Leaching potential of S-metolachlor in a medium-textured Oxisol soil with bioindicator plants

ABSTRACT Information about the impact of herbicides in the soil based on the growth of bioindicator species is extremely useful in developing crop management strategies. Therefore, this study aims to evaluate the leaching potential of the herbicide S-metolachlor under different natural precipitations in medium-textured Oxisol using bioindicator plants. A completely randomized experimental design was adopted, with four replicates and treatments arranged in a 3 × 8 factorial scheme [three indexes of precipitation occurred in the environment before the collection of the samples (50, 91, and 131 mm) and eight depths in the soil profile (0-0.03; 0.03-0.06; 0.06-0.09; 0.09-0.12; 0.12-0.15; 0.15-0.20; 0.20-0.25; 0.25-0.30 m)]. PVC columns were used, maintaining the original soil integrity during sampling after accumulating the stipulated natural precipitation. Longitudinal sections separated the columns to sow the bioindicator species (cucumber, lettuce, Alexander grass, and sorghum). The phytotoxicity symptoms of bioindicator plants were evaluated, adopting a phytotoxicity visual scale between 0 and 100%, at 5, 7, 9, and 11 days after seeding. The responses of the bioindicator species to the residual effect of the herbicide S-metolachlor were variable and depended on the rainfall level. Generally, in a medium-textured Oxisol, the higher values of concentration of S-metolachlor occurs in depths ranging between 0 and 0.06 m. The maximum leaching depth detected was 0.12-0.15 m with 131 mm of precipitation. Cucumber was the most sensitive species to the presence of S-metolachlor in an Oxisol of medium-texture since it presents symptoms of phytotoxicity at higher depths.

Weed coexistence in carioca bean inoculated

The supply of nitrogen (N) to the carioca bean plant via inoculation with Rhizobium tropici can prevent competition with the weed community by allowing the crop to absorb the nutrient available in the soil. On this basis, this study proposes to examine the period before weed interference (PBI) in the carioca bean plant following inoculation with R. tropici or N topdressing. The experiments were carried out under field conditions during the summer seasons of 2014 and 2015. A randomized-block experimental design with four replicates was adopted, in a 2 × 11 factorial arrangement (common bean plant inoculated or topdressed with N × 11 periods of coexistence with weeds, namely, 0, 7, 14, 21, 28, 35, 42, 49, 56, 63, or 90 days after emergence [DAE]). Nitrogen topdressing increased the crop's tolerance to coexist with weeds from 6 to 14 DAE, compared with inoculation with R. tropici The PBI for the inoculated common bean plant was 24 and 16 DAE in the years 2014 and 2015, respectively. For the N-topdressed plant, the PBI was 30 DAE in both years.

Framework of Experimental Design and Data Mining in Multi-agent Simulation

Multi-agent based simulation (MABS) is an important approach for studying complex systems. The Agent-based model often contains many parameters, these parameters are usually not independent, with differences in their range, and may be subjected to constraints. How to use MABS investigating complex systems effectively is still a challenge. The common tasks of MABS include: summarizing the macroscopic patterns of the system, identifying key factors, establishing a meta-model, and optimization. We proposed a framework of experimental design and data mining for MABS. In the framework, method of experimental design is used to generate experiment points in the parameter space, then generate simulation data, and finally using data mining techniques to analyze data. With this framework, we could explore and analyze complex system iteratively. Using central composite discrepancy (CCD) as measure of uniformity, we designed an algorithm of experimental design in which parameters could meet any constraints. We discussed the relationship between tasks of complex system simulation and data mining, such as using cluster analysis to classify the macro patterns of the system, and using CART, PCA, ICA and other dimensionality reduction methods to identify key factors, using linear regression, stepwise regression, SVM, neural network, etc. to build the meta-model of the system. This framework integrates MABS with experimental design and data mining to provide a reference for complex system exploration and analysis.

The Order & Complexity Toolbox for Aesthetics (OCTA): A systematic approach to study the relations between order, complexity, and aesthetic appreciation

Do individuals prefer stimuli that are ordered or chaotic, simple or complex, or that strike the right balance of order and complexity? Earlier research mainly focused on the separate influence of order and complexity on aesthetic appreciation. When order and complexity were studied in combination, stimulus manipulations were often not parametrically controlled, only rather specific types of order (i.e., balance or symmetry) were studied, and/or the multidimensionality of order and complexity was ignored. Progress has also been limited by the lack of an easy way to create reproducible and expandible stimulus sets, including both order and complexity manipulations. The Order & Complexity Toolbox for Aesthetics (OCTA), a Python toolbox that is also available as a point-and-click Shiny application, aims to fill this gap. OCTA provides researchers with a free and easy way to create multi-element displays varying qualitatively (i.e., different types) and quantitatively (i.e., different levels) in order and complexity, based on regularity and variety along multiple element features (e.g., shape, size, color, orientation). The standard vector-based output is ideal for experiments on the web and the creation of dynamic interfaces and stimuli. OCTA will not only facilitate reproducible stimulus construction and experimental design in research on order, complexity, and aesthetics. In addition, OCTA can be a very useful tool in any type of research using visual stimuli, or even to create digital art. To illustrate OCTA’s potential, we will propose several possible applications and diverse questions that can be addressed using OCTA.