No abstract

Phytoremediation is an attractive alternative to conventional treatments of soil due to advantages such as low cost, large application areas, and the possibility of in situ treatment. This study presents the assessment of phytoremediation processes conducted under controlled experimental conditions to evaluate the ability of Ricinus communis L., tropical plant species, to promote the degradation of 15 persistent organic pollutants (POPs), in a 66-day period. The contaminants tested were hexachlorocyclohexane (HCH), DDT, heptachlor, aldrin, and others. Measurements made in rhizosphere soil indicate that the roots of the studied species reduce the concentration of pesticides. Results obtained during this study indicated that the higher the hydrophobicity of the organic compound and its molecular interaction with soil or root matrix the greater its tendency to concentrate in root tissues and the research showed the following trend: HCHs < diclofop-methyl < chlorpyrifos < methoxychlor < heptachlor epoxide < endrin < o,p′-DDE < heptachlor < dieldrin < aldrin < o,p′-DDT < p,p′-DDT by increasing order of log K ow values. The experimental results confirm the importance of vegetation in removing pollutants, obtaining remediation from 25% to 70%, and demonstrated that Ricinus communis L. can be used for the phytoremediation of such compounds.

No abstract

No abstract

Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.