A FEM-experimental approach for the development of a conceptual linear actuator based on tendril's free coiling

Within the vastness of the plant species, certain living systems show tendril structures whose motion is of particular interest for biomimetic engineers. Tendrils sense and coil around suitable grips, and by shortening in length, they erect the remaining plant body. To achieve contraction, tendrils rotate along their main axis and shift from a linear to a double-spring geometry. This phenomenon is denoted as the free-coiling phase. In this work, with the aim of understanding the fundamentals of the mechanics behind the free coiling, a reverse-engineering approach based on the finite element method was firstly applied. The model consisted of an elongated cylinder with suitable material properties, boundary, and loading conditions, in order to reproduce the kinematics of the tendril. The simulation succeeded in mimicking coiling faithfully and was therefore used to validate a tentative linear actuator model based on the plant’s working principle. More in detail, exploiting shape memory alloy materials to obtain large reversible deformations, the main tendril features were implemented into a nickel-titanium spring-based testing model. The results of the experimental tests confirmed the feasibility of the idea in terms of both functioning principles and actual performance. It can be concluded that the final set-up can be used as a base for a prototype design of a new kind of a linear actuator

Calibrated multivariate distributions for improved conditional prediction

The specification of multivariate prediction regions, having coverage probability closed to the target nominal value, is a challenging problem both from the theoretical and the practical point of view. In this paper we define a well-calibrated multivariate predictive distribution giving suitable conditional prediction intervals with the desired overall coverage accuracy. This distribution is the extension in the multivariate setting of a calibrated predictive distribution defined for the univariate case and it is found on the idea of calibrating prediction regions for improving the coverage probability. This solution is asymptotically equivalent to that one based on asymptotic calculations and, whenever its explicit computation is not feasible, an approximation based on a simple bootstrap simulation procedure is readily available. Moreover, we state a simple, simulation-based, procedure for computing the associated improved conditional prediction limits

A note on predictive densities based on composite likelihood methods

Whenever the computation of data distribution is unfeasible or inconvenient, the classical predictive procedures prove not to be useful. These rely, after all, on the conditional distribution of the future random variable, which is also unavailable. This paper considers a notion of composite likelihood for specifying composite predictive distributions, viewed as surrogates for true unknown predictive distribution. In particular, the focus is on the pairwise likelihood obtained as a weighted product of likelihood factors related to bivariate events associated with both the sample data and future observation. The specification of the weights, andmore generally the evaluation of the frequentist properties of alternative pairwise predictive distributions, is performed by considering the mean square prediction error of the associated predictors and the expected Kullback\u2013Liebler loss of the related predictive densities. Finally, simple examples concerning autoregressive models are presented

Adsorption and Diffusion of Light Hydrocarbons in DDR Zeolite

This thesis reports the results of an experimental study aimed at characterizing the transport properties of DDR crystals (a pure silica zeolite analog) by the “zero length column” technique. This material is potentially useful as a size selective molecular sieve adsorbent for separation of CH4 – CO2 in the upgrading of low grade natural gas (or biogas) as well as for the separation of C3H6 - C3H8 for production of polypropylene. In both these applications pure silica zeolites (such as DDR) have important practical advantages over the traditional cationic zeolites since they are hydrophobic and have low catalytic activity. Intracrystalline diffusivities of CH4 in DDR were measured for the single component system and in the presence of an excess of CO2. In contrast to the predictions from recent molecular simulations the experimental data show that the diffusivity of methane is increased (rather than decreased) by the presence of CO2. This is as expected from transition state theory if CH4 and CO2 are competitively adsorbed. In contrast the data for C2H6 (and C2H4) show no significant difference in diffusivity in the presence of CO2, suggesting non-competitive adsorption. This result can be explained if it is assumed that C2 hydrocarbon molecules occupy preferentially the window sites. The equilibrium isotherms provide tentative support for this hypothesis. Some of the samples showed evidence of significant surface resistance to mass transfer (in addition to intracrystalline diffusional resistance). This led to a further development of the mathematical model used to analyze the ZLC response curves and hence to an extension of the ZLC technique to allow the simultaneous measurement of both the surface rate coefficient and the intracrystalline diffusivity. A detailed study of CO2 equilibrium on several different samples of both DDR and silicalite (another pure silica zeolite) was also undertaken in order to determine the effect of surface hydroxyl content

Improved multivariate prediction regions for Markov process models

This paper concerns the specification of multivariate prediction regions which may be useful in time series applications whenever we aim at considering not just one single forecast but a group of consecutive forecasts. We review a general result on improved multivariate prediction and we use it in order to calculate conditional prediction intervals for Markov process models so that the associated coverage probability turns out to be close to the target value. This improved solution is asymptotically superior to the estimative one, which is simpler but it may lead to unreliable predictive conclusions. An application to general autoregressive models is presented, focusing in particular on AR and ARCH models

Milán 1838: 'La aurora en Copacabana' traducida por Pietro Monti

En Italia, a comienzos del siglo XIX, tenemos una idea del teatro de Calderón que, en el plano teórico, contrasta con el modelo todavía dominante de clasicismo. Sin embargo con la afirmación del romanticismo apreciamos una valoración nueva de la libertad expresiva y de los contactos entre las literaturas nacionales, por lo cual surge también un interés por el gran dramaturgo español. Así se registran las traducciones de Biagio Gamboa, Giacinto Battaglia, pero, sobre todo de Pietro Monti, una figura de cura de rural, en un pueblecito cerca de Como, que entre 1838 y 1855 publica las versiones de catorce dramas calderonianos. Examinaremos una, 'La aurora en Copacabana', para dar una idea del tipo de interpretación del texto de Calderón por parte de este estudioso y de los problemas que emergen en el contexto del ambiente cultural milanés en el cual el traductor se encuadra. At the beginning of the nineteenth century, in Italy, the received view of Calderón’s theatrical works saw them in contrast with the prevailing classicism at the theoretical level. However, with the growing establishment of romanticism, there was an exploitation of the freedom of expression and exchange among national literatures. As a consequence, there was a new interest in the great Spanish playwright. We find the translations by Biagio Gamboa, Giacinto Battaglia, and particularly Pietro Monti: a remarkable and well- read parish priest from a small village nearby Como, who published the Italian translations of 14 dramas by Calderón between 1838 and 1855.We consider one of them, 'La aurora en Copacabana', to give an idea of the approach of this translator to Calderón’s text and of the influence of the Milanese milieu in which Monti lived

Finding the largest triangle in a graph in expected quadratic time

Finding the largest triangle in an n-nodes edge-weighted graph belongs to a set of problems all equivalent under subcubic reductions. Namely, a truly subcubic algorithm for any one of them would imply that they are all subcubic. A recent strong conjecture states that none of them can be solved in less than \u398(n3) time, but this negative result does not rule out the possibility of algorithms with average, rather than worst-case, subcubic running time. Indeed, in this work we describe the first truly-subcubic average complexity procedure for this problem for graphs whose edge lengths are uniformly distributed in [0,1]. Our procedure finds the largest triangle in average quadratic time, which is the best possible complexity of any algorithm for this problem. We also give empirical evidence that the quadratic average complexity holds for many other random distributions of the edge lengths. A notable exception is when the lengths are distances between random points in Euclidean space, for which the algorithm takes average cubic time

Algorithmic strategies for finding the best TSP 2-OPT move in average sub-quadratic time

We describe an exact algorithm for finding the best 2-OPT move which, experimentally, was observed to be much faster than the standard quadratic approach. To analyze its average-case complexity, we introduce a family of heuristic procedures and discuss their complexity when applied to a random tour in graphs whose edge costs are either uniform random numbers in [0, 1] or Euclidean distances between random points in the plane. We prove that, for any probability p: (i) there is a heuristic in the family which can find the best move with probability at least p in average-time O(n^3/2) for uniform instances and O(n) for Euclidean instances; (ii) the exact algorithm take lesser time then the above heuristic on all instances on which the heuristic finds the best move. During local search, while the tour becomes less and less random, the speed of our algorithm worsens until it becomes quadratic. We then discuss how to fine tune a successful hybrid approach, made of our algorithm in the beginning followed by the usual quadratic enumeration

Energy-saving optimization method for point-to-point trajectories planned via standard primitives in 1-DoF mechatronic systems

AbstractIn this work, an analytical methodology to minimize the energy expenditure of mechatronic systems performing point-to-point (PTP) trajectories based on well-known motion primitives is developed and validated. Both PTP trajectory profiles commonly used in industrial motor drives and more complex ones are investigated. Focusing on generic 1-DoF mechatronic systems moving a constant inertia load (e.g., elevators, cranes, CNC machines, Cartesian axis) and possibly equipped or retrofitted with regenerative devices, the consumed energy formulation is firstly derived. Then, the analytical optimization considering all the selected PTP trajectory profiles is computed and a generic closed-form solution is determined. Finally, numerical and experimental evaluations are done showing the effectiveness of the theoretical results and proposed methodology. In addition, all the different trajectories are compared with respect to energy consumption