Abstract
Noncommutative features are introduced into a relativistic quantum field theory model of nuclear matter, the quantum hadrodynamics-I nuclear model (QHD-I). It is shown that the nuclear matter equation of state (NMEoS) depends on the fundamental momentum scale, eta, introduced by the phase-space noncommutativity (NC). Although it is found that NC geometry does not affect the nucleon fields up to O(eta(2)), it affects the energy density, the pressure and other derivable quantities of the NMEoS, such as the nucleon effective mass. Under the conditions of saturation of the symmetric NM under consideration, the estimated value for the noncommutative parameter is root eta approximate to 0.12 MeV/c.

Abstract
This paper presents a domain-specific querying language for model-driven spreadsheets. We briefly show the design of the language and present in detail its implementation, from the denormalization of data and translation of our user-friendly query language to a more efficient query, to the execution of the query using Google. To validate our work, we executed an empirical study, comparing QuerySheet with an alternative spreadsheet querying tool, which produced positive results.

Abstract
Currently, we are facing increasingly environmental issues affecting our cities and consequently the health of living beings. A practical example it's the pollution, the excess of different odors and gases. Thus, our project is to create a sensor integrated in a smart grid that can measure gas pollution. This new sensor has the ability of detecting and classification of different odors that reflect the air quality in a given space.

Abstract
Herdade da Contenda (HC), located in Moura municipality, Beja district (Alentejo province) in the south of Portugal (southwestern Iberia Peninsula), is a national hunting area with 5270ha. The development of an integrated system that aims to make the management of the natural and cultural heritage resources will be very useful for an effective management of this area. This integrated system should include the physical characterization of the territory, natural conservation, land use and land management themes, as well the cultural heritage resources. This paper presents a new tool for an integrated environmental management system of the HC, which aims to produce maps under a GIS open source environment (QGIS). The application is composed by a single button which opens a window. The window is composed by twelve menus (File, DRASTIC, Forest Fire Risk, Revised Universal Soil Loss Equation (RUSLE), Bioclimatic Index, Cultural Heritage, Fauna and Flora, Ortofoto, Normalizes Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Land Use Land Cover Cover (LULC) and Help. Several inputs are requires to generate these maps, e.g. DEM, geologic information, soil map, hydraulic conductivity information, LULC map, vulnerability and economic information, NDVI. Six buttons were added to the toolbar which allows to manipulate the information in the map canvas: Zoom in, Zoom out, Pan, Print/Layout and Clear. This integrated and open source GIS environment management system was developed for the HC area, but could be easily adapted to other natural or protected area. Despite the lack of data, the methodology presented fulfills the objectives.

Abstract
The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier) implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

Abstract