Aerobic degradation of hydrocarbon contaminants in anaerobic ground water would be enhanced by oxygenating the water. However, when a contaminated ground water contains high concentrations of reduced iron, competition for oxygen may occur. This study was designed to study this competition. The oxidation of iron and biodegradation of organics was studied in a 12 in X 2.5 in soil column by injecting either oxygen microbubbles or hydrogen peroxide into the soil matrix to provide a source of oxygen. The effluent concentrations of organic and inorganic constituents and the head losses were monitored after injecting oxygen. First, iron oxidation alone was studied. Second, biodegradation of the organic compound was studied. Finally, the simultaneous iron oxidation and biodegradation of organic compound was carried out. The effect of different Fe2+ concentrations in water on the biodegradation of the organic contaminants was evaluated. It was intended to determine whether iron oxidation or biodegradation of organic compound would cause more plugging in soil. The oxidation efficiencies using oxygen microbubbles and peroxide were compared. It was concluded that at high concentrations of Fe2+ in water, iron is rapidly oxidized utilizing most of the available oxygen. At low Fe2+ concentrations, biodegradation efficiency was high. Oxygen microbubbles were found to be slightly more effective than hydrogen peroxide in transferring oxygen to ground water and oxidizing iron or biodegrading organic contaminants. Soil plugging was found to occur regardless of the method of oxygen delivery. The use of coarse media removal system seemed to solve the problem. / Master of Science

Combinatorial control of transcription is a well established phenomenon in the cell. Multiple transcription factors often bind to the same transcriptional control region of a gene and interact with each other to control the expression of the gene. It is thus necessary to consider the joint conservation of sequence pairs in order to identify combinations of binding sites to which the transcription factors bind. Conventional motif finding algorithms fail to address this issue. We propose a novel biclustering algorithm based on random sampling to identify candidate binding site combinations. We establish bounds on the various parameters to the algorithm and study the conditions under which the algorithm is guaranteed to identify candidate binding sites. We analyzed a yeast cell cycle gene expression data set using our algorithm and recovered certain novel combinations of binding sites, besides those already reported in the literature. / Master of Science

A detailed statistical study was performed on molluscan fossil assemblages collected from the Pliocene Morgarts Beach Member of the Yorktown Formation, located in Isle of Wight County, southeast Virginia. The principal objectives of this study were to examine potential sampling problems and biases inherent in paleontological research. This has been accomplished by statistically testing for the homogeneity, or lack thereof, in species assemblages obtained from samples collected from three localities within the Morgarts Beach Member. Multivariate analysis of variance, two-way analysis of variance, multiple comparison tests and cluster analysis were performed on data collected from a five horizon by five section sampling grid (18 feet [5.5 meters] high, 21 feet [6.4 meters] long) located at Morgarts Beach, Virginia. The results of this analysis demonstrate that the relative abundances of species present in the five horizons are significantly different, whereas the relative abundances of species present in the five sections are not significantly different. Results from cluster analysis show that species assemblages contained in samples collected from the Morgarts Beach Member located at Rushmere are substantially different from the Morgarts Beach type area assemblages, in terms of relative abundances of species. The reason for the lack of faunal similarity relates to the documented facies change between the two localities. The results demonstrate that there is no reliable method to obtain accurate census data (frequency abundance curves) from biostratigraphic or lithostratigraphic units deposited during anything but a restricted time interval. In addition, replicate sampling was found to be unnecessary when attempting to determine the relative abundances of species contained in closely spaced sections within the Morgarts Beach Member. Species accumulation curves were constructed from the data collected from the Morgarts Beach Member. Examination of these curves demonstrate that many rare species will not be found unless extensive collecting is undertaken. / Master of Science

Street trees are a subpopulation of the urban forest resource and exist in the rights-of-way adjacent to public roads in a municipality. Benefit-cost analyses have shown that the annual benefits provided by the average street tree far outweigh the costs of planting and maintenance. City and municipal foresters spend a majority of their time and resources managing street tree populations. Sample street tree inventories are a common method of estimating municipal street tree populations for the purposes of making urban forest policy, planning, and management decisions. i-Tree Streets is a suite of software tools capable of producing estimates of street tree abundance and value from a sample of street trees taken along randomly selected sections (segments) of public streets. During sample street tree inventories conducted by Virginia Tech Urban Forestry, it was observed that the lengths of the sample streets recommended by i-Tree varied greatly within most municipalities leading to concern about the impact of street length variation on sampling precision. This project was conducted to improve i-Tree Streets by changing the recommended sampling protocol without altering the software. Complete street tree censuses were obtained from 7 localities and standardized using GIS. The effects of standardizing street segments to 3 different lengths prior to sampling on the accuracy and precision of i-Tree Streets estimates were investigated though computer simulations and analysis of changes in variation in number of trees per street segment as a basis for recommending procedural changes. It was found that standardizing street segments significantly improved the precision of i-Tree Streets estimates. Based on the results of this investigation, it is generally recommended that street segments be standardized to 91m (300 ft) prior to conducting a sample inventory. Standardizing to 91m will significantly reduce the number of trees, the number of street segments, and the percentage of total street segments that must be sampled to achieve an estimate with a 10% relative standard error. The effectiveness of standardization and the associated processing time can be computed from municipal attributes before standardization so practitioners can gauge the marginal gains in field time versus costs in processing time. Automating standardization procedures or conducting an optimization study of segment length would continue to increase the efficiency and marginal gains associated with street segment standardization. / Master of Science

Data Visualization is used to help humans perceive high dimensional data, but it is unable to be applied in real time to data intensive computing applications. Attempts to process and apply traditional information visualization techniques to such applications result in slow or non-responsive applications. For such applications, sampling is often used to reduce big data to smaller data so that the benefits of data visualization can be brought to data intensive applications. Sampling allows data visualization to be used as an interface between humans and insights contained in the big data of data intensive computing. However, sampling introduces error. The objective of sampling is to reduce the amount of data being processed without introducing too much error into the results of the data intensive application. To determine an adequate level of sampling one can use statistical measures like standard error. However, such measures do not translate well for cases involving data visualization. Knowing the standard error of a sample can tell you very little about the visualization of that data. What is needed is a measure that allows system users to make an informed decision on the level of sampling needed to speed up a data intensive application. In this work we introduce an insight based measure for the impact of sampling on the results of visualized data. We develop a framework for the quantification of the level of insight, model the relationship between the level of insight and the amount of sampling, use this model to provide data intensive computing users with the ability to control the amount of sampling as a function of user provided insight requirements, and we develop a prototype that utilizes our framework. This work allows users to speed up data intensive applications with a clear understanding of how the speedup will impact the insights gained from the visualization of this data. Starting with a simple one dimensional data intensive application we apply our framework and work our way to a more complicated computational fluid dynamics case as a proof concept of the application of our framework and insight error feedback measure for those using sampling to speedup data intensive computing. / Doctor of Philosophy / Data Visualization is used to help humans perceive high dimensional data, but it is unable to be applied in real time to computing applications that generate or process vast amounts of data, also known as data intensive computing applications. Attempts to process and apply traditional information visualization techniques to such data result in slow or non-responsive data intensive applications. For such applications, sampling is often used to reduce big data to smaller data so that the benefits of data visualization can be brought to data intensive applications. Sampling allows data visualization to be used as an interface between humans and insights contained in the big data of data intensive computing. However, sampling introduces error. The objective of sampling is to reduce the amount of data being processed without introducing too much error into the results of the data intensive application. This error results from the possibility that a data sample could exclude valuable information that was included in the original data set. To determine an adequate level of sampling one can use statistical measures like standard error. However, such measures do not translate well for cases involving data visualization. Knowing the standard error of a sample can tell you very little about the visualization of that data. What is needed is a measure that allows one to make an informed decision of how much sampling to use in a data intensive application, as a result of knowing how sampling impacts how people gain insights from a visualization of the sampled data. In this work we introduce an insight based measure for the impact of sampling on the results of visualized data. We develop a framework for the quantification of the level of insight, model the relationship between the level of insight and the amount of sampling, use this model to provide data intensive computing users with an insight based feedback measure for each arbitrary sample size they choose for speeding up data intensive computing, and we develop a prototype that utilizes our framework. Our prototype applies our framework and insight based feedback measure to a computational fluid dynamics (CFD) case, but our work starts off with a simple one dimensional data application and works its way up to the more complicated CFD case. This work allows users to speed up data intensive applications with a clear understanding of how the speedup will impact the insights gained from the visualization of this data.

Anthropogenic salinization of freshwater is a global concern. In freshwater environments, elevated levels of major ions, measured as total dissolved solids (TDS) or specific conductance (SC), can cause adverse effects on aquatic ecosystem structure and function. In central Appalachia, eastern USA, studies largely rely on Rapid Bioassessment Protocols with semi-quantitative sampling to characterize benthic macroinvertebrate community response to increased salinity caused by surface coal mining. These protocols require subsampling procedures and identification of fixed numbers of individuals regardless of organism density, limiting measures of community structure. Quantitative sampling involves enumeration of all individuals collected within a defined area and typically includes larger sample sizes relative to semi-quantitative sampling, allowing expanded characterization of the benthic community. Working in central Appalachia, I evaluated quantitative and semi-quantitative methods for bioassessments in headwater streams salinized by coal mining during two time periods. I compared the two sampling methods for capability to detect SC-induced changes in the macroinvertebrate community. Quantitative sampling consistently produced higher estimates of taxonomic richness than corresponding semi-quantitative samples, and differences between sampling methods were found for community composition, functional feeding group, dominance, tolerance, and habit metrics. Quantitative methods were generally stronger predictors of benthic community-metric responses to SC and were more sensitive for detecting SC-induced changes in the macroinvertebrate community. Quantitative methods are advantageous compared to semi-quantitative sampling methods when characterizing benthic macroinvertebrate community structure because they provide more complete estimates of taxonomic richness and diversity and produce metrics that are stronger predictors of community response to elevated SC. / Master of Science / Surface coal mining in central Appalachia, eastern USA, contributes to increased salinity of surface waters, causing adverse effects on water quality and aquatic life. Stream condition is often evaluated through sampling of benthic macroinvertebrates because they are ubiquitous in aquatic environments and differ in sensitivity to various types of pollution and environmental stressors. In central Appalachia, studies have largely relied on semi-quantitative sampling methods to characterize effects of elevated salinity on benthic macroinvertebrate communities in headwater streams. These methods are ‘semiquantitative’ because processing of samples requires subsampling procedures and identification of a fixed number of individuals, regardless of the number of organisms that were originally collected. In contrast, quantitative sampling involves identification and counting of all collected individuals, often resulting in organism counts that are much higher than those of semi-quantitative samples. Quantitative samples are typically more time-consuming and expensive to process but allow for expanded description of the benthic macroinvertebrate community and characterization of community-wide response to an environmental stressor such as elevated salinity. Working in central Appalachian streams, I compared 1) depictions of benthic macroinvertebrate community structure; 2) benthic community response to elevated salinity; and 3) the minimum levels of salinity associated with community change between quantitative and semi-quantitative methods. Quantitative sampling methods provide many advantages over semi-quantitative methods by providing more complete enumerations of the taxa present, thus enhancing the ability to evaluate aquatic-life condition and to characterize overall benthic macroinvertebrate community response to elevated salinity caused by surface coal mining.

Bioassessment is the concept of evaluating the ecological condition of habitats by surveying the resident assemblages of living organisms. Conducting bioassessment with benthic macroinvertebrates is still evolving and continues to be refined. There are strongly divided opinions about study design, sampling methods, laboratory analyses, and data analysis. Two issues that are currently being debated about study design for bioassessment in streams were examined here: 1) what habitats within streams should be sampled; 2) and is it necessary to identify organisms to the species level? The influence of habitat sampling design and level of taxonomic identification on the interpretation of ecological conditions of ten small streams in western Virginia was examined. Cattle watering and grazing heavily affected five of these streams (impaired sites). The other five streams, with no recent cattle activity or other impact by man, were considered to be reference sites because they were minimally impaired and represented best attainable conditions. Inferential and non-inferential statistical analyses concluded that multiple habitat sampling design was more effective than a single habitat design (riffle only) at distinguishing impaired conditions, regardless of taxonomic level. It appeared that sampling design (riffle habitat versus multiple habitats) is more important than taxonomic identification level for distinguishing reference and impaired ecological conditions in this bioassessment study. All levels of taxonomic resolution, which were studied, showed that the macroinvertebrate assemblages at the reference and impaired sites were very different and the assemblages at the impaired sites were adversely affected by perturbation. This study supported the sampling of multiple habitats and identification to the family level as a design for best determining the ecological condition of streams in bioassessment. / Master of Science

Two methods developed to improve regression estimation in double sampling under the superpopulation model approach are examined. One method proposes the use of an alternative double sample regression estimator. The other method recommends the use of nonrandom, purposive subsampling plans. Both methods aim to reduce the mean squared errors of regression estimators in double sampling. A major criticism against the superpopulation model approach is its strong dependence on the correctness of the assumed model. Thus, two purposive subsampling plans were considered. The first plan designed subsamples based on the assumption that the superpopulation model was a first order linear model. The second plan selected subsamples that guarded against the occurrence of a second order model. As expected, the designed subsamples without protection can be very sensitive to the presence of a second order linear model. On the other hand, the designed subsamples with protection rendered the double sample regression estimators robust not only to a second order superpopulation model but also fairly robust to other slight model deviations such as variance misspecification. Therefore the use of designed subsamples with protection against a second order model is suggested whenever a first order superpopulation model is uncertain. Under designed subsamples with or without protection, the alternative double sample regression estimator is not found to be more efficient than the usual double sample regression estimator found in most sampling textbooks . However, the alternative double sample regression estimator has shown itself to be more efficient under simple random subsampling when the correlation between variables is weak and subsamples are small. / Ph. D.

In planning an effective and efficient audit examination, the auditor has to choose appropriate auditing technologies and procedures. This audit choice problem has been explored from several perspectives. However, it has not been viewed as an innovation process. This dissertation reports the results of an innovation decision study in internal auditing. Hypotheses of associations between the internal auditor’s decision to use statistical sampling and the perceived characteristics of statistical sampling are derived from Rogers’ <i>Innovation Diffusion</i> model (Everett Rogers, <i>Diffusion of Innovations</i>, 1983). Additional hypotheses relating the decision to use statistical sampling to personal and organizational characteristics are derived from the innovation adoption and implementation research literature. Data for this study were gathered by mailing a questionnaire to a sample of internal audit directors. Incorporated into the questionnaire are several scales for measuring (1) innovation attributes, (2) professionalism, (3) professional and organizational commitment, (4) management support for innovation, and (5) creativity decision style. The usable response rate was 32.5% (n= 260). The primary finding of this study is that the extent of use of attributes, dollar unit, and variables sampling techniques is positively associated with the respondents’ perceptions of their relative advantage, trialability, compatibility, and observability, and negatively associated with the techniques’ perceived complexity. A secondary finding is that there is no overall association between the extent of use of statistical sampling by the internal auditors and their (1) professionalism, (2) professional and organizational commitment, (3) decision style, and (4) organizational support for innovation. Further exploration using multiple regression and logistic regression analyses indicate that several of the personal and organizational characteristics add to the ability of the regression models to explain the extent of use of statistical sampling. Evidence that organization types do have an effect upon the innovation decision process is presented. The study concludes by discussing its implications for understanding the innovation decision process of internal auditors, for designing and managing future innovation processes in auditing, and for further research into audit choice problems and innovation decisions of auditors and accountants. / Ph. D.

Process control charts are widely used to display sample data from a process for purposes of determining whether a process is in control, for bringing an out-of-control process into control, and for monitoring a process to make sure that it stays in control. The usual practice in maintaining a control chart is to take samples from the process at fixed length sampling intervals. This research investigates the modification of the standard practice where the sampling interval or time between samples is not fixed but can vary depending on what is observed from the data. Variable sampling interval process control procedures are considered for monitoring the outcome of a production process. The time until the next sample depends on what is being observed in the current sample. Sampling is less frequent when the process is at a high level of quality and vise versa. Properties such as the average number of samples until signal, average time to signal and the variance of the time to signal are developed for the variable sampling interval Shewhart and cusum charts. A Markov chain is utilized to approximate the average time to signal and the corresponding variance for the cusum charts. Properties of the variable sampling interval Shewhart chart are investigated through Renewal Theory and Markov chain approaches for the cases of a sudden and gradual shift in the process mean respectively. Also considered is the case of a shift occurring in the time between two samples without the simplifying assumption that the process mean remains the same from time zero onward. For such a case, the adjusted time to signal is developed for both the Shewhart and cusum charts in addition to the variance of the adjusted time to signal. Results show that the variable sampling interval control charts are considerably more efficient than the corresponding fixed sampling interval control charts. It is preferable to use only two sampling intervals which keeps the complexity of the chart to a reasonable level and has practical implications. This feature should make such charts very appealing for use in industry and other fields of application where control charts are used. / Ph. D.