Development of a Ground-Based Aerial-Tracking Instrument for Open-Path Spectroscopy to Monitor Atmospheric Constituents

A ground-based aerial-tracking instrument, known as the Ground Tracker, designed to provide spectral data to quantify greenhouse gases is under development. The Ground Tracker includes an Optical System including a high power rifle scope, video camera, and spectrometer used to locate an active light source from the Emitter, and collect spectral data by utilizing an actuating mirror. The implementation of this instrument could be made low cost by utilizing existing weather balloon infrastructure to allow the Emitter to be placed into the lower stratosphere. The recovery of the emitter will be possible by tracking the GPS coordinates. Weather balloon instrument packages contain shipping instructions and postage for those packages that go beyond GPS range or are lost. The Ground Tracker and Emitter Gimbal, while not ready for implementation, demonstrate the feasibility of a spectroscopy system that could provide important data for climate observation and modeling at temporal and spatial resolutions not currently available to state-of-the-art satellites

Analysis of signal to noise enhancement using a highly selective modulation tracking filter

Experiments are reported which utilize photodielectric effects in semiconductor loaded superconducting resonant circuits for suppressing noise in RF communication systems. The superconducting tunable cavity acts as a narrow band tracking filter for detecting conventional RF signals. Analytical techniques were developed which lead to prediction of signal-to-noise improvements. Progress is reported in optimization of the experimental variables. These include improved Q, new semiconductors, improved optics, and simplification of the electronics. Information bearing signals were passed through the system, and noise was introduced into the computer model

Every Step a Novel: Historical Circumstances and Somali American Identity

This project is designed to help high school students learn about the experiences, history and identity of Somali men who came to Utah as refugees. It is organized around the oral histories of eight Somali men who live in the Salt Lake City area. They were collected by Haden Griggs in the latter half of 2019. Transcripts and audio recordings for all the interviews are available here. A paper, analyzing the historical circumstances and variations on Somali identity, is included here for scholarly or instructor use. This project also includes a digital exhibit tracing recent Somali history and contextualizing the experiences of these men within that history, found at http://exhibits.usu.edu/exhibits/show/somalisaltlake, as well as a lesson plan to be used in conjunction with the exhibit

Controlling illegal stimulants: a regulated market model

Prohibition of illegal drugs is a failed social policy and new models of regulation of these substances are needed. This paper explores a proposal for a post-prohibition, public health based model for the regulation of the most problematic drugs, the smokable and injectable stimulants. The literature on stimulant maintenance is explored. Seven foundational principles are suggested that could support this regulatory model of drug control that would reduce both health and social problems related to illegal stimulants. Some details of this model are examined and the paper concludes that drug policies need to be subject to research and based on evidence

Atomic Cows: The Alamogordo Herd at Oak Ridge, Tennessee, 1945-1965

This thesis examines the place of cows and agriculture in nuclear fallout research at Oak Ridge, Tennessee. The 1945 Trinity atomic bomb test exposed a herd of grazing Hereford cattle in Alamogordo, New Mexico, to radioactive fallout. The U.S. Army shipped this herd to Oak Ridge which led to the establishment of the University of Tennessee-Atomic Energy Commission Agricultural Research Laboratory (UT-AEC). The UT-AEC laboratory studied the Alamogordo herd until 1964 to understand the long-term effects of radiation. Close research and media following of the Alamogordo herd directly informed humans’ attempts to dominate nuclear power by controlling nature. Scientists and nationwide media outlets celebrated the Alamogordo herd as proof of the animals’ health and reproductive success after radiation exposure, aligning with broader efforts to ease atomic anxieties. However, as this thesis demonstrates, nature fights back because the Alamogordo herd thwarted these human narratives of control. Ultimately, my thesis engages with scholarship on agricultural, Cold War, and nuclear energy history. My research examines the changes in postwar America, such as rising atomic anxieties, vulnerabilities to industrial agriculture, “Atoms for Peace” movements, and civil defense efforts

Modelling water quality : complexity versus simplicity

Water quality management makes use of water quality models as decision making tools. Water quality management decisions need to be informed by information that is as reliable as possible. There are many situations where observational data are limited and therefore models or simulation methods have a significant role to play in providing some information that can be used to guide management decisions. Water quality modelling is the use of mathematical equations and statistics to represent the processes affecting water quality in the natural environment. Water quality data are expensive and difficult to obtain. Nutrient sampling requires a technician to obtain ‘grab samples’ which need to be kept at low temperatures and analysed in a laboratory. The laboratory analyses of nutrients is expensive and time consuming. The data required by water quality models are seldom available as complete datasets of sufficient length. This is especially true for ungauged regions, either in small rural catchments or even major rivers in developing countries. Water quality modelling requires simulated or observed water quantity data as water quality is affected by water quantity. Both the water quality modelling and water quantity modelling require data to simulate the required processes. Data are necessary for both model structure as well as model set up for calibration and validation. This study aimed to investigate the simulation of water quality in a low order stream with limited observed data using a relatively complex as well as a much simpler water quality model, represented by QUAL2K and an in-house developed Mass Balance Nutrient (MBN) model, respectively. The two models differ greatly in the approach adopted for water quality modelling, with QUAL2K being an instream water quality fate model and the MBN model being a catchment scale model that links water quantity and quality. The MBN model uses hydrological routines to simulate those components of the hydrological cycle that are expected to differ with respect to their water quality signatures (low flows, high flows, etc.). Incremental flows are broken down into flow fractions, and nutrient signatures are assigned to fractions to represent catchment nutrient load input. A linear regression linked to an urban runoff model was used to simulate water quality entering the river system from failing municipal infrastructure, which was found to be a highly variable source of nutrients within the system. A simple algal model was adapted from CE-QUAL-W2 to simulate nutrient assimilation by benthic algae. QUAL2K, an instream water quality fate model, proved unsuitable for modelling diffuse sources for a wide range of conditions and was data intensive when compared to the data requirements of the MBN model. QUAL2K did not simulate water quality accurately over a wide range of flow conditions and was found to be more suitable to simulating point sources. The MBN model did not provide accurate results in terms of the simulation of individual daily water quality values; however, the general trends and frequency characteristics of the simulations were satisfactory. Despite some uncertainties, the MBN model remains useful for extending data for catchments with limited observed water quality data. The MBN model was found to be more suitable for South African conditions than QUAL2K, given the data requirements of each model and water quality and flow data available from the Department of Water and Sanitation. The MBN model was found to be particularly useful by providing frequency distributions of water quality loads or concentrations using minimal data that can be related to the risks of exceeding management thresholds

Modelling water quality : complexity versus simplicity

Water quality management makes use of water quality models as decision making tools. Water quality management decisions need to be informed by information that is as reliable as possible. There are many situations where observational data are limited and therefore models or simulation methods have a significant role to play in providing some information that can be used to guide management decisions. Water quality modelling is the use of mathematical equations and statistics to represent the processes affecting water quality in the natural environment. Water quality data are expensive and difficult to obtain. Nutrient sampling requires a technician to obtain ‘grab samples’ which need to be kept at low temperatures and analysed in a laboratory. The laboratory analyses of nutrients is expensive and time consuming. The data required by water quality models are seldom available as complete datasets of sufficient length. This is especially true for ungauged regions, either in small rural catchments or even major rivers in developing countries. Water quality modelling requires simulated or observed water quantity data as water quality is affected by water quantity. Both the water quality modelling and water quantity modelling require data to simulate the required processes. Data are necessary for both model structure as well as model set up for calibration and validation. This study aimed to investigate the simulation of water quality in a low order stream with limited observed data using a relatively complex as well as a much simpler water quality model, represented by QUAL2K and an in-house developed Mass Balance Nutrient (MBN) model, respectively. The two models differ greatly in the approach adopted for water quality modelling, with QUAL2K being an instream water quality fate model and the MBN model being a catchment scale model that links water quantity and quality. The MBN model uses hydrological routines to simulate those components of the hydrological cycle that are expected to differ with respect to their water quality signatures (low flows, high flows, etc.). Incremental flows are broken down into flow fractions, and nutrient signatures are assigned to fractions to represent catchment nutrient load input. A linear regression linked to an urban runoff model was used to simulate water quality entering the river system from failing municipal infrastructure, which was found to be a highly variable source of nutrients within the system. A simple algal model was adapted from CE-QUAL-W2 to simulate nutrient assimilation by benthic algae. QUAL2K, an instream water quality fate model, proved unsuitable for modelling diffuse sources for a wide range of conditions and was data intensive when compared to the data requirements of the MBN model. QUAL2K did not simulate water quality accurately over a wide range of flow conditions and was found to be more suitable to simulating point sources. The MBN model did not provide accurate results in terms of the simulation of individual daily water quality values; however, the general trends and frequency characteristics of the simulations were satisfactory. Despite some uncertainties, the MBN model remains useful for extending data for catchments with limited observed water quality data. The MBN model was found to be more suitable for South African conditions than QUAL2K, given the data requirements of each model and water quality and flow data available from the Department of Water and Sanitation. The MBN model was found to be particularly useful by providing frequency distributions of water quality loads or concentrations using minimal data that can be related to the risks of exceeding management thresholds