Abstract
Association rules is the traditional way used to study market basket or transactional data. One drawback of this analysis is the huge number of rules generated. As a complement to association rules, Association Rules Network (ARN), based on Social Network Analysis (SNA) has been proposed by several researchers. In this work we study a real market basket analysis problem, available in a Belgian supermarket, using ARNs. We learn ARNs by considering the relationships between items that appear more often in the consequent of the association rules. Moreover, we propose a more compact variant of ARNs: the Maximal Itemsets Social Network. In order to assess the quality of these structures, we compute SNA based metrics, like weighted degree and utility of community.

Abstract
This paper presents the results of a preliminary study to examine the ability of post-silicon devices for analog processing. It is focused on the latest thin-film transistors (TFTs) with amorphous gallium-indium-zinc oxide (a-GIZO) as active layer. Three circuit configurations are presented: a differential pair and two multiplier topologies. Both triode and saturation regions of operation are included in the analysis, with the devices set to remain in strong accumulation. A neural model, which is developed based on the measured data of the TFTs, is used for the circuit simulations in the Cadence Virtuoso environment. The analog multipliers simulation results are compared against the expected functional results.

Abstract
This paper presents and evaluates an order release decision rule in a job-shop, based on the input-output control concept. The order release mechanism is part of a global decision-making scheme that includes four main decisions: accept/reject orders, define the order's due date, release the accepted jobs and dispatch it on the shop floor. This paper also presents an evaluation of two acceptance rules, four release mechanisms, and two dispatching rules, using four levels of due date tightness. Extensive simulation experiments were performed to compare the different decision rules, using several criteria: mean tardiness, percent tardy, mean absolute deviation, mean queue time in the shop floor and in the system, and machine usage. We conclude that considering the four decisions simultaneously improve the job-shop performance, and planning both the input and the output when deciding to release a job, leads to an improvement of the operational performance measures. Copyright © 2012 Inderscience Enterprises Ltd.

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.