Aspectos epidemiológicos e espaciais de escolares avaliados na Campanha Nacional de Hanseníase em Sobral - Ceará, Brasil

Objetivo: Descrever os aspectos epidemiológicos dos escolares da rede pública de educação, na faixa etária de cinco a quatroze anos, que participaram da Campanha Nacional de Hanseníase, em 2016. Método: Estudo transversal com dados de escolares de cinco a quatorze anos de Sobral, Ceará. Empregou-se o teste de associação Qui-quadrado de Pearson e o diagrama de Voronoi associado a matriz de distância linear e de proximidade. Resultados: Foram incluídos 1.216 escolares, dos quais 18,1% tinham casos de hanseníase na família, 31,7% apresentaram manchas no corpo e destes, 19,4% referiram ter algum caso de hanseníase na família. Dos 386 escolares que relataram mancha no corpo, 41,3% são do sexo masculino e 34,8% residiam na zona urbana. Conclusão: Evidenciou-se prevalência de manchas nos escolares do sexo masculino, residentes na zona urbana, com distância mínima espacial (de até 10 km) dos escolares com manchas para os casos que tiveram hanseníase na família.Palavras-chave: Hanseníase; Serviços de Saúde Escolar; Epidemiologia Descritiva; Estudos transversais; Análise Espacial. Epidemiological and spatial aspects of schoolchildren in National Leprosy Campaign in Sobral – Ceará, BrazilObjectives: Describe the epidemiological aspects of schoolchildren in the public education system, aged five to fourteen years, who participated in the National Leprosy Campaign in 2016. Methods: Cross-sectional study with data from schoolchildren aged five to fourteen years, from Sobral, Ceará. Pearson's Chi-square association test and the Voronoi diagram associated with the linear distance and proximity matrix were used. Results: 1.216 students were included, of which 18.1% had cases of leprosy in the family, 31.7% had cases without leprosy, 19.4% reported some case of leprosy in the family. Of the 386 schoolchildren who relate to the body, 41.3% are male and 34.8% live in the urban area. Conclusion: There was a prevalence of spots in male students living in the urban area, with a minimum spatial distance (up to 10 km) of students with spots for cases with leprosy in the family.Keyworks: Leprosy; School Health Services; Epidemiology Descriptive; Cross-Sectional Studies; Spatial Analysis. Aspectos epidemiológicos y espaciales de escolares en la Campaña Nacional contra la Lepra en Sobral – Ceará, BrasilObjetivos: Describa los aspectos epidemiológicos de los escolares en el sistema de educación pública, de cinco a catorce años, que participaron la Campaña Nacional contra la Lepra en 2016. Métodos: Estudio transversal con datos de escolares de cinco a catorce años, de Sobral, Ceará. Se utilizaron la prueba de asociación Chi-cuadrado de Pearson y el diagrama de Voronoi asociado con distancia lineal y la matriz de proximidad. Resultados: Se incluyeron 1.216 estudiantes, de los cuales 18.1% tenían casos de lepra en la familia, 31.7% tenían casos sin lepra, 19.4% informaron algún caso de lepra en la familia. De los 386 escolares que se relacionan con el cuerpo, 41.3% son hombres y 34.8% viven en el área urbana. Conclusión: Hubo una prevalencia de manchas en estudiantes varones que viven en el área urbana, con una distancia espacial mínima (hasta 10 km) de estudiantes con manchas para casos de lepra en la familia.Palabras-clave: Lepra; Servicios de Salud Escolar; Epidemiología Descriptiva; Estudios Transversales; Análisis Espacial.

Spatio-temporal analysis of flood data from South Carolina

To investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

The method of dendrograms disclosure for evaluation of cluster analysis results in IoT domain

Background: Increasing the amount of information generated as a result of smart city activity leads to the problem of its accumulation and preprocessing. One type of data preprocessing is clustering. The cluster analysis is an objective method of classification. It provides an appropriate choice of further processing methods as well as the visualization and interpretation of the collected data, which are multidimensional objects. The most valuable feature of cluster analysis is the representation of the result by an image of a dendrogram that reflects a particular hierarchy of relationships between the selected clusters and their objects. The aim of the paper is to develop method of 3D visualization of hierarchical clustering for streaming and multidimensional data collected from IoT devices and open databases. Methods: It is suggested that a more detailed interpretation of the dendrogram is made by implementing the hypothesis given above. Testing this hypothesis means a procedure of visualizing and interpreting the result of a cluster analysis. The disclosed dendrogram allows fully usage of association metrics. Since this metric is derived from the calculation of the values of the proximity matrix in accordance with the chosen object pooling strategy, the use of the disclosed dendrogram is quite legitimate. In addition, the procedure for opening the dendrogram is specific and unambiguous. This methods is built on hierarchical clustering algorithm as the simplest and fasters one. The developed algorithm should make it impossible to cross clusters on a plane. It is also necessary to look for the distance not only between objects, but also between clusters, represented as complex geometric figures. It will allow explaining the nature of the clusters Results: The result of the research and verification of the proposed hypothesis is the diclosure of the dendrogram algorithm as the extension of classical methods of cluster analysis. This extension is made by studying and disclosing the resulting image of the dendrogram. The dendrogram visualization thus obtained differs significantly from the classical results. The opening of the dendrogram according to the developed algorithm allows us 3D visualization of the analysis results, as well as calculating the area and perimeter of the obtained clusters. Therefore, using analytical geometry methods, it is quite easy to isolate and calculate the parameters of minimum cluster coverage surfaces and the immediate distances between any objects of one or different clusters, as well as between the objects of a given cluster. This, in turn, is a significant complement to cluster analysis. Conclusion: The disclosed dendrogram retains proportions in distances between objects. On the basis of these characteristics, it is possible to determine the close relationship between the clusters themselves by correlating the values of their quantitative averaged values of the traits. Thus, the opening of the dendrogram allows us to clearly identify the set of clusters, each of which has its own distribution of the range of features values. The quantitative characteristics of clusters on both dendrograms are quite simple. In addition, the mean values of the features of objects in a given cluster can be interpreted as generalized characteristics of this cluster, and the cluster itself can be represented as a single integral object.

Discrete Embedding for Latent Networks

Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.

A data mining method to identify field type in global oil and gas field case study

<p>Based on the locations and types of past oil and gas field, new discoveries can be predicted from the tectonic setting of the world’s oil and gas field. Geoscientists can characterize a field based on the dominant geological event that influenced the structure’s ability to trap and contain oil and gas in recoverable quantities. But in fact multiple factors affected the type of the oil and gas fields. In this paper, a data mining approach was used to integrated factors of field type. The factors are evaluated by the quantified field data. These data included general field data, location, well statistics, cumulative production data, reserves data and reservoir properties data. The method includes four steps. Firstly, a set of attributes are identified to describe the field characteristics. Secondly, the application of principal component analysis and categorical principal components analysis reduced redundant data and noise by representing the main data variances with a few vector components in a transformed coordinate space. Finally, clustering was done based on a proximity matrix between samples. Euclidean distance definitions were tested in order to build a meaningful cluster tree. By applying this method to the world’s oil and gas field data, we concluded that: (1) the world’s fields can be clusfied in six types according to the quantified field data; (2) over 20% of the world’s fields are clustered at top depth between 2000 and 2500 meters. (3)more attributes can be added to this clustering method, and the influence of the attributes can be evaluated.</p>

An Effective Stock Classification Method Via MDS Based on Modified Mutual Information Distance

This paper proposes a multidimensional scaling (MDS) method based on modified mutual information distance (M-MDS) to analyze stock market data. To better describe the relativity of financial data, it is worthwhile to point out that the commonly used proximity matrix in MDS is replaced with modified mutual information distance (M-MI-D) matrix. Refer to M-MI-D, a higher dissimilarity leads to a larger distance. In order to demonstrate the stability and accuracy of M-MDS, logistic time series are used in simulation experiments. In addition, a comparison of this new M-MDS method with classical MDS is given using the stock market data. It is noted that the new M-MDS method shows better stability than that of classical MDS method. Moreover, not only the stocks in the same US stock block, but also the stocks in different blocks have been discussed to illustrate the efficiency of M-MDS method.