We present a comparison between three approaches to test non-Gaussianity of cosmic microwave background data. The Minkowski functionals, the empirical process method and the skewness of wavelet coefficients are applied to maps generated from non-standard inflationary models and to Gaussian maps with point sources included. We discuss the different power of the pixel, harmonic and wavelet space methods on these simulated almost full-sky data (with Planck like noise). We also suggest a new procedure consisting of a combination of statistics in pixel, harmonic and wavelet space.

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset.

In this paper we present a model-based approach for designing efficient control strategies with the aim of increasing the performance of Heating, Ventilation and Air- Conditioning (HVAC) systems with ice Cold Thermal Energy Storage (ice CTES). The use of TES systems ensures reduced energy costs and energy consumption, increased flexibility of operation, reduced equipment size and pollutant emissions. A simulation environment based on Matlab/Simulink® is developed, where the thermal behaviour of the plant is analysed by a lumped formulation of the conservation equations. In particular, the ice CTES is modelled as a hybrid system, where the water phase transitions (solid-melting-liquid, liquidfreezing- solid) are described by combining continuous and discrete dynamics, thus considering both latent and sensible heat. Three standard control strategies and a model predictive control approach are developed and compared. Extensive simulations confirm that the MPC provides the best control in terms of energy efficiency and cooling load demand satisfaction with respect to standard control strategies

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Manufacturing of Silicon Carbide (SiC) based devices will soon require the accuracy and control typical of the advanced Si based nanoelectronics. As a consequence, the processes development will surely benefit of technology computer aided design (TCAD) tools dedicated to the current and future SiC process technologies. Plasma etching is one of the most critical and difficult process for optimization procedures in the micro/nanofabrication area, since the resultant 2D (e.g. in trenches) or 3D (e.g in holes) profiling is the consequence of the complex interactions between plasma and materials in the device structures. In this contribution we present a simulation tool dedicated to the etching simulation of SiC structures based on the sequential combination of a plasma scale global model and feature scale Kinetic Monte Carlo simulations. As an example of the approach validation procedure the simulations are compared with the characterization analysis of particular real process results.