Rotational properties of the Haumea family members and candidates: Short-term variability

Haumea is one of the most interesting and intriguing transneptunian objects (TNOs). It is a large, bright, fast rotator, and its spectrum indicates nearly pure water ice on the surface. It has at least two satellites and a dynamically related family of more than ten TNOs with very similar proper orbital parameters and similar surface properties. The Haumean family is the only one currently known in the transneptunian belt. Various models have been proposed but the formation of the family remains poorly understood. In this work, we have investigated the rotational properties of the family members and unconfirmed family candidates with short-term variability studies, and report the most complete review to date. We present results based on five years of observations and report the short-term variability of five family members, and seven candidates. The mean rotational periods, from Maxwellian fits to the frequency distributions, are 6.27+/-1.19 h for the confirmed family members, 6.44+/-1.16 h for the candidates, and 7.65+/-0.54 h for other TNOs (without relation to the family). According to our study, there is a suggestion that Haumea family members rotate faster than other TNOs, however, the sample of family member is still too limited for a secure conclusion. We also highlight the fast rotation of 2002 GH32. This object has a 0.36+/-0.02 mag amplitude lightcurve and a rotational period of about 3.98 h. Assuming 2002 GH32 is a triaxial object in hydrostatic equilibrium, we derive a lower limit to the density of 2.56 g cm^-3. This density is similar to Haumea's and much more dense than other small TNO densities.Comment: Accepted for publication, A

JCLEC Meets WEKA!

WEKA has recently become a very referenced DM tool. In spite of all the functionality it provides, it does not include any framework for the development of evolutionary algorithms. An evolutionary computation framework is JCLEC, which has been successfully employed for developing several EAs. The combination of both may lead in a mutual bene t. Thus, this paper proposes an intermediate layer to connect WEKA with JCLEC. It also presents a study case which samples the process of including a JCLEC's EA into WEK

Effective Dielectric Response of Metamaterials

We use a homogenization procedure for Maxwell's equations in order to obtain in the local limit the frequency ($\omega$) dependent macroscopic dielectric response $\epsilon^M(\omega)$ of metamaterials made of natural constituents with any geometrical shape repeated periodically with any structure. We illustrate the formalism calculating $\epsilon^M(\omega)$ for several structures. For dielectric rectangular inclusions within a conducting material we obtained a very anisotropic response which changes along one direction from conductor-like at low $\omega$ to a resonant dielectric-like at large $\omega$, attaining a very small reflectance at intermediate frequencies unrelated to surface plasmon excitation and which can be tuned through geometrycal tayloring. A similar behavior is obtained for other shapes close to the percolation threshold.Comment: 16 pages 7 figures. Accepted in Phys. Rev. B (2009-06-08

Hybrid genetic algorithm for clustering IC topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.Funding for open access charge: Universidad de Málaga / CBUA Funding for open access publishing: Universidad Málaga/CBUA. This work was supported by projects PGC2018-098,813-B C32 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086 (Consejería de economía y conocimiento, Junta de Andalucía), Project P18-rt-1624, and by European Regional Development Funds (ERDF). We also thank the Leeduca research group and Junta de Andalucía for the data supplied and the support

Alternative Splicing Variation: Accessing and Exploiting in Crop Improvement Programs

Alternative splicing (AS) is a gene regulatory mechanism modulating gene expression in multiple ways. AS is prevalent in all eukaryotes including plants. AS generates two or more mRNAs from the precursor mRNA (pre-mRNA) to regulate transcriptome complexity and proteome diversity. Advances in next-generation sequencing, omics technology, bioinformatics tools, and computational methods provide new opportunities to quantify and visualize AS-based quantitative trait variation associated with plant growth, development, reproduction, and stress tolerance. Domestication, polyploidization, and environmental perturbation may evolve novel splicing variants associated with agronomically beneficial traits. To date, pre-mRNAs from many genes are spliced into multiple transcripts that cause phenotypic variation for complex traits, both in model plant Arabidopsis and field crops. Cataloguing and exploiting such variation may provide new paths to enhance climate resilience, resource-use efficiency, productivity, and nutritional quality of staple food crops. This review provides insights into AS variation alongside a gene expression analysis to select for novel phenotypic diversity for use in breeding programs. AS contributes to heterosis, enhances plant symbiosis (mycorrhiza and rhizobium), and provides a mechanistic link between the core clock genes and diverse environmental clues

Analytical and discrete solutions for the incipient motion of ellipsoidal sediment particles

[EN] This work introduces analytical and numerical approaches to compute the incipient motion of ellipsoidal sediment particles. Initiation of motion of spherical particles is dominated by rolling mode. However, solutions for initiation of motion for non-spherical grains have to incorporate rolling, sliding, and mixed modes. The proposed approaches include a wide variety of shapes and inclinations that represent realistic configurations of sediment bed layers. The numerical procedure is based on the discrete element method, simulating the micro-mechanics of the sediment as an aggregate of rigid ellipsoids interacting by contact. The numerical solution covers a range of incipient movements that cannot be covered by the analytical approach. Hence, some trapped modes observed in analytical calculations are complemented by the numerical computation of threshold stresses. The main results are organized as novel extended Shields diagrams for non-spherical grains, where non-dimensional critical shear stress is represented in terms of friction Reynolds number.This work was supported by the Ministerio de Ciencia e Innovación Grant [#BIA-2012-32918 and #BIA-2015-64994-P (MINECO/FEDER)].Bravo, R.; Ortiz, P.; Pérez-Aparicio, JL. (2018). Analytical and discrete solutions for the incipient motion of ellipsoidal sediment particles. Journal of Hydraulic Research. 56(1):29-43. https://doi.org/10.1080/00221686.2017.1289263S2943561Belytschko, T., & Neal, M. O. (1991). Contact-impact by the pinball algorithm with penalty and Lagrangian methods. International Journal for Numerical Methods in Engineering, 31(3), 547-572. doi:10.1002/nme.1620310309Bravo, R., Ortiz, P., & Pérez-Aparicio, J. L. (2014). Incipient sediment transport for non-cohesive landforms by the discrete element method (DEM). Applied Mathematical Modelling, 38(4), 1326-1337. doi:10.1016/j.apm.2013.08.010Bravo, R., Pérez-Aparicio, J. L., & Gómez-Hernández, J. J. (2015). Numerical sedimentation particle-size analysis using the Discrete Element Method. Advances in Water Resources, 86, 58-72. doi:10.1016/j.advwatres.2015.09.024Bravo, R., Pérez-Aparicio, J. L., & Laursen, T. A. (2012). An energy consistent frictional dissipating algorithm for particle contact problems. International Journal for Numerical Methods in Engineering, 92(9), 753-781. doi:10.1002/nme.4346Buffington, J. M., & Montgomery, D. R. (1997). A systematic analysis of eight decades of incipient motion studies, with special reference to gravel-bedded rivers. Water Resources Research, 33(8), 1993-2029. doi:10.1029/96wr03190Cheng, N.-S., & Chiew, Y.-M. (1999). Incipient sediment motion with upward seepage. Journal of Hydraulic Research, 37(5), 665-681. doi:10.1080/00221689909498522Chiew, Y.-M., & Parker, G. (1994). Incipient sediment motion on non-horizontal slopes. Journal of Hydraulic Research, 32(5), 649-660. doi:10.1080/00221689409498706Derksen, J. J. (2015). Simulations of granular bed erosion due to a mildly turbulent shear flow. Journal of Hydraulic Research, 53(5), 622-632. doi:10.1080/00221686.2015.1077354Dey, S. (1999). Sediment threshold. Applied Mathematical Modelling, 23(5), 399-417. doi:10.1016/s0307-904x(98)10081-1Dey, S. (2003). Threshold of sediment motion on combined transverse and longitudinal sloping beds. Journal of Hydraulic Research, 41(4), 405-415. doi:10.1080/00221680309499985Dey, S., Sarker, H. K. D., & Debnath, K. (1999). Sediment Threshold under Stream Flow on Horizontal and Sloping Beds. Journal of Engineering Mechanics, 125(5), 545-553. doi:10.1061/(asce)0733-9399(1999)125:5(545)Hölzer, A., & Sommerfeld, M. (2008). New simple correlation formula for the drag coefficient of non-spherical particles. Powder Technology, 184(3), 361-365. doi:10.1016/j.powtec.2007.08.021James, C. S. (1990). Prediction of entrainment conditions for nonuniform, noncohesive sediments. Journal of Hydraulic Research, 28(1), 25-41. doi:10.1080/00221689009499145Ji, C., Munjiza, A., Avital, E., Ma, J., & Williams, J. J. R. (2013). Direct numerical simulation of sediment entrainment in turbulent channel flow. Physics of Fluids, 25(5), 056601. doi:10.1063/1.4807075Klamkin, M. S. (1971). Elementary Approximations to the Area of N-Dimensional Ellipsoids. The American Mathematical Monthly, 78(3), 280. doi:10.2307/2317530Mandø, M., & Rosendahl, L. (2010). On the motion of non-spherical particles at high Reynolds number. Powder Technology, 202(1-3), 1-13. doi:10.1016/j.powtec.2010.05.001MILLER, M. C., McCAVE, I. N., & KOMAR, P. D. (1977). Threshold of sediment motion under unidirectional currents. Sedimentology, 24(4), 507-527. doi:10.1111/j.1365-3091.1977.tb00136.xWan Mohtar, W. H. M., & Munro, R. J. (2013). Threshold criteria for incipient sediment motion on an inclined bedform in the presence of oscillating-grid turbulence. Physics of Fluids, 25(1), 015103. doi:10.1063/1.4774341Ortiz, P., & Smolarkiewicz, P. K. (2006). Numerical simulation of sand dune evolution in severe winds. International Journal for Numerical Methods in Fluids, 50(10), 1229-1246. doi:10.1002/fld.1138Ortiz, P., & Smolarkiewicz, P. K. (2009). Coupling the dynamics of boundary layers and evolutionary dunes. Physical Review E, 79(4). doi:10.1103/physreve.79.041307Van Rijn, L. C. (1984). Sediment Transport, Part I: Bed Load Transport. Journal of Hydraulic Engineering, 110(10), 1431-1456. doi:10.1061/(asce)0733-9429(1984)110:10(1431)Shi, G.-H., & Goodman, R. E. (1985). Two dimensional discontinuous deformation analysis. International Journal for Numerical and Analytical Methods in Geomechanics, 9(6), 541-556. doi:10.1002/nag.1610090604Shields, A. (1936). Application of similarity principles and turbulence research to bed-load movement (Tech. Rep.). Lab. for Hydraulic Water Resources.Wellmann, C., Lillie, C., & Wriggers, P. (2008). A contact detection algorithm for superellipsoids based on the common‐normal concept. Engineering Computations, 25(5), 432-442. doi:10.1108/02644400810881374Wiberg, P. L., & Smith, J. D. (1985). A theoretical model for saltating grains in water. Journal of Geophysical Research, 90(C4), 7341. doi:10.1029/jc090ic04p0734