Properties of low-dimensional collective variables in the molecular dynamics of biopolymers

The description of the dynamics of a complex, high-dimensional system in terms of a low-dimensional set of collective variables Y can be fruitful if the low dimensional representation satisfies a Langevin equation with drift and diffusion coefficients which depend only on Y. We present a computational scheme to evaluate whether a given collective variable provides a faithful low-dimensional representation of the dynamics of a high-dimensional system. The scheme is based on the framework of finite-difference Langevin-equation, similar to that used for molecular-dynamics simulations. This allows one to calculate the drift and diffusion coefficients in any point of the full-dimensional system. The width of the distribution of drift and diffusion coefficients in an ensemble of microscopic points at the same value of Y indicates to which extent the dynamics of Y is described by a simple Langevin equation. Using a simple protein model we show that collective variables often used to describe biopolymers display a non-negligible width both in the drift and in the diffusion coefficients. We also show that the associated effective force is compatible with the equilibrium free--energy calculated from a microscopic sampling, but results in markedly different dynamical properties

Robust Optimization in Simulation: Taguchi and Response Surface Methodology

Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization of real (non-simulated) systems. We combine Taguchi's view with RSM for simulated systems, and apply the resulting methodology to classic Economic Order Quantity (EOQ) inventory models. Our results demonstrate that in general robust optimization requires order quantities that differ from the classic EOQ.Pareto frontier;bootstrap;Latin hypercube sampling

An observable for vacancy characterization and diffusion in crystals

To locate the position and characterize the dynamics of a vacancy in a crystal, we propose to represent it by the ground state density of a quantum probe quasi-particle for the Hamiltonian associated to the potential energy field generated by the atoms in the sample. In this description, the h^2/2mu coefficient of the kinetic energy term is a tunable parameter controlling the density localization in the regions of relevant minima of the potential energy field. Based on this description, we derive a set of collective variables that we use in rare event simulations to identify some of the vacancy diffusion paths in a 2D crystal. Our simulations reveal, in addition to the simple and expected nearest neighbor hopping path, a collective migration mechanism of the vacancy. This mechanism involves several lattice sites and produces a long range migration of the vacancy. Finally, we also observed a vacancy induced crystal reorientation process

Finite Symmetry of Leptonic Mass Matrices

We search for possible symmetries present in the leptonic mixing data from SU(3) subgroups of order up to 511. Theoretical results based on symmetry are compared with global fits of experimental data in a chi-squared analysis, yielding the following results. There is no longer a group that can produce all the mixing data without a free parameter, but a number of them can accommodate the first or the second column of the mixing matrix. The only group that fits the third column is $\Delta(150)$. It predicts $\sin^22\theta_{13}=0.11$ and $\sin^22\theta_{23}=0.94$, in good agreement with experimental results.Comment: Version to appear in Physical Review

Revelations of Folies through Geometric Transformations

This article presents an activity carried out in a course on representation in a master’s degree in architecture, which aims to train students in the practice and theory of geometric transformation for the production of shapes, using a case study from contemporary architectural design: the Folies of the Parc de la Villette

Natural and man-made terrestrial electromagnetic noise: an outlook

The terrestrial environment is continuously exposed to electromagnetic radiations which set up a «background» electromagnetic noise. Within the Non Ionizing Radiation band (NIR), i.e. for frequencies lower than 300 GHz, this background can have a natural or an artificial origin. Natural origins of electromagnetic radiations are generally atmospheric or cosmic while artificial origins are technological applications, power transmission, communications, etc. This paper briefly describes the natural and man-made electromagnetic noise in the NIR band. Natural noise comes from a large variety of sources involving different physical phenomena and covering a wide range of frequencies and showing various propagation characteristics with an extremely broad range of power levels. Due to technological growth man-made electromagnetic noise is nowadays superimposed on natural noise almost everywhere on Earth. In the last decades man-made noise has increased dramatically over and above the natural noise in residential and business areas. This increase has led some scientists to consider possible negative effects of electromagnetic waves on human life and living systems in general. Accurate measurements of natural and man-made electromagnetic noise are necessary to understand the relative power levels in the different bands and their influence on life

Building lighting energy consumption modelling with hybrid neural-statistic approaches

"In the proposed work we aim at modelling building lighting energy consumption. We compared several classical methods to the latest Artificial Intelligence. modelling technique: Artificial Neural Networks Ensembling (ANNE). Therefore, in this study we show how we built the ANNE and a new hybrid model based on the. statistical-ANNE combination. Experimentation has been carried out over a three. months data set coming from a real office building located in the ENEA ‘Casaccia’. Research Centre. Experimental results show that the proposed hybrid statistical-ANNE approach can get a remarkable improvement with respect to the best classical method(the statistical one).

Symbolic and non-symbolic predictors of number line task in Italian kindergarteners

The number line estimation task (NLE) is often used as a predictor for broader measures of mathematical achievement. In spite of its popularity, it is still not clear whether the task is based on symbolic or non-symbolic numerical competence. In particular, there is only a very limited amount of studies investigating the relationship between NLE performance and symbolic vs. non-symbolic math skills in children who have not yet begun formal schooling. This study investigates the strength of the association between NLE performance and symbolic and non-symbolic tasks in young kindergarteners. Ninety two 5-year-old children completed the NLE task (range 0-100) and a battery of early numerical competence tests including symbolic-lexical tasks, symbolic semantic tasks, and non-symbolic semantic tasks. The relationship between symbolic and non-symbolic early numerical competence and NLE performance was analyzed using a regression model based on the Bayesian Information Criterion (BIC). Results show that only symbolic semantic tasks are significant predictors of NLE performance. These results suggest that symbolic numerical knowledge is involved in number line processing among young children, whilst non-symbolic knowledge is not. This finding brings new data to the debate on the relationship between non-symbolic numeral knowledge and symbolic number processing and supports the evidence of a primary role of symbolic number processing already in young kindergarteners