Dynamic Traffic Grooming Algorithms for Reconfigurable SONET Over WDM Networks

The emergence of wavelength-division multiplexing (WDM) technology provides the capability for increasing the bandwidth of synchronous optical network (SONET) rings by grooming low-speed traffic streams onto different high-speed wavelength channels. Since the cost of SONET add–drop multiplexers (SADM) at each node dominates the total cost of these networks, how to assign the wavelength, groom the traffic, and bypass the traffic through the intermediate nodes has received a lot of attention from researchers recently. Moreover, the traffic pattern of the optical network changes from time to time. How to develop dynamic reconfiguration algorithms for traffic grooming is an important issue. In this paper, two cases (best fit and full fit) for handling reconfigurable SONET over WDM networks are proposed. For each approach, an integer linear programming model and heuristic algorithms (TS-1 and TS-2, based on the tabu search method) are given. The results demonstrate that the TS-1 algorithm can yield better solutions but has a greater running time than the greedy algorithm for the best fit case. For the full fit case, the tabu search heuristic yields competitive results compared with an earlier simulated annealing based method and it is more stable for the dynamic case.

Routing Algorithms for All-Optical Networks with Power Considerations: The Unicast Case

In this paper, we investigate the problem of routing connections in all-optical networks while allowing for degradation of routed signals by different optical components. To overcome the complexity of the problem, we divide it into two parts. First, we solve the pure RWA problem using fixed routes for every connection. Second, power assignment is accomplished by either using the smallest-gain first (SGF) heuristic or using a genetic algorithm. Numerical examples on a wide variety of networks show that (a) the number of connections established without considering the signal attenuation was most of the time greater than that achievable considering attenuation and (b) the genetic solution quality was much better than that of SGF, especially when the conflict graph of the connections generated by the linear solver is denser.

Approximation Algorithms for Survivable Multicommodity Flow Problems with Applications to Network Design

Multicommodity flow (MF) problems have a wide variety of applications in areas such as VLSI circuit design, network design, etc., and are therefore very well studied. The fractional MF problems are polynomial time solvable while integer versions are NP-complete. However, exact algorithms to solve the fractional MF problems have high computational complexity. Therefore approximation algorithms to solve the fractional MF problems have been explored in the literature to reduce their computational complexity. Using these approximation algorithms and the randomized rounding technique, polynomial time approximation algorithms have been explored in the literature. In the design of high-speed networks, such as optical wavelength division multiplexing (WDM) networks, providing survivability carries great significance. Survivability is the ability of the network to recover from failures. It further increases the complexity of network design and presents network designers with more formidable challenges. In this work we formulate the survivable versions of the MF problems. We build approximation algorithms for the survivable multicommodity flow (SMF) problems based on the framework of the approximation algorithms for the MF problems presented in [1] and [2]. We discuss applications of the SMF problems to solve survivable routing in capacitated networks.

Dynamic traffic grooming algorithms for reconfigurable SONET over WDM networks

The emergence of Wavelength Division Multiplexing (WDM) technology provides the capability for increasing the bandwidth of Synchronous Optical Network (SONET) rings by grooming low-speed traffic streams onto different high-speed wavelength channels. Since the cost of SONET add-drop multiplexers (SADM) at each node dominates the total cost of these networks, how to assign the wavelength, groom in the traffic and bypass the traffic through the intermediate nodes has received a lot of attention from researchers recently.

Bandit-Based Algorithms for Budgeted Learning

We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples in the budgeted learning model based on algorithms for the multi-armed bandit problem. All of our approaches outperformed the current state of the art. Furthermore, we present a new means for selecting an example to purchase after the attribute is selected, instead of selecting an example uniformly at random, which is typically done. Our new example selection method improved performance of all the algorithms we tested, both ours and those in the literature.

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Collaborative fuzzy clustering algorithms: some refinements and design guidelines

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

Litterfall and leaf decomposition in forest fragments under different successional phases on the Atlantic Plateau of the state of Sao Paulo, Brazil

Litterfall and litter decomposition are vital processes in tropical forests because they regulate nutrient cycling. Nutrient cycling can be altered by forest fragmentation. The Atlantic Forest is one of the most threatened biomes in the world due to human occupation over the last 500 years. This scenario has resulted in fragments of different size, age and regeneration phase. To investigate differences in litterfall and leaf decomposition between forest successional phases, we compared six forest fragments at three different successional phases and an area of mature forest on the Atlantic Plateau of Sao Paulo, Brazil. We sampled litter monthly from November 2008 to October 2009. We used litterbags to calculate leaf decomposition rate of an exotic species, Tipuana tipu (Fabaceae), over the same period litter sampling was performed. Litterfall was higher in the earliest successional area. This pattern may be related to the structural properties of the forest fragments, especially the higher abundance of pioneer species, which have higher productivity and are typical of early successional areas. However, we have not found significant differences in the decomposition rates between the studied areas, which may be caused by rapid stabilization of the decomposition environment (combined effect of microclimatic conditions and the decomposers activities). This result indicates that the leaf decomposition process have already been restored to levels observed in mature forests after a few decades of regeneration, although litterfall has not been entirely restored. This study emphasizes the importance of secondary forests for restoration of ecosystem processes on a regional scale.

Characterization of depolymerized residues from extremely low acid hydrolysis (ELA) of sugarcane bagasse cellulose: Effects of degree of polymerization, crystallinity and crystallite size on thermal decomposition

Sugarcane bagasse cellulose was subjected to the extremely low acid (ELA) hydrolysis in 0.07% H2SO4 at 190, 210 and 225 degrees C for various times. The cellulose residues from this process were characterized by TGA, XRD, GPC, FIR and SEM. A kinetic study of thermal decomposition of the residues was also carried out, using the ASTM and Kissinger methods. The thermal studies revealed that residues of cellulose hydrolyzed at 190, 210 and 225 degrees C for 80,40 and 8 min have initial decomposition temperature and activation energy for the main decomposition step similar to those of Avicel PH-101. XRD studies confirmed this finding by showing that these cellulose residues are similar to Avicel in crystallinity index and crystallite size in relation to the 110 and 200 planes. FTIR spectra revealed no significant changes in the cellulose chemical structure and analysis of SEM micrographs demonstrated that the particle size of the cellulose residues hydrolyzed at 190 and 210 degrees C were similar to that of Avicel. (C) 2011 Elsevier B.V. All rights reserved.

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

The sigma-isotypic decomposition and the sigma-index of reversible-equivariant systems

This work is concerned with dynamical systems in presence of symmetries and reversing symmetries. We describe a construction process of subspaces that are invariant by linear Gamma-reversible-equivariant mappings, where Gamma is the compact Lie group of all the symmetries and reversing symmetries of such systems. These subspaces are the sigma-isotypic components, first introduced by Lamb and Roberts in (1999) [10] and that correspond to the isotypic components for purely equivariant systems. In addition, by representation theory methods derived from the topological structure of the group Gamma, two algebraic formulae are established for the computation of the sigma-index of a closed subgroup of Gamma. The results obtained here are to be applied to general reversible-equivariant systems, but are of particular interest for the more subtle of the two possible cases, namely the non-self-dual case. Some examples are presented. (C) 2011 Elsevier BM. All rights reserved.

LITTER DEVOLUTION AND DECOMPOSITION IN A PERIODICALLY FLOODED FOREST IN RESTINGA DA MARAMBAIA, RIO DE JANEIRO

Restinga (sandbank) areas are fragile environments, which have been subjected to anthropogenic pressures since the country colonization. So that mitigate actions can be taken, it must be developed studies to better understand the ecological processes in these environments. Thus, this study aims to quantify litter and nutrients devolution and litter decomposition in a periodically flooded forest in 'Restinga da Marambaia', Rio de Janeiro. In the study area 10 conic collectors and 30 litter bags were installed. The annual litter devolution was 7.64 Mg.ha(-1), and September was the highest contribution month. Nitrogen was the element returned to the soil to a higher amount (71.9 kg ha(-1) yr(-1)), followed by potassium (41.1 kg ha(-1) yr(-1)). Litter decomposition rate 0.0015 g g(-1) day(-1) and the half-life were 462 days. Potassium was the element that showed the highest losses in comparison to the others. Cellulose appeared as a major participant in the structure of leaf litter, followed by lignin, the latter being associated with the leathery texture of the leaves in this formation.

The contribution of HGAL/GCET2 in immunohistological algorithms: a comparative study in 424 cases of nodal diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma can be subclassified into at least two molecular subgroups by gene expression profiling: germinal center B-cell like and activated B-cell like diffuse large B-cell lymphoma. Several immunohistological algorithms have been proposed as surrogates to gene expression profiling at the level of protein expression, but their reliability has been an issue of controversy. Furthermore, the proportion of misclassified cases of germinal center B-cell subgroup by immunohistochemistry, in all reported algorithms, is higher compared with germinal center B-cell cases defined by gene expression profiling. We analyzed 424 cases of nodal diffuse large B-cell lymphoma with the panel of markers included in the three previously described algorithms: Hans, Choi, and Tally. To test whether the sensitivity of detecting germinal center B-cell cases could be improved, the germinal center B-cell marker HGAL/GCET2 was also added to all three algorithms. Our results show that the inclusion of HGAL/GCET2 significantly increased the detection of germinal center B-cell cases in all three algorithms (P<0.001). The proportions of germinal center B-cell cases in the original algorithms were 27%, 34%, and 19% for Hans, Choi, and Tally, respectively. In the modified algorithms, with the inclusion of HGAL/GCET2, the frequencies of germinal center B-cell cases were increased to 38%, 48%, and 35%, respectively. Therefore, HGAL/GCET2 protein expression may function as a marker for germinal center B-cell type diffuse large B-cell lymphoma. Consideration should be given to the inclusion of HGAL/GCET2 analysis in algorithms to better predict the cell of origin. These findings bear further validation, from comparison to gene expression profiles and from clinical/therapeutic data. Modern Pathology (2012) 25, 1439-1445; doi: 10.1038/modpathol.2012.119; published online 29 June 2012

Long-term decomposition of sugarcane harvest residues in Sao Paulo state, Brazil

Crop residues returned to the soil are important to preserve fertility and sustainability. This research addressed the long-term decomposition of sugarcane post-harvest residues (trash) under reduced tillage, therefore field renewal was performed with herbicide followed by subsoiling and ratoons were deprived of interrow scarification. The trial was conducted in the northern Sao Paulo State, Brazil during four consecutive crops (2005-2008) where litter bags containing N-15-labeled trash were disposed in the field attempting to simulate two distinct situations: the previous crop trash (PCT) or residues incorporated in the field after tillage, and post-harvest trash (PHT) or the remains of plant-cane harvest. Decomposition rates regarding dry matter (DM), carbon (C), root growth, plant nutrients (N, P, K, Ca, Mg and S), lignin (LIG) cellulose (CEL) and hemicellulose (HCEL) contents were assessed for PCT (2005 ndash;2008) and for PHT (2006-2008). There were significant reductions on DM and C:N ratio due to C losses and root growth within the litter bags over time. The DM from PCT and PHT decreased 96% and 73% after four and three crops, respectively, and the higher nutrients release were found for K, Ca and N. The LIG, CEL and HCEL concentrations in PCT decreased 60%, 29%, 70% after four crops and 47%, 35%, 70% from PHT after three crops, respectively. Trash decomposition was driven mainly by residues biochemical composition, root growth within the trash blanket and the climatic conditions during the crop cycles. (C) 2012 Elsevier Ltd. All rights reserved.