Montana Technological University

Taxomyces andreanae, a fungal endophyte, was isolated from the phloem (inner bark) of the Pacific yew, Taxus brevifolia. The fungus is hyphomyceteous and, when grown in a semi-synthetic liquid medium, produced taxol and related compounds. Taxol was identified by mass spectrometry, chromatography, and reactivity with monoclonal antibodies specific for taxol. Both [1-14C]acetic acid and L-[U-14C]phenylalanine served as precursors of [14C]taxol in fungal cultures. No taxol was detected in zero-time cultures or in the small agar plugs used to inoculate the culture flasks.

The magnitude 7.3 Landers earthquake of 28 June 1992 triggered a remarkably sudden and widespread increase in earthquake activity across much of the western United States. The triggered earthquakes, which occurred at distances up to 1250 kilometers (17 source dimensions) from the Landers mainshock, were confined to areas of persistent seismicity and strike-slip to normal faulting. Many of the triggered areas also are sites of geothermal and recent volcanic activity. Static stress changes calculated for elastic models of the earthquake appear to be too small to have caused the triggering. The most promising explanations involve nonlinear interactions between large dynamic strains accompanying seismic waves from the mainshock and crustal fluids (perhaps including crustal magma).

Power system loads are constantly changing. Over a time-span of a few minutes, these changes are primarily random. The random load variations act as a constant low-level excitation to the electromechanical dynamics of the power system which shows up as ambient noise in field measured voltage, current and power signals. Assuming the random variations are white and stationary over an analysis window, it is theoretically possible to estimate the electromechanical modal frequencies and damping from the spectral content of the ambient noise. In this paper, field collected ambient noise is analyzed by solving the Wiener-Hopf linear prediction equations to estimate the modal frequency and damping. These estimates are then compared with results from a Prony analysis on a ringdown resulting from a 1400 MW brake insertion under the same operating conditions as the ambient data. Results show that estimates are consistent between the ambient and ringdown analysis indicating that it is possible to estimate a power system's electromechanical characteristics simply from ambient data. These results demonstrate that it may be possible to provide power system control and operation algorithms with a real-time estimate of modal frequency and damping.

Prony analysis has proven to be a valuable tool in estimating the modal content of power oscillations from measured ringdowns. The accuracy of the mode estimates is limited by the noise content always found in field measured signals. Current Prony analysis methods assume the system to be single output, and individual signals are analyzed independently often resulting in conflicting frequency and damping estimates (due to noise effects). This paper considers a simple extension to Prony analysis that allows multiple signals to be analyzed simultaneously resulting in one set of mode estimates. Examples are used to show that this extension improves the accuracy of modal estimates and simplifies the analysis steps. The first example uses a Monte Carlo type simulation model and the second analyzes field measured data from the western North American power system.

The Industrial Revolution changed the landscape of lifestyle and along came the pollution problems. The United States took notice of the growing air pollution problem, and the Air Pollution Control Act was formulated in 1955 by the Congress, making the way to fund federal agencies to conduct research in air pollution. The Air Quality Act of 1967 articulated that the federal government has the right and duty to enforce control measures for air pollution. This chapter focuses on control techniques for particulate and gaseous pollutants. The gaseous pollutants included are SO2, NOx, and volatile organic compound (VOC). The two major techniques for FGT of NO X are selective catalytic reduction (SCR) and selective noncatalytic reduction (SCNR). Catalytic oxidation systems are preferred and commonly used to destroy gaseous compounds from soil venting operations. The chapter shows various particle capture mechanisms in various particulate control devices.

Abstract Unconventional formations such as the Eagle Ford, Niobrara and Bakken have made a significant impact on the oil industry over the last decade, but primary recovery factors are still low, typically less than 10%. The need for improved oil recovery (IOR) has been documented, but most published studies have focused on simulation models and lab tests. The next logical step includes field trials or pilot projects. Over the last 7-8 years, there have been a number of pilot tests for both water and gas injection in the Bakken. Results from these small pilots were reported to state agencies, and the first part of this paper analyzes the available public data on these pilots. Injectivity of gas or water does not appear to be an issue in the Bakken; however, the projects, in general, show early breakthrough times and poor reservoir sweep efficiencies. Offset wells showed little to no additional oil recovery, but the pilots were limited in scope and duration. Mitigating procedures were not fully implemented to deal with the problems that occurred. This paper also proposes methodologies for implementing second generation pilots for unconventional reservoirs. Methods are devised to improve understanding of the near well formation before injection starts, detect where fluids are entering and leaving along the lateral and correct for any associated poor sweep efficiency. We also propose long term information collecting strategies and contingency plans to deal with difficulties that may arise during the pilot. Using EOR to increase recovery from unconventional oil fields is important for the continued success of these plays, and this paper provides a thorough analysis of implementing IOR pilots in these fields.

The frequency and damping of electromechanical modes offer considerable insight into the dynamic stability properties of a power system. The performance properties of three mode-estimation block-processing algorithms from the perspective of near real-time automated stability assessment are demonstrated and examined. The algorithms are: the extended modified Yule Walker (YW); extended modified Yule Walker with spectral analysis (YWS); and sub-space system identification (N4SID). The YW and N4SID have been introduced in previous publications while the YWS is introduced here. Issues addressed include: stability assessment requirements; automated subset selecting identified modes; using algorithms in an automated format; data assumptions and quality; and expected algorithm estimation performance.

This paper proposes a regularized robust recursive least squares (R3LS) method for online estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudo-random probing, ambient, and ringdown data. A robust objective function is utilized to reduce the negative influence from nontypical data, which include outliers and missing data. A dynamic regularization method is introduced to help include <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and nontypical measurement data.

Abstract Formation waters associated with coalbed methane have a common chemical character that can be an exploration tool, regardless of formation lithology or age. Effectively devoid of sulfate, calcium, and magnesium, the waters contain primarily sodium and bicarbonate and, where influenced by water of marine association, also contain chloride. The distinct geochemical signature evolves through the processes of biochemical reduction of sulfate, enrichment of bicarbonate, and precipitation of calcium and magnesium. Cation exchange with clays may also deplete the dissolved calcium and magnesium, but is not prerequisite. Low sulfate/bicarbonate ratios characterize these waters and are also common but less pronounced with occurrences of conventional oil and gas. Waters rich in sulfate, calcium, and magnesium occur in many coalbed aquifers but are not found in association with methane. Users of total dissolved solids data should ensure that the values reflect adjustments of bicarbonate concentrations to simulate evaporation residues. Results that erroneously sum the entire bicarbonate content can be far too high in these bicarbonate-rich waters, thereby exacerbating the issues of disposal. Evaluations of prospects and choices of exploration targets can be enhanced by an added focus on the geochemical signature that should be expected in association with methane. Knowledge of the geochemical signature may also be useful in the commonly protracted testing of wells. The appearance of high sulfate concentrations in water analyses can justify early curtailment of test pumping and can prompt the siting of subsequent drill holes farther from areas of recharge.

It is often argued that 3D inversion of entire airborne electromagnetic (AEM) surveys is impractical, and that 1D methods provide the only viable option for quantitative interpretation. However, real geological formations are 3D by nature and 3D inversion is required to produce accurate images of the subsurface. To that end, we show that it is practical to invert entire AEM surveys to 3D conductivity models with hundreds of thousands if not millions of elements. The key to solving a 3D AEM inversion problem is the application of a moving footprint approach. We have exploited the fact that the area of the footprint of an AEM system is significantly smaller than the area of an AEM survey, and developed a robust 3D inversion method that uses a moving footprint. Our implementation is based on the 3D integral equation method for computing data and sensitivities, and uses the re-weighted regularised conjugate gradient method for minimising the objective functional. We demonstrate our methodology with the 3D inversion of AEM data acquired for salinity mapping over the Bookpurnong Irrigation District in South Australia. We have inverted 146 line km of RESOLVE data for a 3D conductivity model with ~310 000 elements in 45 min using just five processors of a multi-processor workstation.

Accurate knowledge of low-frequency electromechanical modes in power systems gives vital information about the stability of the system. Current techniques for estimating electromechanical modes are computationally intensive and rely on complex system models. This research complements model-based approaches and uses measurement-based techniques. This paper discusses the development of an autoregressive moving average (ARMA) block-processing technique to estimate these low-frequency electromechanical modes from measured ambient power system data without requiring a disturbance. This technique is applied to simulated data containing a stationary low-frequency mode generated from a 19-machine test model. The frequency and damping factor of the estimated modes are compared with the actual modes for various block sizes. This technique is also applied to 35-min blocks of actual ambient power system data before and after a disturbance and compared to results from Prony analysis on the ringdown from the disturbance.

Accurate knowledge of low-frequency electromechanical modes in power systems gives vital information about the stability of the system. Current techniques for estimating electromechanical modes are computationally intensive and rely on complex system models. This research complements model based approaches and uses measurement-based techniques. This paper discusses the development of an ARMA (auto-regressive moving average) block processing technique to estimate these low-frequency electromechanical modes from measured ambient power system data without requiring a disturbance. This technique is applied to simulated data containing a stationary low-frequency mode generated from a 19-machine test model. The frequency and damping factor of the estimated modes are compared with the actual modes for various block sizes. This technique is also applied to 35-minute blocks of actual ambient power system data before and after a disturbance and compared to results from Prony analysis on the ringdown from the disturbance.

Substantial diel (24‐hour) cycles in dissolved (0.1‐μm filtration) metal concentrations were observed during low flow for 18 sampling episodes at 14 sites on 12 neutral and alkaline streams draining historical mining areas in Montana and Idaho. At some sites, concentrations of Cd, Mn, Ni, and Zn increased as much as 119, 306, 167, and 500%, respectively, from afternoon minimum values to maximum values shortly after sunrise. Arsenic concentrations exhibited the inverse temporal pattern with increases of up to 54%. Variations in Cu concentrations were small and inconsistent. Diel metal cycles are widespread and persistent, occur over a wide range of metal concentrations, and likely are caused primarily by instream geochemical processes. Adsorption is the only process that can explain the inverse temporal patterns of As and the divalent metals. Diel metal cycles have important implications for many types of water‐quality studies and for understanding trace‐metal mobility.

This paper provides a review of the progress in regard to the InSAR remote sensing technique and its applications in earth and environmental sciences, especially in the past decade. Basic principles, factors, limits, InSAR sensors, available software packages for the generation of InSAR interferograms were summarized to support future applications. Emphasis was placed on the applications of InSAR in seismology, volcanology, land subsidence/uplift, landslide, glaciology, hydrology, and forestry sciences. It ends with a discussion of future research directions.

This paper proposes a robust recursive least square (RRLS) algorithm for online identification of power system modes based on measurement data. The measurement data can be either ambient or ringdown. Also, the mode estimation is provided in real-time. The validity of the proposed RRLS algorithm is demonstrated with both simulation data from a 17-machine model and field measurement data from a wide area measurement system (WAMS). Comparison with the conventional recursive least square (RLS) and least mean square (LMS) algorithms shows that the proposed RRLS algorithm can identify the modes from the combined ringdown and ambient signals with outliers and missing data in real-time without noticeable performance degradation. An adaptive detrend algorithm is also proposed to remove the signal trend based on the RRLS algorithm. It is shown that the algorithm can keep up with the measurement data flow and work online to provide real-time mode estimation.

Increasing levels of wind-turbine generation in modern power systems is initiating a need for accurate wind-generation transient stability models. Because many wind generators are often grouped together in wind parks, equivalence modeling of several wind generators is especially critical. In this paper, a reduced-order dynamic fixed-speed wind-generator model appropriate for transient stability simulation is presented. The model is derived using a model reduction technique of a high-order finite-element model. Then, an equivalencing approach is presented that demonstrates how several wind generators in a wind park can be combined into a single reduced-order model. Simulation cases are presented to demonstrate several unique properties of a power system containing wind generators. The results in this paper focus on horizontal-axis turbines using an induction machine directly connected to the grid as the generator.

Cyanobacteriophage Syn9 is a large, contractile-tailed bacteriophage infecting the widespread, numerically dominant marine cyanobacteria of the genera Prochlorococcus and Synechococcus. Its 177,300 bp genome sequence encodes 226 putative proteins and six tRNAs. Experimental and computational analyses identified genes likely involved in virion formation, nucleotide synthesis, and DNA replication and repair. Syn9 shows significant mosaicism when compared with related cyanophages S-PM2, P-SSM2 and P-SSM4, although shared genes show strong purifying selection and evidence for large population sizes relative to other phages. Related to coliphage T4 - which shares 19% of Syn9's genes - Syn9 shows evidence for different patterns of DNA replication and uses homologous proteins to assemble capsids with a different overall structure that shares topology with phage SPO1 and herpes virus. Noteworthy bacteria-related sequences in the Syn9 genome potentially encode subunits of the photosynthetic reaction centre, electron transport proteins, three pentose pathway enzymes and two tryptophan halogenases. These genes suggest that Syn9 is well adapted to the physiology of its photosynthetic hosts and may affect the evolution of these sequences within marine cyanobacteria.

Berkeley Pit Lake is an abandoned open-pit copper mine filled with 30 billion gallons of acidic, metal-contaminated water. This harsh environment is proving to be a source of unusual microorganisms that produce novel bioactive metabolites. Bioassay-guided fractionation using signal transduction enzyme assays led to the isolation of the novel spiroketal, berkelic acid 1, and of the known gamma-pyrone, spiciferone A 4. Berkelic acid has shown selective, nanomolar activity against OVCAR-3, an ovarian cancer cell line in the National Cancer Institute cell line screen. The isolation and characterization of these compounds are reported here.

A theoretical basis and signal-processing approach for estimating a power system's electromechanical mode-shape properties using time-synchronized phasor measurements are presented. The relationship between modal eigenvectors and measurable power system quantities are derived. Spectral correlation analysis is used to implement the approach with demonstrative examples. This includes simulation examples as well as measured data from the western North American power system.

The management of rare species is a conservation priority worldwide, but this task is made difficult by detection errors in population surveys. Both false positive (misidentification) and false negative (missed detection) errors are prevalent in surveys for rare species and can affect resulting inferences about their population status or distribution. Environmental DNA (eDNA)—DNA shed from an organism in its environment—coupled with quantitative PCR (qPCR) analyses, has become a reliable and extremely sensitive mean for identifying rare species in aquatic systems. Due to the demonstrated effectiveness of these methods, we tested their efficacy in surveys for rare species in terrestrial settings to reduce detection errors for three rare forest carnivores of conservation concern: Canada lynx (Lynx canadensis), fisher (Pekania pennanti), and wolverine (Gulo gulo). We specifically investigated our ability to reliably: 1) identify species directly from snow samples collected within tracks; 2) identify species by collecting snow in locations where an animal had been photographed; and 3) identify species from hair samples collected during the summer after being deployed throughout the winter (i.e., overwinter surveys). Our findings indicated that qPCR assays can effectively detect DNA of all three species, including from snow-track surveys, snow collected at camera stations, and overwinter samples that failed to amplify with conventional PCR techniques. All results indicate that the sources of targeted DNA collection provided adequate quantities of DNA for robust species detection. We suggest that using qPCR methods to detect DNA has the potential to revolutionize winter surveys for rare species in terrestrial settings by reducing or eliminating misidentifications and missed detections.