Sensitivity of Eisenia andrei (Annelida, Oligochaeta) to a commercial formulation of abamectin in avoidance tests with artificial substrate and natural soil under tropical conditions

Obtaining ecotoxicological data on pesticides in tropical regions is imperative for performing more realistic risk analysis, and avoidance tests have been proposed as a useful, fast and cost-effective tool. Therefore, the present study aimed to evaluate the avoidance behavior of Eisenia andrei to a formulated product, Vertimec(A (R)) 18 EC (a.i abamectin), in tests performed on a reference tropical artificial soil (TAS), to derive ecotoxicological data on tropical conditions, and a natural soil (NS), simulating crop field conditions. In TAS tests an adaptation of the substrate recommended by OECD and ISO protocols was used, with residues of coconut fiber as a source of organic matter. Concentrations of the pesticide on TAS test ranged from 0 to 7 mg abamectin/kg (dry weight-d.w.). In NS tests, earthworms were exposed to samples of soils sprayed in situ with: 0.9 L of Vertimec(A (R)) 18 EC/ha (RD); twice as much this dosage (2RD); and distilled water (Control), respectively, and to 2RD: control dilutions (12.5, 25, 50, 75%). All tests were performed under 25 +/- A 2A degrees C, to simulate tropical conditions, and a 12hL:12hD photoperiod. The organisms avoided contaminated TAS for an EC50,48h = 3.918 mg/kg soil d.w., LOEC = 1.75 mg/kg soil d.w. and NOEC = 0.85 mg/kg soil d.w. No significant avoidance response occurred for any NS test. Abamectin concentrations in NS were rather lower than EC50, 48h and LOEC determined in TAS tests. The results obtained contribute to overcome a lack of ecotoxicological data on pesticides under tropical conditions, but more tests with different soil invertebrates are needed to improve pesticides risk analysis.

EVALUATION OF THE PERFORMANCE OF A CASTOR-OIL BASED FORMULATION IN LIMITING PESTICIDE RESIDUES IN STRAWBERRY CROPS

A study was made to evaluate the effect of a castor oil-based detergent on strawberry crops treated with different classes of pesticides, namely deltamethrin, folpet, tebuconazole, abamectin and mancozeb, in a controlled environment. Experimental crops of greenhouse strawberries were cultivated in five different ways with control groups using pesticides and castor oil-based detergent. The results showed that the group 2, which was treated with castor oil-based detergent, presented the lowest amount of pesticide residues and the highest quality of fruit produced.

Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis

A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions. The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC(max). The output of GC(max) coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC(max) is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC(max) algorithm runs in linear time with respect to the variable M=|C|+|Z|, where |C| is the image scene size and |Z| is the size of the allowable range, Z, of the associated weight/affinity function. For most implementations, Z is identical to the set of allowable image intensity values, and its size can be treated as small with respect to |C|, meaning that O(M)=O(|C|). In such a situation, GC(max) runs in linear time with respect to the image size |C|. We show that the output of GC(max) constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the a"" (a) norm ayenF (P) ayen(a) of the map F (P) that associates, with every element e from the boundary of an object P, its weight w(e). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ayenF (P) ayen (q) for qa[1,a]. Of these, the best known minimization problem is for the energy ayenF (P) ayen(1), which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm. We notice that a minimization problem for ayenF (P) ayen (q) , qa[1,a), is identical to that for ayenF (P) ayen(1), when the original weight function w is replaced by w (q) . Thus, any algorithm GC(sum) solving the ayenF (P) ayen(1) minimization problem, solves also one for ayenF (P) ayen (q) with qa[1,a), so just two algorithms, GC(sum) and GC(max), are enough to solve all ayenF (P) ayen (q) -minimization problems. We also show that, for any fixed weight assignment, the solutions of the ayenF (P) ayen (q) -minimization problems converge to a solution of the ayenF (P) ayen(a)-minimization problem (ayenF (P) ayen(a)=lim (q -> a)ayenF (P) ayen (q) is not enough to deduce that). An experimental comparison of the performance of GC(max) and GC(sum) algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as the influence of the choice of the seeds on the output.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

GENERALIZED FINITE ELEMENT METHOD ON NONCONVENTIONAL HYBRID-MIXED FORMULATION

The generalized finite element method (GFEM) is applied to a nonconventional hybrid-mixed stress formulation (HMSF) for plane analysis. In the HMSF, three approximation fields are involved: stresses and displacements in the domain and displacement fields on the static boundary. The GFEM-HMSF shape functions are then generated by the product of a partition of unity associated to each field and the polynomials enrichment functions. In principle, the enrichment can be conducted independently over each of the HMSF approximation fields. However, stability and convergence features of the resulting numerical method can be affected mainly by spurious modes generated when enrichment is arbitrarily applied to the displacement fields. With the aim to efficiently explore the enrichment possibilities, an extension to GFEM-HMSF of the conventional Zienkiewicz-Patch-Test is proposed as a necessary condition to ensure numerical stability. Finally, once the extended Patch-Test is satisfied, some numerical analyses focusing on the selective enrichment over distorted meshes formed by bilinear quadrilateral finite elements are presented, thus showing the performance of the GFEM-HMSF combination.

Evaluation of the performance of a castor-oil based formulation in limiting pesticide residues in strawberry crops

A study was made to evaluate the effect of a castor oil-based detergent on strawberry crops treated with different classes of pesticides, namely deltamethrin, folpet, tebuconazole, abamectin and mancozeb, in a controlled environment. Experimental crops of greenhouse strawberries were cultivated in five different ways with control groups using pesticides and castor oil-based detergent. The results showed that the group 2, which was treated with castor oil-based detergent, presented the lowest amount of pesticide residues and the highest quality of fruit produced.