Determination of roughness coefficient in 3D digital representations of rocks.

The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out through 2D linear measurements (through mechanical profilometer equipment), obtaining a coefficient that may not represent the entire rock surface. Thus, based on the hypothesis that it is possible to quantify the roughness coefficient in rock plugs reconstructed three-dimensionally by the computer vision technique, this research aims to an alternative method to determine the roughness coefficient in rock plugs.

Method for evaluating roughness and valley areas coefficients of surfaces acquired by laser scanner.

The quantitative determination of average roughness parameters, from the determination of height variations of the surface points, is frequently used to estimate the adhesion between an adhesive and the surface of a substrate. However, to determine the interaction between an adhesive and a surface of a heterogeneous material, such as a red ceramic, it is essential to define other roughness parameters. This work proposes a method for determining the roughness of red ceramic blocks from a three-dimensional evaluation, with the objective of estimating the contact area that the ceramic substrate can provide for a cementitious matrix.

Fire association with respiratory disease and COVID-19 complications in the State of Pará, Brazil.

Background: Brazil has faced two simultaneous problems related to respiratory health: forest fires and the high mortality rate due to COVID-19 pandemics. The Amazon rain forest is one of the Brazilian biomes that suffers the most with fires caused by droughts and illegal deforestation. These fires can bring respiratory diseases associated with air pollution, and the State of Pará in Brazil is the most affected.

On the effects of hard and soft equality constraints in the iterative outlier elimination procedure.

Reliability analysis allows for the estimation of a system's probability of detecting and identifying outliers. Failure to identify an outlier can jeopardize the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system.

Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks.

Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion.

Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining.

The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data.

A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning.

Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing.

An invincible memory: what surname analysis tells us about history, health and population medical genetics in the Brazilian Northeast.

Several studies have shown that the Brazilian Northeast is a region with high rates of inbreeding as well as a high incidence of autosomal recessive diseases. The elaboration of public health policies focused on the epidemiological surveillance of congenital anomalies and rare genetic diseases in this region is urgently needed. However, the vast territory, socio-demographic heterogeneity, economic difficulties and low number of professionals with expertise in medical genetics make strategic planning a challenging task.

New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning.

Quality evaluation of a material's surface is performed through roughness analysis of surface samples. Several techniques have been presented to achieve this goal, including geometrical analysis and surface roughness analysis. Geometric analysis allows a visual and subjective evaluation of roughness (a qualitative assessment), whereas computation of the roughness parameters is a quantitative assessment and allows a standardized analysis of the surfaces.

Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search.

Geodetic networks provide accurate three-dimensional control points for mapping activities, geoinformation, and infrastructure works. Accurate computation and adjustment are necessary, as all data collection is vulnerable to outliers. Applying a Least Squares (LS) process can lead to inaccuracy over many points in such conditions.

Spatial analyzes of HLA data in Rio Grande do Sul, south Brazil: genetic structure and possible correlation with autoimmune diseases.

Background: HLA genes are the most polymorphic of the human genome and have distinct allelic frequencies in populations of different geographical regions of the world, serving as genetic markers in ancestry studies. In addition, specific HLA alleles may be associated with various autoimmune and infectious diseases. The bone marrow donor registry in Brazil is the third largest in the world, and it counts with genetic typing of HLA-A, -B, and -DRB1.

Admixture in Latin America: geographic structure, phenotypic diversity and self-perception of ancestry based on 7,342 individuals.

The current genetic makeup of Latin America has been shaped by a history of extensive admixture between Africans, Europeans and Native Americans, a process taking place within the context of extensive geographic and social stratification. We estimated individual ancestry proportions in a sample of 7,342 subjects ascertained in five countries (Brazil, Chile, Colombia, México and Perú). These individuals were also characterized for a range of physical appearance traits and for self-perception of ancestry.

Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso state, Brazil.

Estimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for fields in the Mato Grosso state, Brazil. Using the MCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period.

Spectral pattern classification in lidar data for rock identification in outcrops.

The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D.

Qualitative environmental analysis for industrial districts implantation using geoprocessing techniques.

Industrial districts became important instruments which are used by the Public Power to induce economic decentralization and create new development poles. For this reason the legislative process is an indispensable means to define the priorities in the uses of soil, also to conduct and follow the process of industrial registration. However, the industrial district establishment shall not be analyzed by taking into account just the economic factor, but also the environmental issue of the enterprise.