Socially Responsible Investing: A Comparative Analysis for the Bluegrass Community Foundation

In the fall of 2010 the Blue Grass Community Foundation began considering whether an updated investment strategy that included Socially Responsible Investing would be advantageous and in line with the mission of the organization. The question was raised whether this type of investing meant that the return on investment from securities currently held in the Foundation’s portfolio had to be sacrificed in order to incorporate this type of investment philosophy into the existing criteria used to invest the organization’s assets. This paper examines literature relevant to the topic and conducts an analysis of a sample of mutual funds currently available in the market, some of which are socially responsible funds and some of which are not. The literature on the topic, while not conclusive, would seem to indicate that social investing has been expanding in recent years, growing even faster than the larger investment universe as a whole. It also seems to be less likely that investor will be forced to give up significant returns in order to satisfy socially responsible motivations. This data analysis, while limited, appears to be in line with this assessment

Polarimetric radar processing of AIRSAR imagery from Los Angeles basin region

Title from PDF of title page (University of Missouri--Columbia, viewed on February 22, 2011).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Justin J. Legarsky.M. S. University of Missouri--Columbia 2009.Extracting useful information and intelligence from polarimetric interferometric synthetic aperture radar (PolInSAR) data involves a variety of highly sophisticated processing methods. To aid in the advancement of efficient PolInSAR processing techniques, an investigation of underlying scattering mechanisms such as coherent scatterers (CS) and polarimetric decomposition techniques is conducted in this study using JPL AIRSAR fully polarimetric data over a portion of the greater Los Angeles area. For this study, selection of the overall optimum polarization showed an increase of CS candidates compared to standard polarizations. In addition, polarimetric decomposition ([alpha]-H and F/D) analysis of CS and non-CS (NCS) pixels found a trend of increasing double-bounce scattering, Fd, with decreasing volume scattering, Fv, and polarimetric Entropy, H, for CS relative to NCS.Includes bibliographical references

Direct Insertion of NASA Airborne Snow Observatory-Derived Snow Depth Time Series Into the \u3cem\u3eiSnobal\u3c/em\u3e Energy Balance Snow Model

Accurately simulating the spatiotemporal distribution of mountain snow water equivalent improves estimates of available meltwater and benefits the water resource management community. In this paper we present the first integration of lidar-derived distributed snow depth data into a physics-based snow model using direct insertion. Over four winter seasons (2013–2016) the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) Airborne Snow Observatory (ASO) performed near-weekly lidar surveys throughout the snowmelt season to measure snow depth at high resolution over the Tuolumne River Basin above Hetch Hetchy Reservoir in the Sierra Nevada Mountains of California. The modeling component of the ASO program implements the iSnobal model to estimate snow density for converting measured depths to snow water equivalent and to provide temporally complete snow cover mass and thermal states between flights. Over the four years considered in this study, snow depths from 36 individual lidar flights were directly inserted into the model to provide updates of snow depth and distribution. Considering all updates to the model, the correlation between ASO depths and modeled depths with and without previous updates was on average r2 = 0.899 (root-mean-square error = 12.5 cm) and r2 = 0.162 (root-mean-square error = 41.5 cm), respectively. The precise definition of the snow depth distribution integrated with the iSnobal model demonstrates how the ASO program represents a new paradigm for the measurement and modeling of mountain snowpacks and reveals the potential benefits for managing water in the region

Synthesizing Measurement, Modeling and Remote Sensing Techniques to Study Spatiotemporal Variability of Seasonal Snow

Mountain snowpacks vary drastically over length scales as small as 1—2 meters in complex terrain and require high resolution measurements to accurately quantify the spatial distribution of snow. This thesis explores this spatial distribution using remote sensing, modeling and ground-based observations. Snow depth estimates from airborne LiDAR at 5 m resolution over 750 km2 was compared to in situ observations and results from physically-based snow and wind redistribution models, and a new low cost method for continuous depth measurements at the slope scale was developed. Repeated airborne Light Detection And Ranging (LiDAR) surveys are capable of recording snow depth distributions at 1—5 meter resolution over very large geographic areas, while additionally providing information about vegetation, slope aspect and terrain roughness. During NASA\u27s second Cold Lands Processes eXperiment (CLPX-II) in the winter of 2006/07, two LiDAR surveys were flown nearly three months apart over a vast 750 km2 swath of the Rocky Mountains near Steamboat Springs, Colorado. Both flights took place well before any significant melt occurred, and the difference of the vegetation-filtered surfaces resulted in an estimate of the change in snow height across the survey area. An intensive manual measurement campaign was conducted to coincide with each LiDAR flight to provide ground truth information for the LiDAR dataset. Using the in situ measurements and the LiDAR-derived snow depth changes, an uncertainty study was performed to investigate errors in snow depth change for this high resolution remote sensing method due to elevation gradients and vegetation types. Secondly, this work leverages the large extent of the CLPX-II LiDAR dataset to validate more than 900 pixels, each at 30 arc-second resolution, of modeled snow depth from the SNOw Data Assimilation System (SNODAS) operational hydrologic model developed by the National Operational Hydrologic Remote Sensing Center (NOHRSC). Upscaling the high resolution LiDAR-derived snow depths to the much lower spatial resolution of the SNODAS estimates produced a statistically robust dataset of over 900 independent pixel comparisons for the first time, due to the difficulty in obtaining independent validation data at the 1 km scale. Results support the notion that sub pixel-scale slope, aspect, vegetation density and terrain rough- ness factors are important to consider for model predictions of snow distribution in mountain regions. To investigate the wind transport factor, a wind redistribution model based on terrain characteristics is implemented for a 1 km2 wind-affected sub region where high resolution snow depths have been supplied from three independent LiDAR flights taken during different winter seasons. The inter-annual consistency of snow depths at the site reveals a close correlation with the terrain parameters produced by the wind model for a known local prevailing wind direction. LiDAR currently remains the highest resolution large extent method for measuring snow depth, even though it is extremely costly to perform frequently and is primarily used only at intensive research sites. To monitor temporal variations of snow depth over more than a point, simple time-lapse photography is a promising and efficient way to obtain information about snowpack evolution at the slope scale. A robust and low power method to measure hourly changes in snow depth was developed that involves only three primary components: (1) an inexpensive, off-the-shelf time-lapse camera, (2) a weatherproof external battery box and (3) an array of secured, brightly painted depth markers. The camera is calibrated at the marker locations and a pixel counting algorithm automatically distinguishes the snow surface at each marker location after the images are captured. Results agreed closely with nearby standard ultrasonic depth sensors

Recommendations for aging beef (1993)

The main reason for aging beef is to improve tenderness and flavor of the meat so that if properly cooked it will be more satisfying to the consumer. Proper aging of beef results in a combination of changes that many people appreciate

Hummingbird flight

SummaryHummingbirds are very distinctive in their form and behavior, the evolution of which is tightly connected to the evolution of their primary source of energy — floral nectar. About forty million years ago, the practical use of this dense fuel, available only in widely-dispersed, insect-sized aliquots — it was originally intended for insect pollinators — presented a severe test to the avian bauplan. This selective pressure forced broad changes in form and function, affecting anatomical structures ranging from the feeding apparatus to the locomotor system. We describe here how these pressures shaped a bird that flies like a bird into one that flies like a fly

Hummingbird flight

Hummingbirds are very distinctive in their form and behavior, the evolution of which is tightly connected to the evolution of their primary source of energy — floral nectar. About forty million years ago, the practical use of this dense fuel, available only in widely-dispersed, insect-sized aliquots — it was originally intended for insect pollinators — presented a severe test to the avian bauplan. This selective pressure forced broad changes in form and function, affecting anatomical structures ranging from the feeding apparatus to the locomotor system. We describe here how these pressures shaped a bird that flies like a bird into one that flies like a fly.Organismic and Evolutionary Biolog

Operational Water Forecast Ability of the HRRR-iSnobal Combination: An Evaluation to Adapt into Production Environments

Operational water-resource forecasters, such as the Colorado Basin River Forecast Center (CBRFC) in the Western United States, currently rely on historical records to calibrate the temperature-index models used for snowmelt runoff predictions. This data dependence is increasingly challenged, with global and regional climatological factors changing the seasonal snowpack dynamics in mountain watersheds. To evaluate and improve the CBRFC modeling options, this work ran the physically based snow energy balance iSnobal model, forced with outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model across 4 years in a Colorado River Basin forecast region. Compared to in situ, remotely sensed, and the current operational CBRFC model data, the HRRR-iSnobal combination showed well-reconstructed snow depth patterns and magnitudes until peak accumulation. Once snowmelt set in, HRRR-iSnobal showed slower simulated snowmelt relative to observations, depleting snow on average up to 34 d later. The melting period is a critical component for water forecasting. Based on the results, there is a need for revised forcing data input preparation (shortwave radiation) required by iSnobal, which is a recommended future improvement to the model. Nevertheless, the presented performance and architecture make HRRR-iSnobal a promising combination for the CBRFC production needs, where there is a demonstrated change to the seasonal snow in the mountain ranges around the Colorado River Basin. The long-term goal is to introduce the HRRR-iSnobal combination in day-to-day CBRFC operations, and this work created the foundation to expand and evaluate larger CBRFC domains

Effects of Flight Speed upon Muscle Activity in Hummingbirds

Hummingbirds have the smallest body size and highest wingbeat frequencies of all flying vertebrates, so they represent one endpoint for evaluating the effects of body size on sustained muscle function and flight performance. Other bird species vary neuromuscular recruitment and contractile behavior to accomplish flight over a wide range of speeds, typically exhibiting a U-shaped curve with maxima at the slowest and fastest flight speeds. To test whether the high wingbeat frequencies and aerodynamically active upstroke of hummingbirds lead to different patterns, we flew rufous hummingbirds (Selasphorus rufus, 3 g body mass, 42 Hz wingbeat frequency) in a variable-speed wind tunnel $(\textrm{0–10 m s}^{-1})$. We measured neuromuscular activity in the pectoralis (PECT) and supracoracoideus (SUPRA) muscles using electromyography (EMG, $N=4$ birds), and we measured changes in PECT length using sonomicrometry ($N=1$). Differing markedly from the pattern in other birds, PECT deactivation occurred before the start of downstroke and the SUPRA was deactivated before the start of upstroke. The relative amplitude of EMG signal in the PECT and SUPRA varied according to a U-shaped curve with flight speed; additionally, the onset of SUPRA activity became relatively later in the wingbeat at intermediate flight speeds $(\textrm{4 and 6 m s}^{−1})$. Variation in the relative amplitude of EMG was comparable with that observed in other birds but the timing of muscle activity was different. These data indicate the high wingbeat frequency of hummingbirds limits the time available for flight muscle relaxation before the next half stroke of a wingbeat. Unlike in a previous study that reported single-twitch EMG signals in the PECT of hovering hummingbirds, across all flight speeds we observed 2.9±0.8 spikes per contraction in the PECT and 3.8±0.8 spikes per contraction in the SUPRA. Muscle strain in the PECT was 10.8±0.5%, the lowest reported for a flying bird, and average strain rate was 7.4±0.2 muscle lengths $s^{−1}$. Among species of birds, PECT strain scales proportional to body mass to the 0.2 power $(\infty M_b^{0.2})$ using species data and $\infty M_b^{0.3}$ using independent contrasts. This positive scaling is probably a physiological response to an adverse scaling of mass-specific power available for flight.Organismic and Evolutionary BiologyOther Research Uni