Engineering topics

Sego, Landon H

Publications and source records attributed to Sego, Landon H.

5 records

DOE OSTI2013

Cross-Combined Composite Sampling Designs for Identification of Elevated Regions

Analyzing soils for contaminants can be costly. Generally, discrete samples are gathered from within a study area, analyzed by a laboratory and the results are used in a site-specific statistical analysis. Because of the heterogeneities that exist in soil samples within study areas, a large amount of variability and skewness may be present in the sample population. This necessitates collecting a large number of samples to obtain reliable inference on the mean contaminant concentration and to understand the spatial patterns for future remediation. Composite or Incremental sampling is a commonly applied method for gathering multiple discrete samples and physically combining them, such that each combination of discrete samples requires a single laboratory analysis, which reduces cost and can improve the estimates of the mean concentration. While incremental sampling can reduce cost and improve mean estimates, current implementations do not readily facilitate the characterization of spatial patterns or the detection of elevated regions within study areas. The methods we present in this work provide efficient estimation and inference for the mean contaminant concentration over the entire spatial area and enable the identification of high contaminant regions within the area of interest. We develop sample design methodologies that explicitly define the characteristics of these designs (such as sample grid layout) and quantify the number of incremental samples that must be obtained under a design criteria to control false positive and false negative (Type I and II) decision errors. We present the sample design theory and specifications as well as results on simulated and real data.

Hathaway, John E↗

DOE OSTI2012

Preliminary Effects of Real-World Factors on the Recovery and Exploitation of Forensic Impurity Profiles of a Nerve-Agent Simulant from Office Media

Dimethyl methylphosphonate (DMMP) was used as a chemical threat agent (CTA) simulant for a first look at the effects of real-world factors on the recovery and exploitation of a CTA’s impurity profile for source matching. Four stocks of DMMP having different impurity profiles were disseminated as aerosols onto cotton, painted wall board, and nylon coupons according to a thorough experimental design. The DMMP-exposed coupons were then solvent extracted and analyzed for DMMP impurities by comprehensive 2-D gas chromatography/mass spectrometry (GC×GC/MS). The similarities between the coupon DMMP impurity profiles and the known (reference) DMMP profiles were measured by dot products of the coupon profiles and known profiles and by score values obtained from principal component analysis. One stock, with a high impurity-profile selectivity value of 0.9 out of 1, had 100% of its respective coupons correctly classified and no false positives from other coupons. Coupons from the other three stocks with low selectivity values (0.0073, 0.012, and 0.018) could not be sufficiently distinguished from one another for reliable matching to their respective stocks. The results from this work support that: (1) extraction solvents, if not appropriately selected, can have some of the same impurities present in a CTA reducing a CTA’s useable impurity profile, (2) low selectivity among a CTA’s known impurity profiles will likely make definitive source matching impossible in some real-world conditions, (3) no detrimental chemical-matrix interference was encountered during the analysis of actual office media, (4) a short elapsed time between release and sample storage is advantageous for the recovery of the impurity profile because it minimizes volatilization of forensic impurities, and (5) forensic impurity profiles weighted towards higher volatility impurities are more likely to be altered by volatilization following CTA exposure.

Chemical forensics↗

DOE OSTI2012

Implementing the Data Center Energy Productivity Metric

As data centers proliferate in both size and number, their energy efficiency is becoming increasingly important. We discuss the properties of a number of the proposed metrics of energy efficiency and productivity. In particular, we focus on the Data Center Energy Productivity (DCeP) metric, which is the ratio of useful work produced by the data center to the energy consumed performing that work. We describe our approach for using DCeP as the principal outcome of a designed experiment using a highly instrumented, high performance computing data center. We found that DCeP was successful in clearly distinguishing between different operational states in the data center, thereby validating its utility as a metric for identifying configurations of hardware and software that would improve (or even maximize) energy productivity. We also discuss some of the challenges and benefits associated with implementing the DCeP metric, and we examine the efficacy of the metric in making comparisons within a data center and among data centers.

Sego, Landon H↗

DOE OSTI2011

Situational Awareness as a Measure of Performance in Cyber Security Collaborative Work

Cyber defense competitions arising from U.S. service academy exercises, offer a platform for collecting data that can inform research that ranges from characterizing the ideal cyber warrior to describing behaviors during certain challenging cyber defense situations. This knowledge in turn could lead to better preparation of cyber defenders in both military and civilian settings. We conducted proof of concept experimentation to collect data during the Pacific-rim Regional Collegiate Cyber Defense Competition (PRCCDC) and analyzed it to study the behavior of cyber defenders. We propose that situational awareness predicts performance of cyber security professionals, and in this paper we focus on our collection and analysis of competition data to determine whether it supports our hypothesis. In addition to normal cyber data, we collected situational awareness and workload data and compared it against the performance of cyber defenders as indicated by their competition score. We conclude that there is a weak correlation between our measure of situational awareness and performance that we hope to exploit in further studies.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2010

Acceptance sampling using judgmental and randomly selected samples

We present a Bayesian model for acceptance sampling where the population consists of two groups, each with different levels of risk of containing unacceptable items. Expert opinion, or judgment, may be required to distinguish between the high and low-risk groups. Hence, high-risk items are likely to be identifed (and sampled) using expert judgment, while the remaining low-risk items are sampled randomly. We focus on the situation where all observed samples must be acceptable. Consequently, the objective of the statistical inference is to quantify the probability that a large percentage of the unsampled items in the population are also acceptable. We demonstrate that traditional (frequentist) acceptance sampling and simpler Bayesian formulations of the problem are essentially special cases of the proposed model. We explore the properties of the model in detail, and discuss the conditions necessary to ensure that required samples sizes are non-decreasing function of the population size. The method is applicable to a variety of acceptance sampling problems, and, in particular, to environmental sampling where the objective is to demonstrate the safety of reoccupying a remediated facility that has been contaminated with a lethal agent.

54 ENVIRONMENTAL SCIENCES↗