Fragmentation Experiment and Model for Falling Mercury Drops

The experiment consists of counting and measuring the size of the many fragments observed after the fall of a mercury drop on the floor. The size distribution follows a power-law for large enough fragments. We address the question of a possible crossover to a second, different power-law for small enough fragments. Two series of experiments were performed. The first uses a traditional film photographic camera, and the picture is later treated on a computer in order to count the fragments and classify them according to their sizes. The second uses a modern digital camera. The first approach has the advantage of a better resolution for small fragment sizes. The second, although with a poorer size resolution, is more reliable concerning the counting of all fragments up to its resolution limit. Both together clearly indicate the real existence of the quoted crossover. The model treats the system microscopically during the tiny time interval when the initial drop collides with the floor. The drop is modelled by a connected cluster of Ising spins pointing up (mercury) surrounded by Ising spins pointing down (air). The Ising coupling which tends to keep the spins segregated represents the surface tension. Initially the cluster carries an extra energy equally shared among all its spins, corresponding to the coherent kinetic energy due to the fall. Each spin which touches the floor loses its extra energy transformed into a thermal, incoherent energy represented by a temperature used then to follow the dynamics through Monte Carlo simulations. Whenever a small piece becomes disconnected from the big cluster, it is considered a fragment, and counted. The results also indicate the existence of the quoted crossover in the fragment-size distribution.Comment: 6 pages, 3 figure

Phase transition in a mean-field model for sympatric speciation

We introduce an analytical model for population dynamics with intra-specific competition, mutation and assortative mating as basic ingredients. The set of equations that describes the time evolution of population size in a mean-field approximation may be decoupled. We find a phase transition leading to sympatric speciation as a parameter that quantifies competition strength is varied. This transition, previously found in a computational model, occurs to be of first order.Comment: accepted for Physica

Critical Exponents for Nuclear Multifragmentation: dynamical lattice model

We present a dynamical and dissipative lattice model, designed to mimic nuclear multifragmentation. Monte-Carlo simulations with this model show clear signature of critical behaviour and reproduce experimentally observed correlations. In particular, using techniques devised for finite systems, we could obtain two of its critical exponents, whose values are in agreement with those of the universality class to which nuclear multifragmentation is supposed to belong.Comment: 10 pages, 3 figures, to be published in Nuclear Physics

Simulated emergence of cyclic sexual-asexual reproduction

Motivated by the cyclic pattern of reproductive regimes observed in some species of green flies (``{\it aphids}''), we simulate the evolution of a population enduring harsh seasonal conditions for survival. The reproductive regime of each female is also seasonal in principle and genetically acquired, and can mutate for each newborn with some small probability. The results show a sharp transition at a critical value of the survival probability in the winter, between a reproductive regime in the fall that is predominantly sexual, for low values of this probability, or asexual, for high values.Comment: 9 pages, 4 figures, requires RevTe

On the energy growth of some periodically driven quantum systems with shrinking gaps in the spectrum

We consider quantum Hamiltonians of the form H(t)=H+V(t) where the spectrum of H is semibounded and discrete, and the eigenvalues behave as E_n~n^\alpha, with 0<\alpha<1. In particular, the gaps between successive eigenvalues decay as n^{\alpha-1}. V(t) is supposed to be periodic, bounded, continuously differentiable in the strong sense and such that the matrix entries with respect to the spectral decomposition of H obey the estimate |V(t)_{m,n}|0, p>=1 and \gamma=(1-\alpha)/2. We show that the energy diffusion exponent can be arbitrarily small provided p is sufficiently large and \epsilon is small enough. More precisely, for any initial condition \Psi\in Dom(H^{1/2}), the diffusion of energy is bounded from above as _\Psi(t)=O(t^\sigma) where \sigma=\alpha/(2\ceil{p-1}\gamma-1/2). As an application we consider the Hamiltonian H(t)=|p|^\alpha+\epsilon*v(\theta,t) on L^2(S^1,d\theta) which was discussed earlier in the literature by Howland

Determining the density of states for classical statistical models: A random walk algorithm to produce a flat histogram

We describe an efficient Monte Carlo algorithm using a random walk in energy space to obtain a very accurate estimate of the density of states for classical statistical models. The density of states is modified at each step when the energy level is visited to produce a flat histogram. By carefully controlling the modification factor, we allow the density of states to converge to the true value very quickly, even for large systems. This algorithm is especially useful for complex systems with a rough landscape since all possible energy levels are visited with the same probability. In this paper, we apply our algorithm to both 1st and 2nd order phase transitions to demonstrate its efficiency and accuracy. We obtained direct simulational estimates for the density of states for two-dimensional ten-state Potts models on lattices up to $200 \times 200$ and Ising models on lattices up to $256 \times 256$. Applying this approach to a 3D $\pm J$ spin glass model we estimate the internal energy and entropy at zero temperature; and, using a two-dimensional random walk in energy and order-parameter space, we obtain the (rough) canonical distribution and energy landscape in order-parameter space. Preliminary data suggest that the glass transition temperature is about 1.2 and that better estimates can be obtained with more extensive application of the method.Comment: 22 pages (figures included

Concentração de macronutrientes em função da idade, doses de fósforo aplicadas e partes de soja (Glycine max (L.) Merrill)

This study was conducted with the objective of determining the concentration of macronutrients in the plant as function of fertilization with nitrogen, phosphorus and potassium. A 3³ factorial experiment with three replications was performed. The experimental area was located at Piracicaba (ESALQ), the soil belonging to the Guamium series. IAC-2, an indeterminate soybean cultivar, was used. N, P and K were applied in the rows at the levels of 0, 20 and 40 kg/ha (N), 0, 60 and 120 kg/ha (P(2)0(5)), and 0, 30 and 60 kg/ha (K(2)0), Plant samples were taken at 21-day intervals at emergence and continuing until partial fall of the leaves (105 days after emergence). The several plant parts were analysed for macronutrients (.N, P, K, Ca, Mg and S). The following conclusions were reached: during the period of greatest efficiency of the crop, the level of 40 kg/ha of nitrogen increased the nitrogen concentration in the upper leaves. The level of 120 kg/ha of P2O5 increased the concentration of phosphorus and potassium in the upper leaves. The highest concentration of calcium and magnesium were found in the lower leaves, while the highest concentrations of sulphur were found in the upper leaves, independent of the levels of N, P and K applied to the soil.O presente trabalho foi desenvolvido visando atingir o seguinte objetivo: determinação das concentrações dos macronutrientes, nas partes da planta, em função de níveis de adubação fosfatada. Para verificar os parâmetros propostos foi instalado um fatorial 3³ com três repetições em solo da serie Guamium, no município de Piracicaba, SP. Usou-se o cultivar IAC-2 de habito de crescimento indeterminado. Aplicou-se no sulco na ocasião da semeadura as seguintes quantidades de fertilizantes: 0, 20 e 40 kg de N por ha; 0, 60 e 120 kg de P2O5por ha; 0, 30 e 60 kg de K2O por ha. Foram igualmente incorporados 2,7 t de calcário dolomitico por ha. Foram colhidas amostras de plantas em intervalos de 21 dias, a partir da emergência, até a queda parcial das folhas aos 105 dias de idade. Conclusões: concentrações maiores de nitrogênio, fósforo, potássio e enxofre foram encontradas nas folhas superiores; e as maiores concentrações de cálcio e magnêsio localizaram-se nas folhas inferiores. As variações nas concentrações de fósforo e magnêsio com a idade das plantas, foram afetadas pelas doses de fósforo

Modulation Of The Catalytic Activity Of Porphyrins By Lipid- And Surfactant-containing Nanostructures

The structural factors modulating porphyrin activity encompass pyrrole and equatorial ligands, as well as the central metal and the number and structure of their axial ligands. Of equal importance is the microenvironment provided by apoproteins, solvents and membranes. Porphyrins are often used to construct supramolecular structures with different applications. The modulation of activity of the porphyrins has been frequently achieved by mimicking nature, i.e., by the provision of different microenvironments for these molecules. The association of porphyrins to surfactant- and lipid-containing nanostructures has changed the activity of these compounds to mimic different enzymes such as SOD, cytochrome P450, peroxidases and others. In determined conditions, the reactive forms of the porphyrins are high-valence states of oxo-metal-π cations and oxo-metal produced by the reaction with peroxides and peracids. The modulation of porphyrin activity by surfactant- and lipid-containing nanostructures has also been achieved for hemeproteins, as the lipid nanostructures affect the conformation of proteins. ©2011 Sociedade Brasileira de Química.22916211633Drain, C.M., Varotto, A., Radivojevic, I., (2009) Chem. Rev., 109, p. 1630Aida, T., Inoue, S., (2000) The Porphyrin Handbook, , Kadish, K., M. Smith, K., M. Guillard R., eds.Academic Press: San Diego ch. 42Ponka, P., (1999) Am. J. Med. Sci., 318, p. 241Da Silva, D.C., De Freitas-Silva, G., Do Nascimento, E., Rebouças, J.S., Barbeira, P.J., De Carvalho, M.E., Idemori, Y.M., (2008) J. Inorg. Biochem., 102, p. 1932Bochot, C., Bartoli, J.F., Frapart, Y., Dansette, P.M., Mansuy, D., Battioni, P., (2007) J. Mol. Catal. A.: Chem., 263, p. 200Alkordi, M.H., Liu, Y.L., Larsen, R.W., Eubank, J.F., Eddaoudi, M., (2008) J. Am. Chem. Soc., 130, p. 12639Suijkerbuijk, B., Schamhart, D.J., Kooijman, H., Spek, A.L., Van Koten, G., Gebbink, R., (2010) Dalton Trans., 39, p. 6198Groves, J.T., Nemo, T.E., (1983) J. Am. Chem. Soc., 10, p. 5786Drain, C.M., Smeureanu, G., Patel, S., Gong, X.C., Garno, J.A., (2006) New. J. Chem., 30, p. 1834Wang, Y.T., Jin, W.J., (2008) Spectrochim. Acta, Part A., 70, p. 871Komatsu, T., Moritake, W., Nakagawa, A., Tsuchida, E., (2002) Chem. - Eur. J., 8, p. 5469Nagami, H., Umakoshi, H., Shimanouchi, T., Kuboi, R., (2004) Biochem. Eng. J., 21, p. 221Szoka, F., Papahadjopoulos, D., (1980) Annu. Rev. Biophys. Bioeng., 9, p. 467Atkin, R., Craig, V.S.J., Wanless, E.J., Biggs, S., (2003) Adv. Colloid Interface Sci., 103, p. 219Grassert, I., Schinkowski, K., Vollhardt, D., Oehme, G., (1998) Chirality, 10, p. 754Hait, S.K., Moulik, S.P., (2001) J. Surfactants Deterg., 4, p. 303Glick, J., Santoyo, G., Casey, P.J., (1996) J. Biol. Chem., 271, p. 2949Maldotti, A., Andreotti, L., Molinari, A., Varani, G., Cerichelli, G., Chiarini, M., (2001) Green Chem., 3, p. 42Batrakova, E.V., Kabanov, A.V., (2008) J. Controlled Release, 130, p. 98Bangham, A.D., Standish, M.M., Watkins, J.C., (1965) J. Mol. Biol., 13, p. 238Johnson, S.M., Bangham, A.D., Hill, M.W., Korn, E.D., (1971) Biochim. Biophys. Acta. Biomembr., 233, p. 820Papahadj, D., Watkins, J.C., (1967) Biochim. Biophys. Acta, Biomembr., 135, p. 639Abramson, M.B., Katzman, R., Gregor, H.P., (1964) J. Biol. Chem., 239, p. 70Hauser, H.O., (1971) Biochem. Biophys. Res. Commun., 45, p. 1049Huang, C.H., (1969) Biochemistry, 8, p. 344Saunders, L., Gammack, D., Perrin, J., (1962) J. Pharm. Pharmacol., 14, p. 567Tien, H.T., (1974) Theory and Practice, , 1th ed.Bilayer Lipid Membranes (BLM): Marcel Dekker: New YorkRazin, S., (1972) Biochim. Biophys. Acta, Biomembr., 265, p. 241Korenbrot, J.I., (1977) Annu. Rev. Physiol., 39, p. 19Batzri, S., Korn, E.D., (1973) Biochim. Biophys. Acta, Biomembr., 298, p. 1015Deamer, D., Bangham, A.D., (1976) Biochim. Biophys. Acta, Biomembr., 443, p. 629Szoka, F., Papahadjopoulos, D., (1978) Proc. Natl. Acad. Sci. U. S. A., 75, p. 4194Mishra, P.P., Bhatnagar, J., Datta, A., (2005) J. Phys. Chem. B., 109, p. 24225Pessoto, F.S., Inada, N.M., Nepomuceno, M.D., Ruggiero, A.C., Nascimento, O.R., Vercesi, A.E., Nantes, I.L., (2009) Chem. Biol. Interact., 181, p. 400Steinbeck, C.A., Hedin, N., Chmelka, B.F., (2004) Langmuir, 20, p. 10399Vanesch, J.H., Feiters, M.C., Peters, A.M., Nolte, R.J.M., (1994) J. Phys. Chem., 98, p. 5541Barber, D.C., Freitagbeeston, R.A., Whitten, D.G., (1991) J. Phys. Chem., 95, p. 4074Maiti, N.C., Mazumdar, S., Periasamy, N.J., (1998) J. Porphyrins Phthalocyanines, 2, p. 369Schmehl, R.H., Whitten, D.G., (1981) J. Phys. Chem., 85, p. 3473Perrin, M.H., (1973) J. Chem. Phys., 59, p. 2090Zhou, X.T., Ji, H.B., (2010) Chem. Eng. J., 156, p. 411Monnereau, C., Ramos, P.H., Deutman, A.B.C., Elemans, J., Nolte, R.J.M., Rowan, A.E., (2010) J. Am. Chem. Soc., 132, p. 1529Merlau, M.L., Grande, W.J., Nguyen, S.T., Hupp, J.T., (2000) J. Mol. Catal. A: Chem., 156, p. 79Anzenbacher, P., Kral, V., Jursikova, K., Gunterova, J., Kasal, A., (1997) J. Mol. Catal. A: Chem., 118, p. 63Zhao, Y.C., Xiang, Y.Z., Pu, L., Yang, M., Yu, X.Q., (2006) Appl. Catal. A, 301, p. 176Zhou, X.T., Tang, Q.H., Ji, H.B., (2009) Tetrahedron Lett., 50, p. 6601Monfared, H.H., Aghapoor, V., Ghorbanloo, M., Mayer, P., (2010) Appl. Catal. A, 372, p. 209Heijnen, J.H.M., De Bruijn, V.G., Van Den Broeke, L.J.P., Keurentjes, J.T.F., (2003) Chem. Eng. Process., 42, p. 223Santos, A.C., Luz, R.A.S., Ferreira, L.G.F., Santos-Júnior, J.R., Silva, W.C., (2010) Quim. Nova, 33, p. 539Zhou, Y.B., Ryu, E.H., Zhao, Y., Woo, L.K., (2007) Organometallics, 26, p. 358Nantes, I.L., Crespilho, F.N., Mugnol, K.C.U., Chaves, J.C.A., Luz, R.A.S., Nascimento, O.R., Pinto, S.M.S., (2010) Circular Dichroism: Theory and Spectroscopy, , Rodgers D. S., ed.Nova Science Publishers: New York ch. 8Travascio, P., Sen, D., Bennet, A.J., (2006) Can. J. Chem., 84, p. 613Omodeo-Sale, F., Monti, D., Olliaro, P., Taramelli, D.P., (2001) Biochem. Pharmacol., 61, p. 999Ishigure, S., Mitsui, T., Ito, S., Kondo, Y., Kawabe, S., Kondo Dewa M, T., Mino, H., Nango, M., (2010) Langmuir, 26, p. 7774Umakoshi, H., Morimoto, K., Ohama, Y., Nagami, H., Shimanouchi, T., Kuboi, R., (2008) Langmuir, 24, p. 4451Prieto, T., Marcon, R.O., Prado, F.M., Caires, A.C.F., Di Mascio, P., Brochsztain, S., Nascimento, O.R., Nantes, I.L., (2006) J. Phys.Chem., 8, p. 1963Mugnol, K.C.U., Ando, R.A., Nagayasu, R.Y., Faljoni-Alario, A., Brochsztain, S., Santos, P.S., Nascimento, R.O., Nantes, I.L., (2008) Biophys. J., 94, p. 4066Nagatomo, H., Matsushita, Y., Sugamoto, K., Matsui, T., (2003) Tetrahedron: Asymmetry, 14, p. 2339Cantonetti, V., Monti, D., Venanzi, M., Bombelli, C., Ceccacci, F., Mancini, G., (2004) Tetrahedron: Asymmetry, 15, p. 1969Hiraka, K., Kanehisa, M., Tamai, M., Asayama, S., Nagaoka, S., Oyaizu, K., Yuasa, M., Kawakami, H., (2008) Colloids Surf. B, 67, p. 54Yuasa, M., Oyaizu, K., Horiuchi, A., Ogata, A., Hatsugai, T., Yamaguchi, A., Kawakami, H., (2004) Mol. Pharm., 1, p. 387Aron, J., Baldwin, D.A., Marques, H.M., Pratt, J.M., Adams, P.A., (1986) J. Inorg. Biochem., 27, p. 227Wang, J.S., Vanwart, H.E., (1989) J. Phys. Chem., 93, p. 7925Munro, O.Q., Marques, H.M., (1996) Inorg. Chem., 35, p. 3752Prieto, T., Nascimento, O.R., Tersariol, I.L.S., Faljoni-Alario, A., Nantes, I.L., (2004) J. Phys. Chem. B, 108, p. 11124Prieto, T., Nantes, I.L., Nascimento, O.R., (2004) Prog. Colloid Polym. Sci., 128, p. 1Makarska, M., Radzki, S., Legendziewicz, J., (2002) J. Alloys Compd., 341, p. 233Riposati, A., Prieto, T., Shida, C.S., Nantes, I.L., Nascimento, O.R., (2006) J. Inorg. Biochem., 100, p. 226Claiborne, A., Fridovich, I., (1979) J. Biol. Chem., 254, p. 4245Hiner, A.N.P., Ruiz, J.H., Lopez, J.N.R., Canovas, F.G., Brisset, N.C., Smith, A.T., Arnao, M.B., Acosta, M., (2002) J. Biol. Chem., 277, p. 26879Primus, J.L., Grunenwald, S., Hagedoorn, P.L., Albrecht-Gary, A.M., Mandon, D., Veeger, C.T., (2002) J. Am. Chem. Soc., 124, p. 1214Demontellano, P.R.O., (1992) Annu. Rev. Pharmacol. Toxicol., 32, p. 89Savenkova, M.I., Kuo, J.M., De Montellano, P.R.O., (1998) Biochemistry, 37, p. 10828Smith, A.T., Veitch, N.C., (1998) Curr. Opin. Chem. Biol., 2, p. 269Prieto, T., Mugnol, K.C.U., Araujo, J.C., Sousa, F.L., Soares, V.A., Cilento, G., Nantes, I.L., (2007) Catalysis and Photochemistry in Heterogeneous Media, , Nantes I. L.Brochesztain, S. eds.Research Signpost: Kerala ch. 1Zucchi, M.R., Nascimento, O.R., Faljoni-Alario, A., Prieto, T., Nantes, I.L., (2003) Biochem. J., 370, p. 671Kinnunen, P.K.J., (1992) Chem. Phys. Lipids, 63, p. 251Kawai, C., Prado, F.M., Nunes, G.L.C., Di Mascio, P., Carmona-Ribeiro, A.M., Nantes, I.L., (2005) J. Biol. Chem., 280, p. 34709Araujo, J.C., Prieto, T., Prado, F.M., Trindade, F.J., Nunes, G.L.C., Dos Santos, J.G., Di Masco, P., Nantes, I.L., (2007) J. Nanosci. Nanotechnol., 7, p. 3643Estevam, M.L., Nascimento, O.R., Baptista, M.S., Di Mascio, P., Prado, F.M., Faljoni-Alario, A., Zucchi, M.D., Nantes, I.L., (2004) J. Biol. Chem., 279, p. 39214Kawai, C., Nantes, I.L., Baptista, M.D.S., (2010) FEBS J., 277, p. 224Nantes, I.L., Faljoni-Alario, A., Vercesi, A.E., Santos, K.E., Bechara, E.J.H., (1998) Free Radic. Biol. Med., 25, p. 54

The spectral action and cosmic topology

The spectral action functional, considered as a model of gravity coupled to matter, provides, in its non-perturbative form, a slow-roll potential for inflation, whose form and corresponding slow-roll parameters can be sensitive to the underlying cosmic topology. We explicitly compute the non-perturbative spectral action for some of the main candidates for cosmic topologies, namely the quaternionic space, the Poincare' dodecahedral space, and the flat tori. We compute the corresponding slow-roll parameters and see we check that the resulting inflation model behaves in the same way as for a simply-connected spherical topology in the case of the quaternionic space and the Poincare' homology sphere, while it behaves differently in the case of the flat tori. We add an appendix with a discussion of the case of lens spaces.Comment: 55 pages, LaTe