947 resultados para spatial clustering algorithms


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We obtained the first data on spatial distribution of a spherical galling insect (Hymenoptera, Eulophidae) at the Caryocar brasiliense Camb. (Caryocaraceae) tree level. This work was developed in two pastures in Montes Claros, Minas Gerais State, Brazil. The areas studied were: pasture 1 (in activity) and pasture 2 (abandoned pasture = savanna in recovery). We evaluated the distribution of spherical galls in: foliage orientation (slope), among leaves (border and interior of the tree crown), among leaflets (right, central, left), distal, median, and proximal as well as border, central area, and adjacent to the mid leaf vein of the leaflet, and difference between areas in 10 infested trees per area. The smaller number of spherical gall/leaflet was observed in pasture 1 than in pasture 2. More spherical galls were found on the northern in pasture 1, but in the pasture 2, the lower spherical galls were observed on the northeast than other slopes. The average number of spherical galls did not differ statistically among the three leaflets of C. brasiliense in pasture 2. However, in pasture 1, we observed highest number of spherical galls in the central leaflet. More spherical galls were found in the border than interior of the tree crown. The average number of spherical galls did not differ statistically among the longitudinal region on leaflet of C. brasiliense. The spherical gall insect preferred to colonize the leaf margin than the central portion or near mid vein on transversal regions on a leaflet.

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Estudio del perfil de la plataforma del Peru a los 9°S. Datos colectados mediante Batfish en zig zag

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Most facility location decision models ignore the fact that for a facility to survive it needs a minimum demand level to cover costs. In this paper we present a decision model for a firm thatwishes to enter a spatial market where there are several competitors already located. This market is such that for each outlet there is a demand threshold level that has to be achievedin order to survive. The firm wishes to know where to locate itsoutlets so as to maximize its market share taking into account the threshold level. It may happen that due to this new entrance, some competitors will not be able to meet the threshold and therefore will disappear. A formulation is presented together with a heuristic solution method and computational experience.

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In this paper we present a model that studies firm mergers in a spatial setting. A new model is formulated that addresses the issue of finding the number of branches that have to be eliminated by a firm after merging with another one, in order to maximize profits. The model is then applied to an example of bank mergers in the city of Barcelona. Finally, a variant of the formulation that introduces competition is presented together with some conclusions.

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PRECON S.A is a manufacturing company dedicated to produce prefabricatedconcrete parts to several industries as rail transportation andagricultural industries.Recently, PRECON signed a contract with RENFE,the Spanish Nnational Rail Transportation Company to manufacturepre-stressed concrete sleepers for siding of the new railways of the highspeed train AVE. The scheduling problem associated with the manufacturingprocess of the sleepers is very complex since it involves severalconstraints and objectives. The constraints are related with productioncapacity, the quantity of available moulds, satisfying demand and otheroperational constraints. The two main objectives are related withmaximizing the usage of the manufacturing resources and minimizing themoulds movements. We developed a deterministic crowding genetic algorithmfor this multiobjective problem. The algorithm has proved to be a powerfuland flexible tool to solve the large-scale instance of this complex realscheduling problem.

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Spatial evaluation of Culicidae (Diptera) larvae from different breeding sites: application of a geospatial method and implications for vector control. This study investigates the spatial distribution of urban Culicidae and informs entomological monitoring of species that use artificial containers as larval habitats. Collections of mosquito larvae were conducted in the São Paulo State municipality of Santa Bárbara d' Oeste between 2004 and 2006 during house-to-house visits. A total of 1,891 samples and nine different species were sampled. Species distribution was assessed using the kriging statistical method by extrapolating municipal administrative divisions. The sampling method followed the norms of the municipal health services of the Ministry of Health and can thus be adopted by public health authorities in disease control and delimitation of risk areas. Moreover, this type of survey and analysis can be employed for entomological surveillance of urban vectors that use artificial containers as larval habitat.

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Choosing what to eat is a complex activity for humans. Determining a food's pleasantness requires us to combine information about what is available at a given time with knowledge of the food's palatability, texture, fat content, and other nutritional information. It has been suggested that humans may have an implicit knowledge of a food's fat content based on its appearance; Toepel et al. (Neuroimage 44:967-974, 2009) reported visual-evoked potential modulations after participants viewed images of high-energy, high-fat food (HF), as compared to viewing low-fat food (LF). In the present study, we investigated whether there are any immediate behavioural consequences of these modulations for human performance. HF, LF, or non-food (NF) images were used to exogenously direct participants' attention to either the left or the right. Next, participants made speeded elevation discrimination responses (up vs. down) to visual targets presented either above or below the midline (and at one of three stimulus onset asynchronies: 150, 300, or 450 ms). Participants responded significantly more rapidly following the presentation of a HF image than following the presentation of either LF or NF images, despite the fact that the identity of the images was entirely task-irrelevant. Similar results were found when comparing response speeds following images of high-carbohydrate (HC) food items to low-carbohydrate (LC) food items. These results support the view that people rapidly process (i.e. within a few hundred milliseconds) the fat/carbohydrate/energy value or, perhaps more generally, the pleasantness of food. Potentially as a result of HF/HC food items being more pleasant and thus having a higher incentive value, it seems as though seeing these foods results in a response readiness, or an overall alerting effect, in the human brain.

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The study of organisms and their resources is critical to further understanding population dynamics in space and time. Although drosophilids have been widely used as biological models, their relationship with breeding and feeding sites has received little attention. Here, we investigate drosophilids breeding in fruits in the Brazilian Savanna, in two contrasting vegetation types, throughout 16 months. Specifically, larval assemblages were compared between savannas and forests, as well as between rainy and dry seasons. The relationships between resource availability and drosophilid abundance and richness were also tested. The community (4,022 drosophilids of 23 species and 2,496 fruits of 57 plant taxa) varied widely in space and time. Drosophilid assemblages experienced a strong bottleneck during the dry season, decreasing to only 0.5% of the abundance of the rainy season. Additionally, savannas displayed lower richness and higher abundance than the forests, and were dominated by exotic species. Both differences in larval assemblages throughout the year and between savannas and gallery forests are consistent with those previously seen in adults. Although the causes of this dynamic are clearly multifactorial, resource availability (richness and abundance of rotten fruits) was a good predictor of the fly assemblage structure.

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The results of the application of the geophysical electromagnetic prospection methods in the resolution of the problems of the spatial location of the travertine quaternary formations of the Banyoles depression are presented

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ABSTRACT The Brazilian Atlantic Forest is one of the world's biodiversity hotspots, and is currently highly fragmented and disturbed due to human activities. Variation in environmental conditions in the Atlantic Forest can influence the distribution of species, which may show associations with some environmental features. Dung beetles (Coleoptera: Scarabaeinae) are insects that act in nutrient cycling via organic matter decomposition and have been used for monitoring environmental changes. The aim of this study is to identify associations between the spatial distribution of dung beetle species and Atlantic Forest structure. The spatial distribution of some dung beetle species was associated with structural forest features. The number of species among the sampling sites ranged widely, and few species were found in all remnant areas. Principal coordinates analysis indicated that species composition, abundance and biomass showed a spatially structured distribution, and these results were corroborated by permutational multivariate analysis of variance. The indicator value index and redundancy analysis showed an association of several dung beetle species with some explanatory environmental variables related to Atlantic Forest structure. This work demonstrated the existence of a spatially structured distribution of dung beetles, with significant associations between several species and forest structure in Atlantic Forest remnants from Southern Brazil.

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Understanding factors that shape ranges of species is central in evolutionary biology. Species distribution models have become important tools to test biogeographical, ecological and evolutionary hypotheses. Moreover, from an ecological and evolutionary perspective, these models help to elucidate the spatial strategies of species at a regional scale. We modelled species distributions of two phylogenetically, geographically and ecologically close Tupinambis species (Teiidae) that occupy the southernmost area of the genus distribution in South America. We hypothesized that similarities between these species might have induced spatial strategies at the species level, such as niche differentiation and divergence of distribution patterns at a regional scale. Using logistic regression and MaxEnt we obtained species distribution models that revealed interspecific differences in habitat requirements, such as environmental temperature, precipitation and altitude. Moreover, the models obtained suggest that although the ecological niches of Tupinambis merianae and T. rufescens are different, these species might co-occur in a large contact zone. We propose that niche plasticity could be the mechanism enabling their co-occurrence. Therefore, the approach used here allowed us to understand the spatial strategies of two Tupinambis lizards at a regional scale.

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Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

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This work proposes an original contribution to the understanding of shermen spatial behavior, based on the behavioral ecology and movement ecology paradigms. Through the analysis of Vessel Monitoring System (VMS) data, we characterized the spatial behavior of Peruvian anchovy shermen at di erent scales: (1) the behavioral modes within shing trips (i.e., searching, shing and cruising); (2) the behavioral patterns among shing trips; (3) the behavioral patterns by shing season conditioned by ecosystem scenarios; and (4) the computation of maps of anchovy presence proxy from the spatial patterns of behavioral mode positions. At the rst scale considered, we compared several Markovian (hidden Markov and semi-Markov models) and discriminative models (random forests, support vector machines and arti cial neural networks) for inferring the behavioral modes associated with VMS tracks. The models were trained under a supervised setting and validated using tracks for which behavioral modes were known (from on-board observers records). Hidden semi-Markov models performed better, and were retained for inferring the behavioral modes on the entire VMS dataset. At the second scale considered, each shing trip was characterized by several features, including the time spent within each behavioral mode. Using a clustering analysis, shing trip patterns were classi ed into groups associated to management zones, eet segments and skippers' personalities. At the third scale considered, we analyzed how ecological conditions shaped shermen behavior. By means of co-inertia analyses, we found signi cant associations between shermen, anchovy and environmental spatial dynamics, and shermen behavioral responses were characterized according to contrasted environmental scenarios. At the fourth scale considered, we investigated whether the spatial behavior of shermen re ected to some extent the spatial distribution of anchovy. Finally, this work provides a wider view of shermen behavior: shermen are not only economic agents, but they are also foragers, constrained by ecosystem variability. To conclude, we discuss how these ndings may be of importance for sheries management, collective behavior analyses and end-to-end models.

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In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.