New thermosets obtained by copolymerization of DGEBA with 1,5,7,11-tetraoxaspiro[5,5]undecane catalyzed by lanthanide triflates

DGEBA epoxy resin was cured with different proportions of 1,5,7,11-tetraoxaspiro[5,5]undecane (TOSU) with ytterbium and lanthanum triflates as catalysts. The curing was studied by differential scanning calorimetry (DSC) and Fourier transform infrared in the attenuated-total-reflexion mode (FTIR/ATR).FITR/ATR was used to monitor the competitive reactive processes and to quantify the evolution of the epoxide, orthocarbonate, linear carbonate and other groups formed in the curing process. The T_g of the cured materials decreased when the proportion of TOSU increased. The kinetics were studied by DSC experiments and analyzed with iso-conversional procedures. The systems catalyzed by ytterbium triflate had a higher curing rate and reached a higher degree of cross-linking. The addition of TOSU reduced the degree of overall shrinkage and even led to one material expanding. The flexibility and the degradability of these materials were improved by introducing higher proportions of TOSU.     

Curing of mixtures of epoxy resins and 4‐methyl‐1,3‐dioxolan‐2‐one with several initiators

Diglycidyl ether of bisphenol A or 3,4‐epoxycyclohexylmethyl 3,4‐epoxycyclohexane carboxylate were mixed with different proportions of 4‐methyl‐1,3‐dioxolan‐2‐one and cured using lanthanide triflates as initiators. In order to compare the materials obtained, conventional initiators such as boron trifluoride complexes and N,N‐dimethylaminopyridine were also tested. The curing process was followed by differential scanning calorimetry (DSC) and Fourier transform IR in attenuated total reflectance mode. This technique proved that the carbonate accelerates the curing process because it helps to form the active initiating species, although it was not chemically incorporated into the network and remained entrapped in the material. The DSC kinetic study was also reported. © 2006 Wiley Periodicals Inc. J Appl Polym Sci 102: 2086–2093, 2006     

Acute Meckel's diverticulum in a senior patient

Meckel's diverticulum is the most common congenital abnormality of the intestinal tract, occurring in 2% of autoptic studies. The case of an 85-year-old man referred to the Emergency Surgery Unit for intestinal obstruction and lower gastrointestinal tract bleeding is reported. Surgical exploration revealed a complicated Meckel's diverticulum full of coproliths, immersed in pus and blood. Examination of the resected diverticulum showed necrotic diverticulitis in the absence of ectopic gastric or pancreatic tissues.     

Deterministic design for neural network learning: an approach based on discrepancy

The general problem of reconstructing an unknown function from a finite collection of samples is considered, in case the position of each input vector in the training set is not fixed beforehand but is part of the learning process. In particular, the consistency of the empirical risk minimization (ERM) principle is analyzed, when the points in the input space are generated by employing a purely deterministic algorithm (deterministic learning). When the output generation is not subject to noise, classical number-theoretic results, involving discrepancy and variation, enable the establishment of a sufficient condition for the consistency of the ERM principle. In addition, the adoption of low-discrepancy sequences enables the achievement of a learning rate of O(1/L), with L being the size of the training set. An extension to the noisy case is provided, which shows that the good properties of deterministic learning are preserved, if the level of noise at the output is not high. Simulation results confirm the validity of the proposed approach.     

Local Models for data-driven learning of control policies for complex systems

An approach based on local learning, relying on Nadaraya–Watson models (NWMs), is introduced for the problem of deriving an automatic controller able to exploit data collected during the operation of some complex plant or system by a reference teacher (e.g., a human operator). Such learning approach is particularly useful when the system is too complex to be modeled accurately and/or the task cannot be easily formalized by a cost function, a situation which rules out classic approaches based, e.g., on dynamic programming. Here it is proved that local models are a suitable solution for a real-time employment, since they allow to incorporate new information directly and efficiently without the need of offline training, and new data immediately reflect in improvement of performance. To this purpose, convergence analysis of the method is provided, also considering the case where the reference controller introduces random variations in the training data. Finally, a simulation test, concerning the control of a mechanical system, is provided to showcase the use of local models in an applicative scenario.     

Neural network and regression spline value function approximations for stochastic dynamic programming

Dynamic programming is a multi-stage optimization method that is applicable to many problems in engineering. A statistical perspective of value function approximation in high-dimensional, continuous-state stochastic dynamic programming (SDP) was first presented using orthogonal array (OA) experimental designs and multivariate adaptive regression splines (MARS). Given the popularity of artificial neural networks (ANNs) for high-dimensional modeling in engineering, this paper presents an implementation of ANNs as an alternative to MARS. Comparisons consider the differences in methodological objectives, computational complexity, model accuracy, and numerical SDP solutions. Two applications are presented: a nine-dimensional inventory forecasting problem and an eight-dimensional water reservoir problem. Both OAs and OA-based Latin hypercube experimental designs are explored, and OA space-filling quality is considered.     

Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming

A methodology to design state estimators for a class of nonlinear continuous-time dynamic systems that is based on neural networks and nonlinear programming is proposed. The estimator has the structure of a Luenberger observer with a linear gain and a parameterized (in general, nonlinear) function, whose argument is an innovation term representing the difference between the current measurement and its prediction. The problem of the estimator design consists in finding the values of the gain and of the parameters that guarantee the asymptotic stability of the estimation error. Toward this end, if a neural network is used to take on this function, the parameters (i.e., the neural weights) are chosen, together with the gain, by constraining the derivative of a quadratic Lyapunov function for the estimation error to be negative definite on a given compact set. It is proved that it is sufficient to impose the negative definiteness of such a derivative only on a suitably dense grid of sampling points. The gain is determined by solving a Lyapunov equation. The neural weights are searched for via nonlinear programming by minimizing a cost penalizing grid-point constraints that are not satisfied. Techniques based on low-discrepancy sequences are applied to deal with a small number of sampling points, and, hence, to reduce the computational burden required to optimize the parameters. Numerical results are reported and comparisons with those obtained by the extended Kalman filter are made     

Design, optimization and performance evaluation of a content distribution overlay for streaming

As an evolution of peer-to-peer (p2p) file-sharing applications, overlay-based networks are also adopted to efficiently distribute content with real-time constraints to a wide user population. In addition, they can be utilized to exploit application level strategies to overcome limitations imposed by the underlying network infrastructure, e.g., the lack of multicast support.In this perspective, the paper introduces an overlay Content Distribution Network (CDN) able to sustain the real-time delivery of data streams. To better use resources, and to face the churn affecting users, the control and optimization of the CDN are performed through a model predictive control scheme. Simulations of two use cases are provided to show the effectiveness of the proposed solution. In particular, the stream of multimedia and interactive grid data are considered.     

A note on the Estimation of a Gamma-Variance Process: Learning from a Failure

This paper confirms that, as originally reported in Seneta (Journal of Applied Probability 41:177–187, 2004, p. 183), it is impossible to replicate Madan et al. (European Finance Review 2:135–156, 1998) results using log daily returns on S&P 500 Index from January 1992 to September 1994. This failure leads to a close investigation of the computational problems associated with finding maximum likelihood estimates of the parameters of the popular VG model. Both standard econometric software, such as R, low level programming languages, such as Matlab\(^{\textregistered }\), and non-standard optimization software, such as Ezgrad described in Tucci (Journal of Economic Dynamics and Control 26:1739–1764, 2002), are used. The complexity of the log-likelihood function is studied. It is shown that it looks very complicated, with many local optima, and may be incredibly sensitive to very small changes in the sample used. Adding or removing a single observation may cause huge changes both in the maximum of the log-likelihood function and in the estimated parameter values. An intuitive procedure which works nicely both when implemented in R and in Matlab\(^{\textregistered }\) is presented.     

The Legnaro ion microprobe in low current experiments

The focused ion microprobe of the Laboratori Nazionali di Legnaro has been recently employed in experiments requiring very low beam current. This article will discuss the performance of our apparatus in this application, the drawbacks which showed up and the appropriate solutions. We present IBIC observations of grain boundaries in polycrystalline silicon solar cells and IBIC and IBIL measurements on CVD diamond samples. The modifications introduced in the experimental setup are outlined. We emphasise the problems typical of these measurements and propose the interposition of thin gold foils in the beam line, as a way to handle very different current intensities. We study the effects of these foils on beam quality and their effectiveness in reducing the current. A new designed system for easy interchange of foils and related collimators is described, which will be inserted upstream in the beam line.