Influence of soil and environmental factors on the persistence and phytotoxicity of pendimethalin and flumetralin

Pendimethalin (N-(1-ethylpropyl)-3,4-dimethyl-2,6-dinitrobenzenamine) and flumetralin(N-ethyl-N-(2-chloro-6-fluorobenzyl-2,6-dinitro-α,αα-trifluorop-p-toluidine} are dinitroanilines used for weed and sucker control, respectively, in tobacco. Use of both compounds can cause enhanced injury to crops following tobacco through an interaction. Experiments were conducted to determine if this interaction results from the increased persistence of one or both pesticides. The influence of soil and environmental factors on the persistence of these pesticides and the best model to describe their degradation in soil were evaluated. Pendimethalin and flumetralin, alone or in combination, were applied to four soils and incubated under four environments for five time intervals. A completely randomized design with a factorial arrangement of treatments was used. Soil concentrations of the pesticides were determined by chemical assay using high performance liquid chromatography. Half-life for each pesticide, alone and in combination, was calculated using the first-order degradation model. The influence of soil properties on pesticide persistence was analyzed by linear correlation with half-lives. Temperature effects on the pesticide degradation rates were determined using activation energies. Effects of soil, soil water content, and temperature on residual phytotoxicity to corn were analyzed. Soil concentration data were fit to several degradation models and compared for the best fit of the data. Pendimethalin half-life was shortest in a Decatur clay loam. Flumetralin half-life was shortest in a Dickson silt loam. Flumetralin half-life was longer than the pendimethalin half-life in all soils except the Dickson silt loam. Pendimethalin and flumetralin half-lives, when applied in combination, were not significantly different from half-lives of that pesticide alone, so the interaction is not due to increased persistence. Pesticide half-lives were longer at 15 C than 30 C. No difference in half-lives either pesticide occurred between soil water contents. Soil properties were not highly correlated with persistence. Activation energy was lowest for flumetralin and in the Dickson silt loam soil, indicating possible differences in degradation pathways between pesticides and between soils. Initially, pendimethalin and flumetralin were equally phytotoxic to corn, with differences over time resulting from temperature and soil effects on pesticide persistence. Observed response of the combination treatment, as the percent of the untreated control, was greater than the calculated expected response although the interaction was not significant. Pesticides were phytotoxic longer in a Sequatchie loam than in the Dickson silt loam. The biexponential and quadratic models had higher coefficients of determination (r2) than the first-order model. Little difference was seen between the first-, second, or zero-order models. Higher r2 values were observed under conditions favoring more rapid degradation

Using Aggregation to Reduce Response Time Variability in Cyclic Fair Sequences

This work is an extension of “Generating Cyclic Fair Sequences using Aggregation and Stride Scheduling,” Technical Report 2007-12, Institute for Systems Research, University of Maryland, College Park. http://hdl.handle.net/1903/7082Fair sequences are useful in a variety of manufacturing and computer systems. This paper considers the generation of cyclic fair sequences for a given set of products, each of which must be produced multiple times in each cycle. The objective is to create a sequence so that, for each product, the variability of the time between consecutive completions is minimized. Because minimizing response time variability is known to be NP-hard and the performance of existing heuristics is poor for certain classes of problems, we present an aggregation approach that combines products with the same demand into groups, creates a sequence for those groups, and then disaggregates the sequence into a sequence for each product. Computational experiments show that using aggregation can reduce response time variability dramatically

Generating Cyclic Fair Sequences using Aggregation and Stride Scheduling

Fair sequences are useful in a variety of manufacturing and computer systems. This paper considers the generation of cyclic fair sequences for a given set of products, each of which must be produced multiple times in each cycle. The objective is to create a sequence so that, for each product, the variability of the time between consecutive completions is minimized. Because the problem is known to be NP-hard, we present a heuristic that combines aggregation and parameterized stride scheduling. This novel algorithm combines products with the same demand into groups, creates a sequence for those groups, and then disaggregates the sequence into a sequence for each product

A Framework for Design Theory and Methodology Research

The scholarly study of design continues to develop new knowledge through a variety of approaches. Some researchers examine how designers work, and many develop new methods to help designers do design tasks. Studying design is complex for many reasons. There are many domains in which design occurs, including all of the disciplines of engineering, architecture, and other fields. More significantly, humans design, and human behavior can be difficult to understand. Designers sometimes work alone and sometimes in a group or team. Designers experience design work in multiple ways. Design researchers have been exploring many different aspects of design and experimenting with many different approaches and generating a variety of different design theories. The focus on exploration, however, has meant that there has been less emphasis on exploiting previous research and creating an organized body of knowledge. Building a unified body of knowledge is a long-term challenge. This paper describes a proposed framework for design theory and methodology research. This framework, which is based on ideas from education research, does not specify specific topics or methodologies. Instead, it describes six different research types: (1) Foundational Research, (2) Early-Stage or Exploratory Research, (3) Design and Development Research, (4) Efficacy Research, (5) Effectiveness Research, and (6) Scale-up Research. Illustrating these types are examples based on a table design example. The paper explains how these six research types are related to each other and how, collectively, they serve to generate valid knowledge about design. The research types follow a logical sequence in which researchers develop basic knowledge, create design methods, and test design methods. Although the framework numbers the research types following this natural progression, it does not insist that researchers do or should work by rigidly following this sequence. These research types actually form a cycle of research that iterates through three “phases”: description, explanation, and testing. In this cycle, researchers observe and describe a phenomenon, develop theories to explain the phenomenon and its interactions and effects, and test that theory against the phenomenon, and then, based on the results, refine their descriptions, revise their theories, and conduct more testing. Over time, the description of the phenomenon is improved (e.g., made more precise or more general), better explanations (theories) are found, and additional testing further demonstrates their correctness (or indicates their limitations). The proposed framework can show how different research studies are related to each other because they are the same research type or they fit into the progress of a design theory or the development of a design method. Thus, the proposed framework, while not a theory of design, can help researchers respond to the challenges of coordinating the different types of research needed to create useful design theories and build a unified body of knowledge. Future work is needed to analyze, test, and refine this framework so that it becomes truly useful to the design research community

Data-driven Metareasoning for Collaborative Autonomous Systems

When coordinating their actions to accomplish a mission, the agents in a multi-agent system may use a collaboration algorithm to determine which agent performs which task. This paper describes a novel data-driven metareasoning approach that generates a metareasoning policy that the agents can use whenever they must collaborate to assign tasks. This metareasoning approach collects data about the performance of the algorithms at many decision points and uses this data to train a set of surrogate models that can estimate the expected performance of different algorithms. This yields a metareasoning policy that, based on the current state of the system, estimated the algorithms’ expected performance and chose the best one. For a ship protection scenario, computational results show that one version of the metareasoning policy performed as well as the best component algorithm but required less computational effort. The proposed data-driven metareasoning approach could be a promising tool for developing policies to control multi-agent autonomous systems.This work was supported in part by the U.S. Naval Air Warfare Center-Aircraft Division