Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.

Flood Risk Assessment in Coastal Drainage Basins through a Multivariate Analysis within a GIS-Based Model

This paper presents a GIS-based multicriteria flood risk assessment and mapping approach applied to coastal drainage basins where hydrological data are not available. It involves risk to different types of possible processes: coastal inundation (storm surge), river, estuarine and flash flood, either at urban or natural areas, and fords. Based on the causes of these processes, several environmental indicators were taken to build-up the risk assessment. Geoindicators include geological-geomorphologic proprieties of Quaternary sedimentary units, water table, drainage basin morphometry, coastal dynamics, beach morphodynamics and microclimatic characteristics. Bioindicators involve coastal plain and low slope native vegetation categories and two alteration states. Anthropogenic indicators encompass land use categories properties such as: type, occupation density, urban structure type and occupation consolidation degree. The selected indicators were stored within an expert Geoenvironmental Information System developed for the State of Sao Paulo Coastal Zone (SIIGAL), which attributes were mathematically classified through deterministic approaches, in order to estimate natural susceptibilities (Sn), human-induced susceptibilities (Sa), return period of rain events (Ri), potential damages (Dp) and the risk classification (R), according to the equation R=(Sn.Sa.Ri).Dp. Thematic maps were automatically processed within the SIIGAL, in which automata cells (""geoenvironmental management units"") aggregating geological-geomorphologic and land use/native vegetation categories were the units of classification. The method has been applied to the Northern Littoral of the State of Sao Paulo (Brazil) in 32 small drainage basins, demonstrating to be very useful for coastal zone public politics, civil defense programs and flood management.

Phylogenetic relationships and infrageneric classification of Epidendrum subgenus Amphiglottium (Laeliinae, Orchidaceae)

Epidendrum L. is the largest genus of Orchidaceae in the Neotropical region; it has an impressive morphological diversification, which imposes difficulties in delimitation of both infrageneric and interspecific boundaries. In this study, we review infrageneric boundaries within the subgenus Amphiglottium and try to contribute to the understanding of morphological diversification and taxa delimitation within this group. We tested the monophyly of the subgenus Amphiglottium sect. Amphiglottium, expanding previous phylogenetic investigations and reevaluated previous infrageneric classifications proposed. Sequence data from the trnL-trnF region were analyzed with both parsimony and maximum likelihood criteria. AFLP markers were also obtained and analyzed with phylogenetic and principal coordinate analyses. Additionally, we obtained chromosome numbers for representative species within the group. The results strengthen the monophyly of the subgenus Amphiglottium but do not support the current classification system proposed by previous authors. Only section Tuberculata comprises a well-supported monophyletic group, with sections Carinata and Integra not supported. Instead of morphology, biogeographical and ecological patterns are reflected in the phylogenetic signal in this group. This study also confirms the large variability of chromosome numbers for the subgenus Amphiglottium (numbers ranging from 2n = 24 to 2n = 240), suggesting that polyploidy and hybridization are probably important mechanisms of speciation within the group.

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. (C) 2011 Elsevier Ltd. All rights reserved.

Classification of quantum relativistic orientable objects

Extending our previous work `Fields on the Poincare group and quantum description of orientable objects` (Gitman and Shelepin 2009 Eur. Phys. J. C 61 111-39), we consider here a classification of orientable relativistic quantum objects in 3 + 1 dimensions. In such a classification, one uses a maximal set of ten commuting operators (generators of left and right transformations) in the space of functions on the Poincare group. In addition to the usual six quantum numbers related to external symmetries (given by left generators), there appear additional quantum numbers related to internal symmetries (given by right generators). Spectra of internal and external symmetry operators are interrelated, which, however, does not contradict the Coleman-Mandula no-go theorem. We believe that the proposed approach can be useful for the description of elementary spinning particles considered as orientable objects. In particular, it gives a group-theoretical interpretation of some facts of the existing phenomenological classification of spinning particles.

Fluorescence spectroscopy to diagnose hepatic steatosis in a rat model of fatty liver

Steatosis is diagnosed on the basis of the macroscopic aspect of the liver evaluated by the surgeon at the time of organ extraction or by means of a frozen biopsy. In the present study, the applicability of laser-induced fluorescence (LIF) spectroscopy was investigated as a method for the diagnosis of different degrees of steatosis experimentally induced in rats. Rats received a high-lipid diet for different periods of time. The animals were divided into groups according to the degree of induced steatosis diagnosis by histology. The concentration of fat in the liver was correlated with LIF by means of the steatosis fluorescence factor (SFF). The histology classification, according to liver fat concentration was, Severe Steatosis, Moderate Steatosis, Mild Steatosis and Control (no liver steatosis). Fluorescence intensity could be directly correlated with fat content. It was possible to estimate an average of fluorescence intensity variable by means of different confidence intervals (P=95%) for each steatosis group. SFF was significantly higher in the Severe Steatosis group (P < 0.001) compared with the Moderate Steatosis, Mild Steatosis and Control groups. The various degrees of steatosis could be directly correlated with SFF. LIF spectroscopy proved to be a method capable of identifying the degree of hepatic steatosis in this animal model, and has the potential of clinical application for non-invasive evaluation of the degree of steatosis.

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of Sao Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model. (C) 2008 Elsevier B.V. All rights reserved.

Pseudolikelihood equations for Potts MRF model parameter estimation on higher order neighborhood systems

This letter presents pseudolikelihood equations for the estimation of the Potts Markov random field model parameter on higher order neighborhood systems. The derived equation for second-order systems is a significantly reduced version of a recent result found in the literature (from 67 to 22 terms). Also, with the proposed method, a completely original equation for Potts model parameter estimation in third-order systems was obtained. These equations allow the modeling of less restrictive contextual systems for a large number of applications in a computationally feasible way. Experiments with both simulated and real remote sensing images provided good results.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Metabolomic assessment with CE-MS of the nutraceutical effect of Cystoseira spp extracts in an animal model

There is a need of scientific evidence of claimed nutraceutical effects, but also there is a social movement towards the use of natural products and among them algae are seen as rich resources. Within this scenario, the development of methodology for rapid and reliable assessment of markers of efficiency and security of these extracts is necessary. The rat treated with streptozotocin has been proposed as the most appropriate model of systemic oxidative stress for studying antioxidant therapies. Cystoseira is a brown alga containing fucoxanthin and other carothenes whose pressure-assisted extracts were assayed to discover a possible beneficial effect on complications related to diabetes evolution in an acute but short-term model. Urine was selected as the sample and CE-TOF-MS as the analytical technique to obtain the fingerprints in a non-target metabolomic approach. Multivariate data analysis revealed a good clustering of the groups and permitted the putative assignment of compounds statistically significant in the classification. Interestingly a group of compounds associated to lysine glycation and cleavage from proteins was found to be increased in diabetic animals receiving vehicle as compared to control animals receiving vehicle (N6, N6, N6-trimethyl-L-lysine, N-methylnicotinamide, galactosylhydroxylysine, L-carnitine, N6-acetyl-N6-hydroxylysine, fructose-lysine, pipecolic acid, urocanic acid, amino-isobutanoate, formylisoglutamine. Fructoselysine significantly decreased after the treatment changing from a 24% increase to a 19% decrease. CE-MS fingerprinting of urine has provided a group of compounds different to those detected with other techniques and therefore proves the necessity of a cross-platform analysis to obtain a broad view of biological samples.

Prosodic Morphology : Gender in Arabic Perfect Active and Passive 3rd Person Singular Verbs

Prosodic /template Morphology, that "draws heavily on the theoretical apparatus and formalisms of the generative phonology model known as autosegmental phonology" (Katamba, F. 1993: 154), is the best analysis that can handle Arabic morphology. Verbs in Arabic are represented on three independent tiers: root tier, the skeletal tier and the vocalic melody tier (Katamba, F. 1993). Vowel morphemes, which are represented by diacritics, are inserted within the consonant morphemes, which are represented by primary symbols, to form words. The morpheme tier hypothesis paves the way to understand the nonconcatenative Arabic morphology. This paper analyzes gender in perfect active and passive 3rd person singular verbs on the basis of PM. The focus of the analysis shall be drawn heavily on the most common Arabic verbs; triconsonantal verbs, with brief introduction of the less common verbs; quadriconsonantal perfect active and passive masculine and feminine 3rd person singular verbs. I shall, too, cast the light on some vowel changes that some verbs undergo when voice changes.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Gender stereotypes in politics: what changes when a woman becomes the local political leader?

This study documents how the presence of a woman in an executive political role affects the gender stereotype of women in politics. We use Brazilian electoral data and restrict our focus to close mayoral races (using an RDD design) in which the top two candidates are of opposite sexes. Our most important result was a reduction in the number of candidates and votes for female mayoral candidates after a woman is elected, regardless of her eligibility status for reelection. This negative result is linked only to the position of mayor and not to other political positions (councilor, state or federal deputy). In addition, our results may be interpreted as evidence that voters do not use their update on women as local leaders to change their beliefs on women’s ability to run for other political positions. Finally, female mayors do not appear to have a role model effect on younger cohorts of women. We also note that our results are not influenced by differences in mayoral policies (generally and specifically for women), which could influence voters’ gender stereotypes.

An analysis of the perceived service quality of hospitality industry in Rio de Janeiro through the SERVQUAL model: a multiple case study

Brazil is under political and financial crises where the end seems far away. Because of that, researchers argue that the hotel rooms offered by Rio de Janeiro, built to host the Olympic Games 2016, will be difficult to occupy after the event. It is then necessary for the hotels to understand how guests perceive the service quality in order to adapt to this new era. If guests’ perceptions meet or exceed their expectations, they will be satisfied and will probably return. Thus based on the SERVQUAL approach, this paper aims to study the impact of the service dimensions on the guests’ overall satisfaction at hotels of Rio de Janeiro. Two hotels were considered representative of the city in terms of service quality and customers’ profile. Interviews to the hotel managers were performed, and questionnaires to the guests were administered. Among the five SERVQUAL dimensions – Reliability, Tangibles, Responsiveness, Assurance, and Empathy – the Empathy dimension appears to be the only one that affects the guests’ overall satisfaction. The study could also identify that gender, country of residence, home country and family income have an impact on guests’ satisfaction. This study has no intention of generalization, but rather of refining the theory about services and the SERVQUAL model.

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

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