Nonperiodic delay mechanism in time-dependent chaotic scattering

We study the occurence of delay mechanisms other than periodic orbits in systems with time dependent potentials that exhibit chaotic scattering. By using as model system two harmonically oscillating disks on a plane, we have found the existence of a mechanism not related to the periodic orbits of the system, that delays trajectories in the scattering region. This mechanism creates a fractal-like structure in the scattering functions and can possibly occur in several time-dependent scattering systems.Comment: 12 pages, 9 figure

A Global Model of β−\beta^--Decay Half-Lives Using Neural Networks

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of $\beta^-$-decay halflives of the class of nuclei that decay 100% by $\beta^-$ mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates generated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the $\beta^-$-decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart.Comment: Proceedings of the 16th Panhellenic Symposium of the Hellenic Nuclear Physics Societ

Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks

A neural-network model is developed to reproduce the differences between experimental nuclear mass-excess values and the theoretical values given by the Finite Range Droplet Model. The results point to the existence of subtle regularities of nuclear structure not yet contained in the best microscopic/phenomenological models of atomic masses. Combining the FRDM and the neural-network model, we create a hybrid model with improved predictive performance on nuclear-mass systematics and related quantities.Comment: Proceedings for the 15th Hellenic Symposium on Nuclear Physic

Hydrologic balance estimation using GIS in Korinthia prefecture, Greece

The main objective of this work is to determine the parameters of hydrological balance for several basins in the prefecture of Korinthia (SE Greece), using hydrometeorological data and geographic information systems (GIS) technology. Multiple linear regression and GIS were used to estimate the spatial distribution of rainfall. The largest precipitation amounts occur in the SW part of the region and decrease towards the eastern and northern coastal parts. The long term mean annual rainfall is 1.39×10<sup>9</sup> m<sup>3</sup>. Based on the Thornthwaite method, infiltration and streamflow were estimated to be 0.29×10<sup>9</sup> m<sup>3</sup>/yr and 0.38×10<sup>9</sup> m<sup>3</sup>/yr, respectively. The unequal distribution of rainfall results in water surplus in the western part of Korinthia prefecture and water deficit in the eastern. We conclude that he estimation of hydrologic balance is a useful tool in order to establish sustainable water resources management in each hydrological basin

Quantum versus Classical Dynamics in a driven barrier: the role of kinematic effects

We study the dynamics of the classical and quantum mechanical scattering of a wave packet from an oscillating barrier. Our main focus is on the dependence of the transmission coefficient on the initial energy of the wave packet for a wide range of oscillation frequencies. The behavior of the quantum transmission coefficient is affected by tunneling phenomena, resonances and kinematic effects emanating from the time dependence of the potential. We show that when kinematic effects dominate (mainly in intermediate frequencies), classical mechanics provides very good approximation of quantum results. Moreover, in the frequency region of optimal agreement between classical and quantum transmission coefficient, the transmission threshold, i.e. the energy above which the transmission coefficient becomes larger than a specific small threshold value, is found to exhibit a minimum. We also consider the form of the transmitted wave packet and we find that for low values of the frequency the incoming classical and quantum wave packet can be split into a train of well separated coherent pulses, a phenomenon which can admit purely classical kinematic interpretation

Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines

In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated Artificial Neural Networks (ANNs) and Support Vector Regression Machines (SVMs) for each class with even-odd character in Z and N to global model the systematics of nuclei that decay 100% by the beta-minus-mode in their ground states. The arising large-scale lifetime calculations generated by both types of machines are discussed and compared with each other, with the available experimental data, with previous results obtained with neural networks, as well as with estimates coming from traditional global nuclear models. Particular attention is paid on the estimates for exotic and halo nuclei and we focus to those nuclides that are involved in the r-process nucleosynthesis. It is found that statistical models based on LM can at least match or even surpass the predictive performance of the best conventional models of beta-decay systematics and can complement the latter.Comment: 8 pages, 1 fiqure, Proceedings of the 17th HNPS Symposiu

A nonlinear classical model for the decay widths of Isoscalar Giant Monopole Resonances

The decay of the Isoscalar Giant Monopole Resonance (ISGMR) in nuclei is studied by means of a nonlinear classical model consisting of several noninteracting nucleons (particles) moving in a potential well with an oscillating nuclear surface (wall). The motion of the nuclear surface is described by means of a collective variable which appears explicitly in the Hamiltonian as an additional degree of freedom. The total energy of the system is therefore conserved. Although the particles do not directly interact with each other, their motions are indirectly coupled by means of their interaction with the moving nuclear surface. We consider as free parameters in this model the degree of collectivity and the fraction of nucleons that participate to the decay of the collective excitation. Specifically, we have calculated the decay width of the ISGMR in the spherical nuclei $^{208}\rm{Pb}$, $^{144}\rm{Sm}$, $^{116}\rm{Sn}$ and $^{90}\rm{Zr}$. Despite its simplicity and its purely classical nature, the model reproduces the trend of the experimental data which show that with increasing mass number the decay width decreases. Moreover the experimental results (with the exception of $^{90}\rm{Zr}$) can be well fitted using appropriate values for the free parameters mentioned above. It is also found that these values allow for a good description of the experimentally measured $^{112}\rm{Sn}$ and $^{124}\rm{Sn}$ decay widths. In addition, we give a prediction for the decay width of the exotic isotope $^{132}Sn$ for which there is experimental interest. The agreement of our results with the corresponding experimental data for medium-heavy nuclei is dictated by the underlying classical mechanics i.e. the behaviour of the maximum Lyapunov exponent as a function of the system size