. Microtiter plate assays and SEM analysis revealed that S. xenophagum D43FB exhibits the ability to form biofilms and can directly adhere to phenanthrene crystals. Genome sequencing analysis also revealed the presence of several genes involved in PAH degradation and heavy metal resistance in the D43FB genome. Altogether, these results demonstrate that S. xenophagum D43FB shows promising potential for its application in the bioremediation of diesel fuel contaminated-Antarctic ecosystems.https://www.frontiersin.org/articles/10.3389/fmicb.2017.01634/ful

AC VS. DC flash sintering: Influence of field frequency on flash processes

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Self-defined information indices: application to the case of university rankings

[EN] University rankings are now relevant decision-making tools for both institutional and private purposes in the management of higher education and research. However, they are often computed only for a small set of institutions using some sophisticated parameters. In this paper we present a new and simple algorithm to calculate an approximation of these indices using some standard bibliometric variables, such as the number of citations from the scientific output of universities and the number of articles per quartile. To show our technique, some results for the ARWU index are presented. From a technical point of view, our technique, which follows a standard machine learning scheme, is based on the interpolation of two classical extrapolation formulas for Lipschitz functions defined in metric spaces-the so-called McShane and Whitney formulae-. In the model, the elements of the metric space are the universities, the distances are measured using some data that can be extracted from the Incites database, and the Lipschitz function is the ARWU index.The third and fourth authors gratefully acknowledge the support of the Ministerio de Ciencia, Innovacion y Universidades (Spain), Agencia Estatal de Investigacion, and FEDER, under Grant MTM2016-77054-C2-1-P. The first author gratefully acknowledge the support of Catedra de Transparencia y Gestion de Datos, Universitat Politecnica de Valencia y Generalitat Valenciana, Spain.Ferrer Sapena, A.; Erdogan, E.; Jiménez-Fernández, E.; Sánchez Pérez, EA.; Peset Mancebo, MF. (2020). Self-defined information indices: application to the case of university rankings. Scientometrics. 124(3):2443-2456. https://doi.org/10.1007/s11192-020-03575-6S244324561243Aguillo, I., Bar-Ilan, J., Levene, M., & Ortega, J. (2010). Comparing university rankings. Scientometrics, 85(1), 243–256.Asadi, K., Dipendra, M., & Littman, M. L. (2018). Lipschitz continuity in model-based reinforcement learning. In Proceedings of the 35th International Conference on Machine Learning, Proc. Mach. Lear. Res., Vol. 80, pp. 264–273.Bougnol, M. L., & Dulá, J. H. (2013). A mathematical model to optimize decisions to impact multi-attribute rankings. Scientometrics, 95(2), 785–796.Çakır, M. P., Acartürk, C., Alaşehir, O., & Çilingir, C. (2015). A comparative analysis of global and national university ranking systems. Scientometrics, 103(3), 813–848.Cancino, C. A., Merigó, J. M., & Coronado, F. C. (2017). A bibliometric analysis of leading universities in innovation research. Journal of Innovation & Knowledge, 2(3), 106–124.Chen, K.-H., & Liao, P.-Y. (2012). A comparative study on world university rankings: A bibliometric survey. Scientometrics, 92(1), 89–103.Cinzia, D., & Bonaccorsi, A. (2017). Beyond university rankings? Generating new indicators on universities by linking data in open platforms. Journal of the Association for Information Science and Technology, 68(2), 508–529.Cobzaş, Ş., Miculescu, R., & Nicolae, A. (2019). Lipschitz functions. Berlin: Springer.Deza, M. M., & Deza, E. (2009). Encyclopedia of distances. Berlin: Springer.2019 U-Multirank ranking: European universities performing well. https://ec.europa.eu/education/news/u-multirank-publishes-sixth-edition-en .Dobrota, M., Bulajic, M., Bornmann, L., & Jeremic, V. (2016). A new approach to the QS university ranking using the composite I-distance indicator: Uncertainty and sensitivity analyses. Journal of the Association for Information Science and Technology, 67(1), 200–211.Falciani, H., Calabuig, J. M., & Sánchez Pérez, E. A. (2020). Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets. Neurocomputing, 398, 172–184.Kehm, B. M. (2014). Global university rankings—Impacts and unintended side effects. European Journal of Education, 49(1), 102–112.Lim, M. A., & Øerberg, J. W. (2017). Active instruments: On the use of university rankings in developing national systems of higher education. Policy Reviews in Higher Education, 1(1), 91–108.Luo, F., Sun, A., Erdt, M., Raamkumar, A. S., & Theng, Y. L. (2018). Exploring prestigious citations sourced from top universities in bibliometrics and altmetrics: A case study in the computer science discipline. Scientometrics, 114(1), 1–17.Marginson, S. (2014). University rankings and social science. European Journal of Education, 49(1), 45–59.Pagell, R. A. (2014). Bibliometrics and university research rankings demystified for librarians. Library and information sciences (pp. 137–160). Berlin: Springer.Rao, A. (2015). Algorithms for Lipschitz extensions on graphs. Yale University: ProQuest Dissertations Publishing, 10010433.Rosa, K. D., Metsis, V., & Athitsos, V. (2012). Boosted ranking models: A unifying framework for ranking predictions. Knowledge and Information Systems, 30(3), 543–568.Saisana, M., d’Hombres, B., & Saltelli, A. (2011). Rickety numbers: Volatility of university rankings and policy implications. Research Policy, 40(1), 165–177.Tabassum, A., Hasan, M., Ahmed, S., Tasmin, R., Abdullah, D. M., & Musharrat, T. (2017). University ranking prediction system by analyzing influential global performance indicators. In 2017 9th International Conference on Knowledge and Smart Technology (KST) (pp. 126–131) IEEE.Van Raan, A. F. J., Van Leeuwen, T. N., & Visser, M. S. (2011). Severe language effect in university rankings: Particularly Germany and France are wronged in citation-based rankings. Scientometrics, 88(2), 495–498.von Luxburg, U., & Bousquet, O. (2004). Distance-based classification with Lipschitz functions. Journal of Machine Learning Research, 5, 669–695