Roberto Rodrigues de Souza

Phytoremediation of water contaminated with mercury using Typha domingensis in constructed wetland

The presence of mercury in aquatic environments is a matter of concern by part of the scientific community and public health organizations worldwide due to its persistence and toxicity. The phytoremediation consists in a group of technologies based on the use of natural occurrence or genetically modified plants, in order to reduce, remove, break or immobilize pollutants and working as an alternative to replace conventional effluent treatment methods due to its sustainability - low cost of maintenance and energy. The current study provides information about a pilot scale experiment designed to evaluate the potential of the aquatic macrophyte Typha domingensis in a constructed wetland with subsurface flow for phytoremediation of water contaminated with mercury. The efficiency in the reduction of the heavy metal concentration in wetlands, and the relative metal sorption by the T. domingensis, varied according to the exposure time. The continued rate of the system was 7 times higher than the control line, demonstrating a better performance and reducing 99.6±0.4% of the mercury presents in the water contaminated. When compared to other species, the results showed that the T. domingensis demonstrated a higher mercury accumulation (273.3515±0.7234 mg kg(-1)) when the transfer coefficient was 7750.9864±569.5468 L kg(-1). The results in this present study shows the great potential of the aquatic macrophyte T. domingensis in constructed wetlands for phytoremediation of water contaminated with mercury.

Production and characterization of amylases from Zea mays malt

In this work the α and β-amylase enzymes were obtained from maize (Zea mays) malt and were biochemistry characterized. A germination study to obtain the maize malt with good amylase activity was made. The maize seeds were germinated in laboratory and the enzymatic activity was measured daily. Activity dependence to germination time were fitted to an exponential model (A=A0eµt), which showed that the behaviour of enzymatic activity in the germination process was similar to the growth of the microorganism. Its model could be applied to describe the mechanism of α-amylase production for each maize varieties and others cereals. Maize malt characterization showed that α and β-amylase had optimal pH between 4-6.5, optimal temperature 50 and 90oC, and molecular weight of 67.4 and 47.5kDa, respectively. This work contributed with the advances in biotechnology generating of conditions for application of a new and of low price amylases source.

Partitioning optimization of proteins from Zea mays malt in ATPS PEG 6000/CaCl2

This work aimed to establish the relationship between the compositions and pH of ATPS PEG 6000/CaCl2 and the proteins partition from maize malt and also to simplify the process optimization in ATPS for a statistical model, established by response surface methodology (RSM). Results showed that these were no influence of pH on the phase diagrams and on the composition of tie line length of PEG 6000/CaCl2 ATPS. SRM analyses showed that elevated pH and larger tie line length were the best conditions for recovering of maize malt proteins. The maximum partition coefficient by PEG 6000/CaCl2 ATPS was about 4.2 and was achieved in ATPS in a single purification step. The theoretical maximum partition coefficient was between 4.1-4.3. The process was very suitable for continuous aqueous two-phase purification due to the stability of proteins (e.g. and -amylases) and could increase their content into middle.

Extinction Profiles for the Classification of Remote Sensing Data

With respect to recent advances in remote sensing technologies, the spatial resolution of airborne and spaceborne sensors is getting finer, which enables us to precisely analyze even small objects on the Earth. This fact has made the research area of developing efficient approaches to extract spatial and contextual information highly active. Among the existing approaches, morphological profile and attribute profile (AP) have gained great attention due to their ability to classify remote sensing data. This paper proposes a novel approach that makes it possible to precisely extract spatial and contextual information from remote sensing images. The proposed approach is based on extinction filters, which are used here for the first time in the remote sensing community. Then, the approach is carried out on two well-known high-resolution panchromatic data sets captured over Rome, Italy, and Reykjavik, Iceland. In order to prove the capabilities of the proposed approach, the obtained results are compared with the results from one of the strongest approaches in the literature, i.e., APs, using different points of view such as classification accuracies, simplification rate, and complexity analysis. Results indicate that the proposed approach can significantly outperform its alternative in terms of classification accuracies. In addition, based on our implementation, profiles can be generated in a very short processing time. It should be noted that the proposed approach is fully automatic.

Optimization of drying process of Zea Mays malt to use as alternative source of amylolytics enzymes

This work aimed to study the drying process optimization of maize (Zea Mays) malt for obtaining maize malt, without affecting enzymatic activity of a e b-amylases from maize malt. Results showed that dryer operation must occur in zone at 54°C and 5.18-6 h process time. The maize malt obtained had good enzymatic properties.

Response surface methodology to evaluation the recovery of amylases by hollow fiber membrane

This work aimed to study the pH and the transmembrane pressure effects during the recovery of a and b amylases enzymes from corn malt (Zea mays) by hollow fiber membrane. The optimal condition was obtained for a statistical model, established by response surface methodology (RSM). The response surface analysis showed that the best operation condition for amylolitics enzymes recovery by hollow fiber membrane was 0.05 bar and pH 5.00, while the enzymes were purified about of 26 times.

A comparison between k-Optimum Path Forest and k-Nearest Neighbors supervised classifiers

This paper presents the k-Optimum Path Forest (k-OPF) supervised classifier, which is a natural extension of the OPF classifier. k-OPF is compared to the k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Decision Tree (DT) classifiers, and we see that k-OPF and k-NN have many similarities. This work shows that the k-OPF is equivalent to the k-NN classifier when all training samples are used as prototypes. Simulations comparing the accuracy results, the decision boundaries and the processing time of the classifiers are presented to experimentally validate our hypothesis. Also, we prove that OPF using the max cost function and the NN supervised classifiers have the same theoretical error bounds.

Hyperspectral Data Classification Using Extended Extinction Profiles

This letter proposes a new approach for the spectral-spatial classification of hyperspectral images, which is based on a novel extrema-oriented connected filtering technique, entitled as extended extinction profiles. The proposed approach progressively simplifies the first informative features extracted from hyperspectral data considering different attributes. Then, the classification approach is applied on two well-known hyperspectral data sets, i.e., Pavia University and Indian Pines, and compared with one of the most powerful filtering approaches in the literature, i.e., extended attribute profiles. Results indicate that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images automatically and swiftly. In addition, an array-based node-oriented max-tree representation was carried out to efficiently implement the proposed approach.

A Comparison Between Extinction Filters and Attribute Filters

Attribute filters and extinction filters are connected filters used to simplify greyscale images. The first kind is widely explored in the image processing literature, while the second is not much explored yet. Both kind of filters can be efficiently implemented on the max-tree. In this work, we compare these filters in terms of processing time, simplification of flat zones and reduction of max-tree nodes. We also compare their influence as a pre-processing step before extracting affine regions used in matching and pattern recognition. We perform repeatability tests using extinction filters and attribute filters, set to preserve the same number of extrema, as a pre-processing step before detecting Hessian-Affine and Maximally Stable Extremal Regions (MSER) affine regions. The results indicate that using extinction filters as pre-processing obtain a significantly higher (more than 5% on average) number of correspondences on the repeatability tests than the attribute filters. The results in processing natural images show that preserving 5% of images extrema using extinction filters achieve on average 95% of the number of correspondences compared to applying the affine region detectors directly to the unfiltered images, and the average number of max-tree nodes is reduced by a factor greater than 3. Therefore, we can conclude that extinction filters are better than attribute filters with respect to preserving the number of correspondences found by affine detectors, while simplifying the max-tree structure. The use of extinction filters as a pre-processing step is recommended to accelerate image recognition tasks.

An array-based node-oriented max-tree representation

This paper presents an array-based node-oriented structure for the max-tree representation, which allows direct access and flexible manipulation of its nodes, and is more suitable for OpenMP parallel processing. The proposed structure is based on two arrays called node array (NA), which stores attributes of the nodes, and node index (NI), which indicates the node that each pixel belongs to. We compare it with the pixel-oriented max-tree representation based on a parent array (parent) and an ordering array (S) that allows tree traversals. We show that our max-tree representation requires less memory when the ratio between the number of image pixels and max-tree nodes is greater than 1.6, which is often the case. It is more flexible, and can compute some attributes, such as height and dynamics, with a complexity linear on the number of max-tree nodes instead of the number of image pixels. In our experiments our structure computed the height attribute on average 11.4 faster than the parent/S representation. Also, for a single area-open filter, the sequential implementation of our structure is on average 1.14 times slower and the parallel implementation in a 4-core CPU is 1.2 times faster than the parent/S structure. For an area-open filter followed by the hmax filter, our sequential implementation is 1.34 times faster and our parallel implementation is 2.32 times faster than the parent/S structure.