A generalized finite element method with global-local enrichment functions for confined plasticity problems

The main feature of partition of unity methods such as the generalized or extended finite element method is their ability of utilizing a priori knowledge about the solution of a problem in the form of enrichment functions. However, analytical derivation of enrichment functions with good approximation properties is mostly limited to two-dimensional linear problems. This paper presents a procedure to numerically generate proper enrichment functions for three-dimensional problems with confined plasticity where plastic evolution is gradual. This procedure involves the solution of boundary value problems around local regions exhibiting nonlinear behavior and the enrichment of the global solution space with the local solutions through the partition of unity method framework. This approach can produce accurate nonlinear solutions with a reduced computational cost compared to standard finite element methods since computationally intensive nonlinear iterations can be performed on coarse global meshes after the creation of enrichment functions properly describing localized nonlinear behavior. Several three-dimensional nonlinear problems based on the rate-independent J (2) plasticity theory with isotropic hardening are solved using the proposed procedure to demonstrate its robustness, accuracy and computational efficiency.

Single-Nucleotide Polymorphism and Copy Number Variation of the Multidrug Resistance-1 Locus of Plasmodium vivax: Local and Global Patterns

Emerging resistance to chloroquine (CQ) poses a major challenge for Plasmodium vivax malaria control, and nucleotide substitutions and copy number variation in the P. vivax multidrug resistance 1 (pvmdr-1) locus, which encodes a digestive vacuole membrane transporter, may modulate this phenotype. We describe patterns of genetic variation in pvmdr-1 alleles from Acre and Amazonas in northwestern Brazil, and compare then with those reported in other malaria-endemic regions. The pvmdr-1 mutation Y976F, which is associated with CQ resistance in Southeast Asia and Oceania, remains rare in northwestern Brazil (1.8%) and its prevalence mirrors that of CO resistance worldwide. Gene amplification of pvmdr-1, which is associated with mefloquine resistance but increased susceptibility to CO, remains relatively rare in northwestern Brazil (0.9%) and globally (< 4%), but became common (> 10%) in Tak Province, Thailand, possibly because of drug-mediated selection. The global database we have assembled provides a baseline for further studies of genetic variation in pvmdr-1 and drug resistance in P. vivax malaria.

Global interannual trends and amplitude modulations of the sea surface height anomaly from the TOPEX/Jason-1 altimeters

This study uses the global Ocean Topography Experiment (TOPEX)/Jason-1 altimeters` time series to estimate the 13-yr trend in sea surface height anomaly. These trends are estimated at each grid point by two methods: one fits a straight line to the time series and the other is based on the difference between the average height between the two halves of the time series. In both cases the trend shows large regional variability, mostly where the intense western boundary currents turn. The authors hypothesize that the regional variability of the sea surface height trends leads to changes in the local geostrophic transport. This in turn affects the instability-related processes that generate mesoscale eddies and enhances the Rossby wave signals. This hypothesis is verified by estimates of the trend of the amplitude of the filtered sea surface height anomaly that contains the spectral bands associated with Rossby waves and mesoscale eddies. The authors found predominantly positive tendency in the amplitude of Rossby waves and eddies, which suggests that, on average, these events are becoming more energetic. In some regions, the variation in amplitude over 13 yr is comparable to the standard deviation of the data and is statistically significant according to both methods employed in this study. It is plausible that in this case, the energy is transferred from the mean currents to the waves and eddies through barotropic and baroclinic instability processes that are more pronounced in the western boundary current extension regions. If these heat storage patterns and trends are confirmed on longer time series, then it will be justified to argue that the warming trend of the last century provides the energy that amplifies both Rossby waves and mesoscale eddies.

Enhancement of Nematic Order and Global Phase Diagram of a Lattice Model for Coupled Nematic Systems

We use an infinite-range Maier-Saupe model, with two sets of local quadrupolar variables and restricted orientations, to investigate the global phase diagram of a coupled system of two nematic subsystems. The free energy and the equations of state are exactly calculated by standard techniques of statistical mechanics. The nematic-isotropic transition temperature of system A increases with both the interaction energy among mesogens of system B, and the two-subsystem coupling J. This enhancement of the nematic phase is manifested in a global phase diagram in terms of the interaction parameters and the temperature T. We make some comments on the connections of these results with experimental findings for a system of diluted ferroelectric nanoparticles embedded in a nematic liquid-crystalline environment.

Local symmetries in gauge theories in a finite-dimensional setting

It is shown that the correct mathematical implementation of symmetry in the geometric formulation of classical field theory leads naturally beyond the concept of Lie groups and their actions on manifolds, out into the realm of Lie group bundles and, more generally, of Lie groupoids and their actions on fiber bundles. This applies not only to local symmetries, which lie at the heart of gauge theories, but is already true even for global symmetries when one allows for fields that are sections of bundles with (possibly) non-trivial topology or, even when these are topologically trivial, in the absence of a preferred trivialization. (C) 2012 Elsevier B.V. All rights reserved.

Trends, drivers and impacts of changes in swidden cultivation in tropical forest-agriculture frontiers: A global assessment

This meta-analysis of land-cover transformations of the past 10-15 years in tropical forest-agriculture frontiers world-wide shows that swidden agriculture decreases in landscapes with access to local, national and international markets that encourage cattle production and cash cropping, including biofuels. Conservation policies and practices also accelerate changes in swidden by restricting forest clearing and encouraging commercial agriculture. However, swidden remains important in many frontier areas where farmers have unequal or insecure access to investment and market opportunities, or where multi-functionality of land uses has been preserved as a strategy to adapt to current ecological, economic and political circumstances. In some areas swidden remains important simply because intensification is not a viable choice, for example when population densities and/or food market demands are low. The transformation of swidden landscapes into more intensive land uses has generally increased household incomes, but has also led to negative effects on the social and human capital of local communities to varying degrees. From an environmental perspective, the transition from swidden to other land uses often contributes to permanent deforestation, loss of biodiversity, increased weed pressure, declines in soil fertility, and accelerated soil erosion. Our prognosis is that, despite the global trend towards land use intensification, in many areas swidden will remain part of rural landscapes as the safety component of diversified systems, particularly in response to risks and uncertainties associated with more intensive land use systems. (C) 2011 Elsevier Ltd. All rights reserved.

Relationship between global structural parameters and Enzyme Commission hierarchy: Implications for function prediction

In protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies suggesting that function evolves fundamentally by recruitment, i.e., a same protein motif or fold can be used to perform different enzymatic functions and a few specific amino acids (AAs) are actually responsible for enzyme activity. These essential amino acids should belong to active sites and an effective method for predicting function should be able to recognize them. (C) 2012 Elsevier Ltd. All rights reserved.

Too Big to Ignore: Global Partnership on Small-Scale Fisheries Research - the Latin America and Caribbean network

Too Big to Ignore (TBTI; www.toobigtoignore.net) is a research network and knowledge mobilization partnership established to elevate the profile of small-scale fisheries (SSF), to argue against their marginalization in national and international policies, and to develop research and governance capacity to address global fisheries challenges. Network participants and partners are conducting global and comparative analyses, as well as in-depth studies of SSF in the context of local complexity and dynamics, along with a thorough examination of governance challenges, to encourage careful consideration of this sector in local, regional and global policy arenas. Comprising 15 partners and 62 researchers from 27 countries, TBTI conducts activities in five regions of the world. In Latin America and the Caribbean (LAC) region, we are taking a participative approach to investigate and promote stewardship and self-governance in SSF, seeking best practices and success stories that could be replicated elsewhere. As well, the region will focus to promote sustainable livelihoods of coastal communities. Key activities include workshops and stakeholder meetings, facilitation of policy dialogue and networking, as well as assessing local capacity needs and training. Currently, LAC members are putting together publications that examine key issues concerning SSF in the region and best practices, with a first focus on ecosystem stewardship. Other planned deliverables include comparative analysis, a regional profile on the top research issues on SSF, and a synthesis of SSF knowledge in LAC

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.