Andrew Ernest

Comparative toxicity of oil, dispersant, and oil plus dispersant to several marine species

Dispersants are a preapproved chemical response agent for oil spills off portions of the U.S. coastline, including the Texas-Louisiana coast. However, questions persist regarding potential environmental risks of dispersant applications in nearshore regions (within three nautical miles of the shoreline) that support dense populations of marine organisms and are prone to spills resulting from human activities. To address these questions, a study was conducted to evaluate the relative toxicity of test media prepared with dispersant, weathered crude oil, and weathered crude oil plus dispersant. Two fish species, Cyprinodon variegatus and Menidia beryllina, and one shrimp species, Americamysis bahia (formerly Mysidopsis bahia), were used to evaluate the relative toxicity of the different media under declining and continuous exposure regimes. Microbial toxicity was evaluated using the luminescent bacteria Vibrio fisheri. The data suggested that oil media prepared with a chemical dispersant was equal to or less toxic than the oil-only test medium. Data also indicated that continuous exposures to the test media were generally more toxic than declining exposures. The toxicity of unweathered crude oil with and without dispersant was also evaluated using Menidia beryllina under declining exposure conditions. Unweathered oil-only media were dominated by soluble hydrocarbon fractions and found to be more toxic than weathered oil-only media in which colloidal oil fractions dominated. Total concentrations of petroleum hydrocarbons in oil-plus-dispersant media prepared with weathered and unweathered crude oil were both dominated by colloidal oil and showed no significant difference in toxicity. Analysis of the toxicity data suggests that the observed toxicity was a function of the soluble crude oil components and not the colloidal oil.

Optimal sensor deployment in a large-scale complex drinking water network: Comparisons between a rule-based decision support system and optimization models

Abstract Many models or algorithms have been suggested for sensor placement in the drinking water distribution networks, such as genetic algorithms, multiobjective optimization models, and heuristic methods. Because these models or algorithms have high computational demands, however, the requirement of expensive technical computing software is unavoidable. This study presents a rule-based decision support system (RBDSS) to analyze and generate a set of sensor placement locations and compares the performance against 10 optimization models based on four indexes. Our findings show that the RBDSS demands relatively lower computational time and still exhibits outstanding performance in terms of all our indexes when dealing with a large-scale complex drinking water network.

Comparisons between a rule-based expert system and optimization models for sensor deployment in a small drinking water network

In response to the needs for long-term water quality monitoring, one of the most significant challenges currently facing the water industry is to investigate the sensor placement strategies with modern concepts of and approaches to risk management. Most of the previous research mainly focuses on using optimization models to deal with small-scale drinking water networks. Yet the challenge of NP complete when handling large-scale networks can never be overcome. This study develops a rule-based expert system (RBES) to generate sensor deployment strategies with no computational burden as we oftentimes encountered via various types of optimization analyses. Two rules, including the accessibility and complexity rules, were derived to address the characteristics of effectiveness and efficiency required for sensor deployment in these networks. To retrieve the information of population exposure, the well-calibrated EPANET model was applied for the vulnerability assessment leading to the derivation of the accessibility rule whereas the graph theory was employed to retrieve the complexity rule eliminating the need to deal with temporal variability. Comparisons between this new expert system and 14 existing optimization and heuristic models confirm that the newly developed expert system in this paper can always compete with most of the optimization models. With no computational burden, the RBES that is designed to promote health risk management could also be applicable to deal with similar applications in large-scale drinking water distribution networks.

Building automatic mapping between XML documents using approximate tree matching

The eXtensible Markup Language (XML) is becoming the standard format for data exchange on the Internet, providing interoperability among Web applications. It is important to provide efficient algorithms and tools to manipulate XML documents that are ubiquitous on the Web. In this paper, we present a novel system for automating the transformation of XML documents based on structural mapping with the restriction that the leaf text information are exactly the same in the source and target documents. Firstly, tree edit distance algorithm is used to find the mapping between a pair of source and target documents. With the introduction of tree partition, the efficiency of the tree matching algorithm has been improved significantly. Secondly, template rules for transformation are inferred from the mapping using generalization. Thirdly, a template matching component is used to process new documents. Experimental studies have shown that our methods are very promising and can be widely used for Web document cleaning, information filtering, and other applications.

A rule-based expert system framework for small water systems: proof-of-concept

Abstract Expert systems, when implemented with rigorous content and ease of use, are uniquely valuable to small water systems where personnel will frequently be required to function in multiple capacities with diverse skill requirements. In this study, the potential for using expert systems to supplement the ‘breadth versus depth’ paradigm prevalent in the operations and management of small water utilities is demonstrated. An expert system framework was constructed using a limited, but extensible set of rules for compliance monitoring decision support. Two expert system shells were incorporated to answer both data- and goal-driven questions. A native XML database was used to store the rules and to ensure consistency, extensibility and eliminate duplication between inference mechanisms. The Environmental Protection Agencys Total Coliform Rule was codified into the XML rules database and used to demonstrate ‘proof-of-concept.’

GeoExpert A Framework for Data Quality in Spatial Databases

Usage of very large sets of historical spatial data in knowledge discovery process became a common trend and in order to obtain better results from this knowledge discovery process the data should be of high quality. We proposed a framework for data quality assessment and cleansing tool for spatial data that integrates the spatial data visualization and analysis capabilities of the ARCGIS engine, the reason and inference capability of an expert system. In this paper, we explain the core architecture of the framework and also the functionality of each module in the framework. We explain the implementation details of the framework

An excel based Semantic email system

Semantic Email is a type of Semantic Web application, which deals with the understanding of emails received, and performing corresponding actions according to the schema specified in the Semantic Email system.In this paper, the Semantic Email is implemented with a restriction of using excel sheets as a mode of communication. The current Semantic Email project could be reengineered to support heterogeneous semantic actions based on the action plan. The Semantic Email project can be enhanced providing a web interface, apart from the email system currently used for communication. The clients can directly use the web page, corresponding to the schema rather than sending an email.

XIS: an XML document integration system

We describe XIS, an XML document integration system. The system is based on an algorithm that computes the top-down edit distance between an XML document and a schema. The complexity of the algorithm is t x s x log s, where t is the size of the document and s is the size of the schema.The system includes a GUI that allows the user to visualize the operations performed on the XML document. Synthesized and real data-sets will be used to show the efficiency and efficacy of the system.