A phylogenetic comparative analysis on the evolution of sequential hermaphroditism in seabreams (Teleostei : Sparidae)

The Sparids are an ideal group of fish in which to study the evolution of sexual systems since they exhibit a great sexual diversity, from gonochorism (separate sexes) to protandrous (male-first) and protogynous (female-first) sequential hermaphroditism (sex-change). According to the size-advantage model (SAM), selection should favour sex change when the second sex achieves greater reproductive success at a larger body size than the first sex. Using phylogenetic comparative methods and a sample of 68 sparid species, we show that protogyny and protandry evolve from gonochorism but evolutionary transitions between these two forms of sequential hermaphroditism are unlikely to happen. Using male gonadosomatic index (GSI) as a measure of investment in gametes and proxy for sperm competition, we find that, while gonochoristic and protogynous species support the predictions of SAM, protandrous species do not, as they exhibit higher GSI values than expected even after considering mating systems and spawning modes. We suggest that small males of protandrous species have to invest disproportionally more in sperm production than predicted not only when spawning in aggregations with high levels of sperm competition, but also when spawning in pairs due to the need to fertilize highly fecund females, much larger than themselves. We propose that this compensatory mechanism, together with Bateman’s principles in sequential hermaphrodites, should be formally incorporated in the SAM

A discrepancy principle for the Landweber iteration based on risk minimization

In this paper we propose a criterion based on risk minimization to stop the Landweber algorithm for estimating the solution of a linear system with noisy data. Under the hypothesis of white Gaussian noise, we provide an unbiased estimator of the risk and we use it for defining a variant of the classical discrepancy principle. Moreover, we prove that the proposed variant satisfies the regularization property in expectation. Finally, we perform some numerical simulations when the signal formation model is given by a convolution or a Radon transform, to show that the proposed method is numerically reliable and furnishes slightly better solutions than classical estimators based on the predictive risk, namely the Unbiased Predictive Risk Estimator and the Generalized Cross Validation

Strangelet spectra from type II supernovae

We study in this work the fate of strangelets injected as a contamination in the tail of a "strange matter-driven" supernova shock. A simple model for the fragmentation and braking of the strangelets when they pass through the expanding oxygen shell is presented and solved to understand the reprocessing of this component. We find that the escaping spectrum is a scaled-down version of the one injected at the base of the oxygen shell. The supernova source is likely to produce low-energy particles of $A \sim 100-1000$ quite independently of the initial conditions. However, it is difficult that ultrarrelativistic strangelets (such as the hypothetical Centauro primaries) can have an origin in those explosive events.Comment: RevTex file, 5 pp., no figure

A Diffusion Model for Classical Chaotic Compound Scattering

We consider the classical map proposed previously to be the exact classical analogue of Rydberg Molecules calculated with the approximations relevant to the multi-channel quantum defect theory. The resulting classical map is analyzed at energies above the threshold for the Rydberg electron. At energies very near to this threshold we find the possibility of bounded motion for positive energy due to conserved tori as well as the possibility of forming a compound system, i.e. a system where the particle is trapped for long times before emerging again to the continuum. The compound scattering displays unusual features for short time behavior. A diffusion model explains these features.Comment: 29 pages, 16 eps figures, LaTeX (elsart), introduction and background info improve

Count-based imaging model for the Spectrometer/Telescope for Imaging X-rays (STIX) in Solar Orbiter

The Spectrometer/Telescope for Imaging X-rays (STIX) will study solar flares across the hard X-ray window provided by the Solar Orbiter cluster. Similarly to the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), STIX is a visibility-based imaging instrument that will require Fourier-based image reconstruction methods. However, in this paper we show that as for RHESSI, count-based imaging is also possible for STIX. Specifically, we introduce and illustrate a mathematical model that mimics the STIX data formation process as a projection from the incoming photon flux into a vector consisting of 120 count components. Then we test the reliability of expectation maximization for image reconstruction in the case of several simulated configurations that are typical of flare morphology

Decay versus survival of a localized state subjected to harmonic forcing: exact results

We investigate the survival probability of a localized 1-d quantum particle subjected to a time dependent potential of the form $rU(x)\sin{\omega t}$ with $U(x)=2\delta (x-a)$ or $U(x)= 2\delta(x-a)-2\delta (x+a)$. The particle is initially in a bound state produced by the binding potential $-2\delta (x)$. We prove that this probability goes to zero as $t\to\infty$ for almost all values of $r$, $\omega$, and $a$. The decay is initially exponential followed by a $t^{-3}$ law if $\omega$ is not close to resonances and $r$ is small; otherwise the exponential disappears and Fermi's golden rule fails. For exceptional sets of parameters $r,\omega$ and $a$ the survival probability never decays to zero, corresponding to the Floquet operator having a bound state. We show similar behavior even in the absence of a binding potential: permitting a free particle to be trapped by harmonically oscillating delta function potential

Rotation And Magnetic Evolution Of Superconducting Strange Stars

Is pulsar make up of strange matter? The magnetic field decay of a pulsar may be able to give us an answer. Since Cooper pairing of quarks occurs inside a sufficiently cold strange star, the strange stellar core is superconducting. In order to compensate the effect of rotation, different superconducting species inside a rotating strange star try to set up different values of London fields. Thus, we have a frustrated system. Using Ginzburg-Landau formalism, I solved the problem of rotating a superconducting strange star: Instead of setting up a global London field, vortex bundles carrying localized magnetic fields are formed. Moreover, the number density of vortex bundles is directly proportional to the angular speed of the star. Since it is energetically favorable for the vortex bundles to pin to magnetic flux tubes, the rotational dynamics and magnetic evolution of a strange star are coupled together, leading to the magnetic flux expulsion as the star slows down. I investigate this effect numerically and find that the characteristic field decay time is much less than 20~Myr in all reasonable parameter region. On the other hand, the characteristic magnetic field decay time for pulsars is $\geq 20$~Myr. Thus, my finding cast doubt on the hypothesis that pulsars are strange stars.Comment: 42 pages (including 13 eps figures) in AASTex 4.0 style with AMSFont

Electropolishing of medical-grade stainless steel in preparation for surface nano-texturing

The purpose of this work is to investigate the electropolishing of medical grade 316L stainless steel to obtain a clean, smooth and defect free surface in preparation for surface nano-texturing. Electropolishing of steel was conducted under stationary conditions in four electrolyte mixtures: A) 4.5 M H2SO4 + 11 M H3PO4, B) 7.2 M H2SO4 + 6.5 M H3PO4, C) 6.4 M glycerol + 6.1 M H3PO4 and D) 6.1 M H3PO4. The influence of electrolyte composition and concentration, temperature and electropolishing time, in conjunction with linear sweep voltammetry and chronoamperometry, on the stainless steel surface was studied. The activation energies for dissolution of steel in the four electrolyte solutions were calculated. The resulting surfaces of unpolished and optimally-polished stainless steel were characterised in terms of contamination, defects, topography, roughness, hydrophilicity and chemical composition by optical and atomic force microscopies, contact angle goniometry and x-ray photoelectron spectroscopy. It was found that the optimally polished surfaces were obtained with the following parameters: electrolyte mixture A at 2.1 V applied potential, 80 °C for 10 minutes. This corresponded to the diffusion-limited dissolution of the surface. The root mean square surface roughness of the electropolished surface achieved was 0.4 nm over 2 x 2 μm2. Surface analysis showed that electropolishing led to ultraclean surfaces with reduced roughness and contamination thickness, and with Cr, P, S, Mo, Ni and O enrichment compared to untreated surfaces

Diffusive Ionization of Relativistic Hydrogen-Like Atom

Stochastic ionization of highly excited relativistic hydrogenlike atom in the monochromatic field is investigated. A theoretical analisis of chaotic dynamics of the relativistic electron based on Chirikov criterion is given for the cases of one- and three-dimensional atoms. Critical value of the external field is evaluated analitically. The diffusion coefficient and ionization time are calculated.Comment: 13 pages, latex, no figures, submitted to PR

Prediction of severe thunderstorm events with ensemble deep learning and radar data

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the most used techniques rely on video prediction deep learning methods which take in input time series of radar reflectivity images to predict the next future sequence of reflectivity images, from which the predicted rainfall quantities are extrapolated. Differently from the previous works, the present paper proposes a deep learning method, exploiting videos of radar reflectivity frames as input and lightning data to realize a warning machine able to sound timely alarms of possible severe thunderstorm events. The problem is recast in a classification one in which the extreme events to be predicted are characterized by a an high level of precipitation and lightning density. From a technical viewpoint, the computational core of this approach is an ensemble learning method based on the recently introduced value-weighted skill scores for both transforming the probabilistic outcomes of the neural network into binary predictions and assessing the forecasting performance. Such value-weighted skill scores are particularly suitable for binary predictions performed over time since they take into account the time evolution of events and predictions paying attention to the value of the prediction for the forecaster. The result of this study is a warning machine validated against weather radar data recorded in the Liguria region, in Italy