Fishery of the Uçá Crab Ucides Cordatus (Linnaeus, 1763) in a Mangrove Area in Cananéia, State of São Paulo, Brazil: Fishery Performance, Exploitation Patterns and Factors Affecting the Catches

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Macroscopic root water uptake distribution using a matric flux potential approach

Hydrological models featuring root water uptake usually do not include compensation mechanisms such that reductions in uptake from dry layers are compensated by an increase in uptake from wetter layers. We developed a physically based root water uptake model with an implicit compensation mechanism. Based on an expression for the matric flux potential (M) as a function of the distance to the root, and assuming a depth-independent value of M at the root surface, uptake per layer is shown to be a function of layer bulk M, root surface M, and a weighting factor that depends on root length density and root radius. Actual transpiration can be calculated from the sum of layer uptake rates. The proposed reduction function (PRF) was built into the SWAP model, and predictions were compared to those made with the Feddes reduction function (FRF). Simulation results were tested against data from Canada (continuous spring wheat [(Triticum aestivum L.]) and Germany (spring wheat, winter barley [Hordeum vulgare L.], sugarbeet [Beta vulgaris L.], winter wheat rotation). For the Canadian data, the root mean square error of prediction (RMSEP) for water content in the upper soil layers was very similar for FRF and PRF; for the deeper layers, RMSEP was smaller for PRF. For the German data, RMSEP was lower for PRF in the upper layers and was similar for both models in the deeper layers. In conclusion, but dependent on the properties of the data sets available for testing,the incorporation of the new reduction function into SWAP was successful, providing new capabilities for simulating compensated root water uptake without increasing the number of input parameters or degrading model performance.

Definitional verbal patterns for semantic relation extraction

In this paper we present a description of the role of definitional verbal patterns for the extraction of semantic relations. Several studies show that semantic relations can be extracted from analytic definitions contained in machine-readable dictionaries (MRDs). In addition, definitions found in specialised texts are a good starting point to search for different types of definitions where other semantic relations occur. The extraction of definitional knowledge from specialised corpora represents another interesting approach for the extraction of semantic relations. Here, we present a descriptive analysis of definitional verbal patterns in Spanish and the first steps towards the development of a system for the automatic extraction of definitional knowledge.

The influence of markets and policy on spatial patterns of non-timber forest product extraction

When villagers extract resources, such as fuelwood, fodder, or medicinal plants from forests, their decisions over where and how much to extract are influenced by market conditions, their particular opportunity costs of time, minimum consumption needs, and access to markets. This paper develops an optimization model of villagers’ extraction behavior that clarifies how, and under what conditions, policies that create incentives such as improved returns to extraction in a buffer zone might be used instead of adversarial enforcement efforts to protect a forest’s pristine ‘‘inner core.’’

Comparison of two extraction methods for evaluation of volatile constituents patterns in commercial whiskeys: Elucidation of the main odour-active compounds

An analytical procedure based on manual dynamic headspace solid-phase microextraction (HS-SPME) method and the conventional extraction method by liquid–liquid extraction (LLE), were compared for their effectiveness in the extraction and quantification of volatile compounds from commercial whiskey samples. Seven extraction solvents covering a wide range of polarities and two SPME fibres coatings, has been evaluated. The highest amounts extracted, were achieved using dichloromethane (CH2Cl2) by LLE method (LLECH2Cl2)(LLECH2Cl2) and using a CAR/PDMS fibre (SPMECAR/PDMS) in HS-SPME. Each method was used to determine the responses of 25 analytes from whiskeys and calibration standards, in order to provide sensitivity comparisons between the two methods. Calibration curves were established in a synthetic whiskey and linear correlation coefficient (r ) were greater than 0.9929 for LLECH2Cl2LLECH2Cl2 and 0.9935 for SPMECAR/PDMS, for all target compounds. Recoveries greater than 80% were achieved. For most compounds, precision (expressed by relative standard deviation, R.S.D.) are very good, with R.S.D. values lower than 14.78% for HS-SPME method and than 19.42% for LLE method. The detection limits ranged from 0.13 to 19.03 μg L−1 for SPME procedure and from 0.50 to 12.48 μg L−1 for LLE. A tentative study to estimate the contribution of a specific compound to the aroma of a whiskey, on the basis of their odour activity values (OAV) was made. Ethyl octanoate followed by isoamyl acetate and isobutyl alcohol, were found the most potent odour-active compounds.

Knowledge Patterns for the Web: extraction, tranformation and reuse

This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.

Automatic extraction of syntactic semantic patterns for multilingual resources

In this paper we present an automatic system for the extraction of syntactic semantic patterns applied to the development of multilingual processing tools. In order to achieve optimum methods for the automatic treatment of more than one language, we propose the use of syntactic semantic patterns. These patterns are formed by a verbal head and the main arguments, and they are aligned among languages. In this paper we present an automatic system for the extraction and alignment of syntactic semantic patterns from two manually annotated corpora, and evaluate the main linguistic problems that we must deal with in the alignment process.

Spatial patterns of soil attributes and components in a mangrove system in Southeast Brazil (Sao Paulo)

Purpose Among environmental factors governing innumerous processes that are active in estuarine environments, those of edaphic character have received special attention in recent studies. With the objectives of determining the spatial patterns of soil attributes and components across different mangrove forest landscapes and obtaining additional information on the cause-effect relationships between these variables and position within the estuary, we analyzed several soil attributes in 31 mangrove soil profiles from the state of So Paulo (Guaruja, Brazil). Materials and methods Soil samples were collected at low tide along two transects within the CrumahA(0) mangrove forest. Samples were analyzed to determine pH, Eh, salinity, and the percentages of sand, silt, clay, total organic carbon (TOC), and total S. Mineralogy of the clay fraction (< 2 mm) was also studied by X-ray diffraction analysis, and partitioning of solid-phase Fe was performed by sequential extraction. Results and discussion The results obtained indicate important differences in soil composition at different depths and landscape positions, causing variations in physicochemical parameters, clay mineralogy, TOC contents, and iron geochemistry. The results also indicate that physicochemical conditions may vary in terms of different local microtopographies. Soil salinity was determined by relative position in relation to flood tide and transition areas with highlands. The proportions of TOC and total S are conditioned by the sedimentation of organic matter derived from vegetation and by the prevailing redox conditions, which clearly favored intense sulfate reduction in the soils (similar to 80% of the total Fe is Fe-pyrite). Particle-size distribution is conditioned by erosive/deposition processes (present and past) and probably by the positioning of ancient and reworked sandy ridges. The existing physicochemical conditions appear to contribute to the synthesis (smectite) and transformation (kaolinite) of clay minerals. Conclusions The results demonstrate that the position of soils in the estuary greatly affects soil attributes. Differences occur even at small scales (meters), indicating that both edaphic (soil classification, soil mineralogy, and soil genesis) and environmental (contamination and carbon stock) studies should take such variability into account.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.

Influence of cephalometric characteristics on the occlusal success rate of Class II malocclusions treated with 2-and 4-premolar extraction protocols

Introduction: The objectives of this investigation were to compare the initial cephalometric characteristics of complete Class II Division 1 malocclusions treated with 2 or 4 premolar extractions and to verify their influence on the occlusal success rate of these treatment protocols. Methods: A sample of 98 records from patients with complete Class II Division 1 malocclusion was divided into 2 groups with the following characteristics: group 1 consisted of 55 patients treated with 2 maxillary first premolar extractions at an initial mean age of 13.07 years; group 2 included 43 patients treated with 4 premolar extractions, with an initial mean age of 12.92 years. Initial and final occlusal statuses were evaluated on dental casts with Grainger`s treatment priority index (TPI), and the initial cephalometric characteristics were obtained from the pretreatment cephalograms. The initial cephalometric characteristics and the initial and final occlusal statuses of the groups were compared with the t test. A multiple regression analysis was used to evaluate the influence of all variables in the final TPI. Results: The 2-premolar extraction protocol provided a statistically smaller TPI and consequently a better occlusal success rate than the 4-premolar extraction protocol. The 4-premolar extraction group had statistically smaller apical base lengths, more vertical facial growth patterns, and greater hard- and soft-tissue convexities at pretreatment than the 2-premolar extraction group. However, the multiple regression analysis showed that only the extraction protocol was significantly associated with the final occlusal status. Conclusions: The initial cephalometric characteristics of the groups did not influence the occlusal success rate of these 2 treatment protocols.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Evaluation of atmospheric deposition and patterns of polycyclic aromatic hydrocarbons in façades of historic monuments of Oporto (Portugal)

Atmospheric pollution by motor vehicles is considered a relevant source of damage to architectural heritage. Thus the aim of this work was to assess the atmospheric depositions and patterns of polycyclic aromatic hydrocarbons (PAHs) in façades of historical monuments. Eighteen PAHs (16 PAHs considered by US EPA as priority pollutants, dibenzo[a,l]pyrene and benzo[j]fluoranthene) were determined in thin black layers collected from façades of two historical monuments: Hospital Santo António and Lapa Church (Oporto, Portugal). Scanning electron microscopy (SEM) was used for morphological and elemental characterisation of thin black layers; PAHs were quantified by microwave-assisted extraction combined with liquid chromatography (MAE-LC). The thickness of thin black layers were 80–110 μm and they contained significant levels of iron, sulfur, calcium and phosphorus. Total concentrations of 18 PAHs ranged from 7.74 to 147.92 ng/g (mean of 45.52 ng/g) in thin black layers of Hospital Santo António, giving a range three times lower than at Lapa Church (5.44– 429.26 ng/g; mean of 110.25 ng/g); four to six rings compounds accounted at both monuments approximately for 80–85% of ΣPAHs. The diagnostic ratios showed that traffic emissions were significant source of PAHs in thin black layers. Composition profiles of PAHs in thin black layers of both monuments were similar to those of ambient air, thus showing that air pollution has a significant impact on the conditions and stone decay of historical building façades. The obtained results confirm that historical monuments in urban areas act as passive repositories for air pollutants present in the surrounding atmosphere.

Patterns of Associational Life in Western Europe, 1800-2000

Thesis submitted for assessment with a view to obtaining the degree of Doctor of Political and Social Science of the European University Institute

Water-contact patterns and risk factors for Schistosoma mansoni infection in a rural village of Northeast Brazil

Schistosomiasis mansoni in the Serrano village, municipality of Cururupu, state of Maranhão, Brazil, is a widely spread disease. The PECE (Program for the Control of Schistosomiasis), undertaken since 1979 has reduced the prevalence of S. mansoni infection and the hepatosplenic form of the disease. Nevertheless piped water is available in 84% of the households, prevalence remains above 20%. In order to identify other risk factors responsible for the persistence of high prevalence levels, a cross-sectional survey was carried out in a systematic sample of 294 people of varying ages. Socioeconomic, environmental and demographic variables, and water contact patterns were investigated. Fecal samples were collected and analyzed by the Kato-Katz technique. Prevalence of S. mansoni infection was 24.1%, higher among males (35.5%) and between 10-19 years of age (36.6%). The risk factors identified in the univariable analysis were water contacts for vegetable extraction (Risk Ratio - RR = 2.92), crossing streams (RR = 2.55), bathing (RR = 2.35), fishing (RR = 2.19), hunting (RR = 2.17), cattle breeding (RR = 2.04), manioc culture (RR = 1.90) and leisure (RR = 1.56). After controlling for confounding variables by proportional hazards model the risks remained higher for males, vegetable extraction, bathing in rivers and water contact in rivers or in periodically inundated parts of riverine woodland (swamplands)