An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

A comparison of two bench-scale anaerobic systems used for the treatment of dairy effluents

The objective of this work was to compare two anaerobic reactor conflgurations, a hybrid upflow anaerobic sludge blanket (UASBh) reactor and an anaerobic sequencing batch reactor with immobilised biomass (ASBBR) treating dairy effluents. The reactors were fed with effluent from the milk pasteurisation process (effluent 1-E1) and later with effluent from the same process combined with the one from the cheese manufacturing (effluent 2-E2). The ASBBR reactor showed average organic matter removal efficiency of 95.2% for E1 and 93.5% for E2, while the hybrid UASB reactor showed removal efficiencies of 90.3% and 80.1% respectively.

Grid job scheduling using Route with Genetic Algorithm support

In 2006 the Route load balancing algorithm was proposed and compared to other techniques aiming at optimizing the process allocation in grid environments. This algorithm schedules tasks of parallel applications considering computer neighborhoods (where the distance is defined by the network latency). Route presents good results for large environments, although there are cases where neighbors do not have an enough computational capacity nor communication system capable of serving the application. In those situations the Route migrates tasks until they stabilize in a grid area with enough resources. This migration may take long time what reduces the overall performance. In order to improve such stabilization time, this paper proposes RouteGA (Route with Genetic Algorithm support) which considers historical information on parallel application behavior and also the computer capacities and load to optimize the scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify the occupation of tasks. Afterwards, such information is used to parameterize a genetic algorithm responsible for optimizing the task allocation. Results confirm that RouteGA outperforms the load balancing carried out by the original Route, which had previously outperformed others scheduling algorithms from literature.

Is the unfolding of the group discussion off-pattern? Improving coordination support in educational forums using mobile devices

A forum is a valuable tool to foster reflection in an in-depth discussion; however, it forces the course mediator to continually pay close attention in order to coordinate learners` activities. Moreover, monitoring a forum is time consuming given that it is impossible to know in advance when new messages are going to be posted. Additionally, a forum may be inactive for a long period and suddenly receive a burst of messages forcing forum mediators to frequently log on in order to know how the discussion is unfolding to intervene whenever it is necessary. Mediators also need to deal with a large amount of messages to identify off-pattern situations. This work presents a piece of action research that investigates how to improve coordination support in a forum using mobile devices for mitigating mediator`s difficulties in following the status of a forum. Based on summarized information extracted from message meta-data, mediators consult visual information summaries on PDAs and receive textual notifications in their mobile phone. This investigation revealed that mediators used the mobile-based coordination support to keep informed on what is taking place within the forum without the need to log on their desktop computer. (C) 2009 Elsevier Ltd. All rights reserved.

SUPPORT OF MAXIMIZING MEASURES FOR TYPICAL C(0) DYNAMICS ON COMPACT MANIFOLDS

Given a compact manifold X, a continuous function g : X -> IR, and a map T : X -> X, we study properties of the T-invariant Borel probability measures that maximize the integral of g. We show that if X is a n-dimensional connected Riemaniann manifold, with n >= 2, then the set of homeomorphisms for which there is a maximizing measure supported on a periodic orbit is meager. We also show that, if X is the circle, then the ""topological size"" of the set of endomorphisms for which there are g maximizing measures with support on a periodic orbit depends on properties of the function g. In particular, if g is C(1), it has interior points.

Methane conversion reactions on Ni catalysts promoted with Rh: Influence of support

Ni catalysts supported on gamma-Al(2)O(3) and Mg(Al)O were prepared with and without Rh as a promoter and tested in the reforming of methane in the presence of excess methane, simulating a model biogas. The effects of adding synthetic air on the methane conversion and the formation of carbon were assessed. The catalysts were characterized by X-ray spectroscopy (EDS), surface area (BET), X-ray diffraction (XRD), Temperature-programmed reduction (TPR), X-ray absorption near-edge structure (XANES) and XPD. The results showed that in catalysts without Rh, the Ni interacts strongly with the supports, showing high reduction temperatures in TPR tests. The addition of Rh increased the amount of reducible Ni and facilitated the reduction of the species interacting strongly with the support. In the catalytic tests, the samples promoted with Rh suffered higher carbon deposition. The in situ XPD suggested that on the support gamma-Al(2)O(3), the presence of Rh probably led to a segregation of Ni species with time on stream, leading to carbon deposition. On the support MgAlO, the presence of Rh improved the dispersion of Ni, by reducing the Ni(0) crystallite size, suggesting that in this case the carbon deposition was due to a favoring of CH(4) decomposition by Rh. (C) 2011 Elsevier B.V. All rights reserved.