Abstract
Many Wireless Sensor Network (WSN) applications operate autonomously in unreliable or inaccessible environments, precluding maintenance or human intervention. Redundant deployment schemes are usually considered in this scenario, making the network resilient to failure and environmental changes. Furthermore, sleep-scheduling techniques can also be applied, enabling redundant nodes to turn off their radios, while active nodes perform monitoring services. This paper investigates the behavior of the (m,k)-Gur Game approach. The main goal of the (m,k)-Gur Game is to provide an uniform network coverage for monitoring applications, with autonomic nodes performing a self-regulated choice between sending message to a base station or sleep until the next period. The proposal was evaluated using the OMNeT++ simulator tool under the MiXiM framework. Preliminary results shows that the (m,k)-Gur Game outperforms the traditional GurGame approach in terms of QoS provision and network coverage.

Abstract
The microbial communities of bulk soil of rice paddy fields under an ancient organic agriculture regimen, consisting on an alfalfa-rice rotation system, were characterized. The drained soil of two adjacent paddies at different stages of the rotation was compared before rice seeding and after harvesting. The relationships among the soil microbial, physicochemical, and biochemical parameters were investigated using multivariate analyses. In the first year of rice cropping, aerobic cultivable heterotrophic populations correlated with lineages of presumably aerobic bacteria (e.g., Sphingobacteriales, Sphingomonadales). In the second year of rice cropping, the total C content correlated with presumable anaerobic bacteria (e.g., Anaerolineae). Independently of the year of rice cropping, before rice seeding, proteolytic activity correlated positively with the cultivable aerobic heterotrophic and ammonifier populations, the soil catabolic profile and with presumable aerobes (e.g., Sphingobacteriales, Rhizobiales) and anaerobes (e.g., Bacteroidales, Anaerolineae). After harvesting, strongest correlations were observed between cultivable diazotrophic populations and bacterial groups described as comprising N-2 fixing members (e.g., Chloroflexi-Ellin6529, Betaproteobacteria, Alphaproteobacteria). It was demonstrated that chemical parameters and microbial functions were correlated with variations on the total bacterial community composition and structure occurring during rice cropping. A better understanding of these correlations and of their implications on soil productivity may be valid contributors for sustainable agriculture practices, based on ancient processes.

Abstract

Abstract
The analysis and interpretation of high-resolution computed tomography (HRCT) images of the chest in the presence of interstitial lung disease (ILD) is a time-consuming task which requires experience. In this paper, a computer-aided diagnosis (CAD) scheme is proposed to assist radiologists in the differentiation of lung patterns associated with ILD and healthy lung parenchyma. Regions of interest were described by a set of texture attributes extracted using differential lacunarity (DLac) and classical methods of statistical texture analysis. The proposed strategy to compute DLac allowed a multiscale texture analysis, while maintaining sensitivity to small details. Support Vector Machines were employed to distinguish between lung patterns. Training and model selection were performed over a stratified 10-fold cross-validation (CV). Dimensional reduction was made based on stepwise regression (F-test, p value < 0.01) during CV. An accuracy of 95.8 +/- 2.2% in the differentiation of normal lung pattern from ILD patterns and an overall accuracy of 94.5 +/- 2.1% in a multiclass scenario revealed the potential of the proposed CAD in clinical practice. Experimental results showed that the performance of the CAD was improved by combining multiscale DLac with classical statistical texture analysis.

Abstract
Rheumatic arthritis (RA) is an autoimmune disease that causes irreversible damage to joints and other physiological structures. The Metacarpophalangeal (MCP) joint is one of the first regions to suffer alterations. These alterations are visible with high frequency ultrasound devices, which are used to quantify inflammatory activity in the MCP due to RA. The accurate segmentation of the bone surface and the identification of the MCP capsule region remains a challenge in ultrasound image processing. In this article we aim to make a contribution to this problem by incorporating prior knowledge of the bone and joint regions anatomy into our segmentation algorithm. The log Gabor filter is used for speckle noise reduction and to extract ridge-like structures from the images, while the phase is left unchanged. After thresholding, scores are generated, based on the intensities and areas of the resulting regions, enabling the selection of the structure that best matches the bone. Finally, segmented joint bones are processed to calculate the initial seeds of joint capsule region. Experimental results demonstrate the accuracy of the proposed segmentation algorithm. The mean pixel error between the automatic segmentation and the reference images were 4.4 pixel. The bone regions not segmented were, on average, 5.4%.

Abstract
The aim of this paper is to present a new approach in user modeling process that use learning and cognitive styles and student emotional state to adapt the user interface, learning content and context. The model is based on a constructivist approach, assessing the user knowledge and presenting contents and activities adapted to the emotional characteristics, learning and cognitive styles of the student. The intelligent behavior of such platform depends on the existence of a tentative description of the student - the student model. The contents of this model and emotional state of the student are used by a domain and interaction model to select the most suitable response to student actions.