Adaptive climate change governance for urban resilience

Climate change poses new challenges to cities and new flexible forms of governance are required that are able to take into account the uncertainty and abruptness of changes. The purpose of this paper is to discuss adaptive climate change governance for urban resilience. This paper identifies and reviews three traditions of literature on the idea of transitions and transformations, and assesses to what extent the transitions encompass elements of adaptive governance. This paper uses the open source Urban Transitions Project database to assess how urban experiments take into account principles of adaptive governance. The results show that: the experiments give no explicit information of ecological knowledge; the leadership of cities is primarily from local authorities; and evidence of partnerships and anticipatory or planned adaptation is limited or absent. The analysis shows that neither technological, political nor ecological solutions alone are sufficient to further our understanding of the analytical aspects of transition thinking in urban climate governance. In conclusion, the paper argues that the future research agenda for urban climate governance needs to explore further the links between the three traditions in order to better identify contradictions, complementarities or compatibilities, and what this means in practice for creating and assessing urban experiments.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

A FOSSIL BULGE GLOBULAR CLUSTER REVEALED BY VERY LARGE TELESCOPE MULTI-CONJUGATE ADAPTIVE OPTICS

The globular cluster HP 1 is projected on the bulge, very close to the Galactic center. The Multi-Conjugate Adaptive Optics Demonstrator on the Very Large Telescope allowed us to acquire high-resolution deep images that, combined with first epoch New Technology Telescope data, enabled us to derive accurate proper motions. The cluster and bulge fields` stellar contents were disentangled through this process and produced an unprecedented definition in color-magnitude diagrams of this cluster. The metallicity of [Fe/H] approximate to -1.0 from previous spectroscopic analysis is confirmed, which together with an extended blue horizontal branch imply an age older than the halo average. Orbit reconstruction results suggest that HP 1 is spatially confined within the bulge.

Multi-Conjugate Adaptive Optics VLT imaging of the distant old open cluster FSR 1415

We employ the recently installed near-infrared Multi-Conjugate Adaptive Optics demonstrator (MAD) to determine the basic properties of a newly identified, old and distant, Galactic open cluster (FSR 1415). The MAD facility remarkably approaches the diffraction limit, reaching a resolution of 0.07 arcsec (in K), that is also uniform in a field of similar to 1.8 arcmin in diameter. The MAD facility provides photometry that is 50 per cent complete at K similar to 19. This corresponds to about 2.5 mag below the cluster main-sequence turn-off. This high-quality data set allows us to derive an accurate heliocentric distance of 8.6 kpc, a metallicity close to solar and an age of similar to 2.5 Gyr. On the other hand, the deepness of the data allows us to reconstruct (completeness-corrected) mass functions (MFs) indicating a relatively massive cluster, with a flat core MF. The Very Large Telescope/MAD capabilities will therefore provide fundamental data for identifying/analysing other faint and distant open clusters in the Galaxy III and IV quadrants.

Variety matters: adaptive genetic diversity and parasite load in two mouse opossums from the Brazilian Atlantic forest

The adaptive potential of a species to a changing environment and in disease defence is primarily based on genetic variation. Immune genes, such as genes of the major histocompatibility complex (MHC), may thereby be of particular importance. In marsupials, however, there is very little knowledge about natural levels and functional importance of MHC polymorphism, despite their key role in the mammalian evolution. In a previous study, we discovered remarkable differences in the MHC class II diversity between two species of mouse opossums (Gracilinanus microtarsus, Marmosops incanus) from the Brazilian Atlantic forest, which is one of the most endangered hotspots for biodiversity conservation. Since the main forces in generating MHC diversity are assumed to be pathogens, we investigated in this study gastrointestinal parasite burden and functional associations between the individual MHC constitution and parasite load. We tested two contrasting scenarios, which might explain differences in MHC diversity between species. We predicted that a species with low MHC diversity would either be under relaxed selection pressure by low parasite diversity (`Evolutionary equilibrium` scenario), or there was a recent loss in MHC diversity leading to a lack of resistance alleles and increased parasite burden (`Unbalanced situation` scenario). In both species it became apparent that the MHC class II is functionally important in defence against gastrointestinal helminths, which was shown here for the first time in marsupials. On the population level, parasite diversity did not markedly differ between the two host species. However, we did observe considerable differences in the individual parasite load (parasite prevalence and infection intensity): while M. incanus revealed low MHC DAB diversity and high parasite load, G. microtarsus showed a tenfold higher population wide MHC DAB diversity and lower parasite burden. These results support the second scenario of an unbalanced situation.

MyD88 Signaling Is Required for Efficient Innate and Adaptive Immune Responses to Paracoccidioides brasiliensis Infection

The mechanisms that govern the initial interaction between Paracoccidioides brasiliensis, a primary dimorphic fungal pathogen, and cells of the innate immunity need to be clarified. Our previous studies showed that Toll-like receptor 2 (TLR2) and TLR4 regulate the initial interaction of fungal cells with macrophages and the pattern of adaptive immunity that further develops. The aim of the present investigation was to assess the role of MyD88, an adaptor molecule used by TLRs to activate genes of the inflammatory response in pulmonary paracoccidioidomycosis. Studies were performed with normal and MyD88(-/-) C57BL/6 mice intratracheally infected with P. brasiliensis yeast cells. MyD88(-/-) macrophages displayed impaired interaction with fungal yeast cells and produced low levels of IL-12, MCP-1, and nitric oxide, thus allowing increased fungal growth. Compared with wild-type (WT) mice, MyD88(-/-) mice developed a more severe infection of the lungs and had marked dissemination of fungal cells to the liver and spleen. MyD88(-/-) mice presented low levels of Th1, Th2, and Th17 cytokines, suppressed lymphoproliferation, and impaired influx of inflammatory cells to the lungs, and this group of cells comprised lower numbers of neutrophils, activated macrophages, and T cells. Nonorganized, coalescent granulomas, which contained high numbers of fungal cells, characterized the severe lesions of MyD88(-/-) mice; the lesions replaced extensive areas of several organs. Therefore, MyD88(-/-) mice were unable to control fungal growth and showed a significantly decreased survival time. In conclusion, our findings demonstrate that MyD88 signaling is important in the activation of fungicidal mechanisms and the induction of protective innate and adaptive immune responses against P. brasiliensis.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

GRASP heuristic with path-relinking for the multi-plant capacitated lot sizing problem

This paper addresses the independent multi-plant, multi-period, and multi-item capacitated lot sizing problem where transfers between the plants are allowed. This is an NP-hard combinatorial optimization problem and few solution methods have been proposed to solve it. We develop a GRASP (Greedy Randomized Adaptive Search Procedure) heuristic as well as a path-relinking intensification procedure to find cost-effective solutions for this problem. In addition, the proposed heuristics is used to solve some instances of the capacitated lot sizing problem with parallel machines. The results of the computational tests show that the proposed heuristics outperform other heuristics previously described in the literature. The results are confirmed by statistical tests. (C) 2009 Elsevier B.V. All rights reserved.

Twofold adaptive partition of unity implicits

Partition of Unity Implicits (PUI) has been recently introduced for surface reconstruction from point clouds. In this work, we propose a PUI method that employs a set of well-observed solutions in order to produce geometrically pleasant results without requiring time consuming or mathematically overloaded computations. One feature of our technique is the use of multivariate orthogonal polynomials in the least-squares approximation, which allows the recursive refinement of the local fittings in terms of the degree of the polynomial. However, since the use of high-order approximations based only on the number of available points is not reliable, we introduce the concept of coverage domain. In addition, the method relies on the use of an algebraically defined triangulation to handle two important tasks in PUI: the spatial decomposition and an adaptive polygonization. As the spatial subdivision is based on tetrahedra, the generated mesh may present poorly-shaped triangles that are improved in this work by means a specific vertex displacement technique. Furthermore, we also address sharp features and raw data treatment. A further contribution is based on the PUI locality property that leads to an intuitive scheme for improving or repairing the surface by means of editing local functions.

Optimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms

J.A. Ferreira Neto, E.C. Santos Junior, U. Fra Paleo, D. Miranda Barros, and M.C.O. Moreira. 2011. Optimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms. Cien. Inv. Agr. 38(2): 169-178. The objective of this manuscript is to develop a new procedure to achieve optimal land subdivision using genetic algorithms (GA). The genetic algorithm was tested in the rural settlement of Veredas, located in Minas Gerais, Brazil. This implementation was based on the land aptitude and its productivity index. The sequence of tests in the study was carried out in two areas with eight different agricultural aptitude classes, including one area of 391.88 ha subdivided into 12 lots and another of 404.1763 ha subdivided into 14 lots. The effectiveness of the method was measured using the shunting line standard value of a parceled area lot`s productivity index. To evaluate each parameter, a sequence of 15 calculations was performed to record the best individual fitness average (MMI) found for each parameter variation. The best parameter combination found in testing and used to generate the new parceling with the GA was the following: 320 as the generation number, a population of 40 individuals, 0.8 mutation tax, and a 0.3 renewal tax. The solution generated rather homogeneous lots in terms of productive capacity.

Entropy-Based Approach to Analyze and Classify Mineral Aggregates

This paper presents an automatic method to detect and classify weathered aggregates by assessing changes of colors and textures. The method allows the extraction of aggregate features from images and the automatic classification of them based on surface characteristics. The concept of entropy is used to extract features from digital images. An analysis of the use of this concept is presented and two classification approaches, based on neural networks architectures, are proposed. The classification performance of the proposed approaches is compared to the results obtained by other algorithms (commonly considered for classification purposes). The obtained results confirm that the presented method strongly supports the detection of weathered aggregates.

Inference From Aging Information

For many learning tasks the duration of the data collection can be greater than the time scale for changes of the underlying data distribution. The question we ask is how to include the information that data are aging. Ad hoc methods to achieve this include the use of validity windows that prevent the learning machine from making inferences based on old data. This introduces the problem of how to define the size of validity windows. In this brief, a new adaptive Bayesian inspired algorithm is presented for learning drifting concepts. It uses the analogy of validity windows in an adaptive Bayesian way to incorporate changes in the data distribution over time. We apply a theoretical approach based on information geometry to the classification problem and measure its performance in simulations. The uncertainty about the appropriate size of the memory windows is dealt with in a Bayesian manner by integrating over the distribution of the adaptive window size. Thus, the posterior distribution of the weights may develop algebraic tails. The learning algorithm results from tracking the mean and variance of the posterior distribution of the weights. It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps.