Abstract
A parametric modelling for interval data is proposed, assuming a multivariate Normal or Skew-Normal distribution for the midpoints and log-ranges of the interval variables. The intrinsic nature of the interval variables leads to special structures of the variance-covariance matrix, which is represented by five different possible configurations. Maximum likelihood estimation for both models under all considered configurations is studied. The proposed modelling is then considered in the context of analysis of variance and multivariate analysis of variance testing. To access the behaviour of the proposed methodology, a simulation study is performed. The results show that, for medium or large sample sizes, tests have good power and their true significance level approaches nominal levels when the constraints assumed for the model are respected; however, for small samples, sizes close to nominal levels cannot be guaranteed. Applications to Chinese meteorological data in three different regions and to credit card usage variables for different card designations, illustrate the proposed methodology.

Abstract
The intermittence of the renewable sources due to its unpredictability increases the instability of the actual grid and energy supply. Besides, in a deregulated and competitive framework, producers and consumers require short-term forecasting tools to derive their bidding strategies to the electricity market. This paper proposes a novel hybrid computational tool, based on a combination of evolutionary particle swarm optimization with an adaptive-network-based fuzzy inference system, for wind power forecasting and electricity prices forecasting in the short-term. The results from two real-world case studies are presented, in order to illustrate the proficiency of the proposed computational tool.

Abstract
Two Fabry-Perot interferometers based on chemical etching in multimode graded index fibers are fabricated and their response to temperature and strain are compared. Chemical etching is applied in the graded index fiber end creating an air cavity. The interferometric cavity is formed when the graded index fiber with the air concavity is spliced to a single-mode fiber. The intrinsic sensors present high sensitivity to strain and low sensitivity to temperature. For the 62.5 mu m core fiber, sensitivities of 6.99 pm/mu epsilon and, 0.95 pm/degrees C were obtained for strain and temperature, respectively. The sensor based in the 50 mu m core fiber, on the other hand, presented sensitivities of 4.06 pm/mu epsilon and -0.84 pm/degrees C for strain and temperature, respectively.

Abstract
This paper addresses a modeling and simulation methodology for analog circuit design with amorphous-GIZO thin-film transistors (TFTs). To reach an effective circuit design flow, with commercially available tools, a TFT model has been first developed with an artificial neural network (ANN). Multilayer perceptron with backpropagation algorithm has been adopted to model the static behavior of the TFT devices, for different aspect ratios. The model was then implemented in Verilog-A, to allow a quick instantiation in circuit. Simulations using Cadence Spectre are performed to validate the model. On a second phase, simulation results of basic analog circuits, with this ANN model, are verified against the actual functional results, namely an adder, subtractor, and current mirror circuit. Results demonstrate not only the ANN model accuracy and compatibility with dc and transient analysis, but also show the a-GIZO TFT capability to perform analog operations. © 2012 IEEE.

Abstract
We introduce the notion of golden tilings, and we prove a one-to-one correspondence between (i) smooth conjugacy classes of Anosov diffeomorphisms, with an invariant measure absolutely continuous with respect to the Lebesgue measure, (ii) affine classes of golden tilings and (iii) solenoid functions. The solenoid functions give a parametrization of the infinite dimensional space consisting of the mathematical objects described in the above equivalences.

Abstract
Spreadsheets are considered to be the most widely used programming language in the world, and reports have shown that 90% of real-world spreadsheets contain errors. In this work, we try to identify spreadsheet smells, a concept adapted from software, which consists of a surface indication that usually corresponds to a deeper problem. Our smells have been integrated in a tool, and were computed for a large spreadsheet repository. Finally, the analysis of the results we obtained led to the refinement of our initial catalog.