Quantifying and Forecasting Vulnerability to Dyadic Conflict in an Integrated Assessment Model: Modeling International Relations Theory

The character of state interaction matters. This dissertation quantifies this interaction from 1960-2001 and then forecasts it from 2010-2050. I contribute to the field of International Relations by improving traditional measures of Realism and Liberalism, quantifying new perspectives sensitive to cultural interaction, and statistically evaluating these indices relative to the occurrence of conflict. It is the first step in an academic research agenda that desires to expand the scope of possibility regarding the modeling of International Relations theory for the purpose of theory evaluation and policy analysis. This dissertation spans two fields of study that do not typically overlap: International Relations and Integrated Assessment Modeling. I begin by laying a broad foundation to bridge this chasm. I do this by first exploring knowledge constraints associated with forecasting. This leads to an overview of my conceptual and empirical tool for calibrating my final model: the historic occurrence of international conflict. Next, I introduce conceptual and applied systems theory, which leads to an overview of the International Futures (IFs) model. I then explore Liberalism and Realism as they have been traditionally operationalized at the macro-level. A newly quantified variable--referred to as the Cultures of Interaction Index-- is introduced that builds on Liberal notions and tries to explain some aspect of intersubjective norms and values operating in a dyad. I perform statistical analysis on these indices and show that using IR theories in conjunction explains more of the historic occurrence of conflict--and thus the character of state interaction--than using any theoretical tradition in isolation. I then endogenize Liberalism and Realism in IFs and use the cultural measure as an exogenous constant. I am interested in whether the stock of culture in a dyad and growth in Liberal notions of interdependence can off-set negative pressures arising from Realism. Most dyads improve their character of interaction to 2050, but some become more conflictual, including China - US and China - India. The analysis is extended by looking at long-term structural shifts in the global system: depleted fossil fuel reserves, stressed fresh water availability and tension from domestic instability. I conclude by offering a series of next steps that builds upon this work and recommendations for policy planners concerned with the future of interstate relations

Analysis of Suomi - NPP VIIRS Vignetting Functions Based on Yaw Maneuver Data

The Suomi NPP Visible Infrared Imager Radiometer Suite (VIIRS) reflective bands are calibrated on-orbit via reference to regular solar observations through a solar attenuation screen (SAS) and diffusely reflected off a Spectralon (Registered Trademark) panel. The degradation of the Spectralon panel BRDF due to UV exposure is tracked via a ratioing radiometer (SDSM) which compares near simultaneous observations of the panel with direct observations of the sun (through a separate attenuation screen). On-orbit, the vignetting functions of both attenuation screens are most easily measured when the satellite performs a series of yaw maneuvers over a short period of time (thereby covering the yearly angular variation of solar observations in a couple of days). Because the SAS is fixed, only the product of the screen transmission and the panel BRDF was measured. Moreover, this product was measured by both VIIRS detectors as well as the SDSM detectors (albeit at different reflectance angles off the Spectralon panel). The SDSM screen is also fixed; in this case, the screen transmission was measured directly. Corrections for instrument drift and degradation, solar geometry, and spectral effects were taken into consideration. The resulting vignetting functions were then compared to the pre-launch measurements as well as models based on screen geometry

Estimating changes in temperature extremes from millennial scale climate simulations using generalized extreme value (GEV) distributions

Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the CCSM3 in equilibrated pre-industrial and possible future conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that the magnitude of warm extremes largely shifts in accordance with mean shifts in summertime temperatures. In contrast, cold extremes warm more than mean shifts in wintertime temperatures, but changes in GEV location parameters are largely explainable by mean shifts combined with reduced wintertime temperature variability. In addition, changes in the spread and shape of the GEV distributions of cold extremes at inland locations can lead to discernible changes in tail behavior. We then examine uncertainties that result from using shorter model runs. In principle, the GEV distribution provides theoretical justification to predict infrequent events using time series shorter than the recurrence frequency of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series that are of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.Comment: 33 pages, 22 figures, 1 tabl

Sexual Minority and Heterosexual Parents Adopting Through the Child Welfare System: Challenges and Surprises During the Transition to Parenthood and Beyond

This session will address some of the unexpected challenges and surprises encountered by heterosexual, lesbian, gay, and bisexual individuals and couples who adopt through the child welfare system. Data will be presented regarding (a) the challenges that foster-to-adopt parents encounter post-placement, including: legal insecurity in their parental role, disorganization within social service systems, inadequate support services, and complex relationships with birth parents; (b) unmet expectations that foster-to-adopt parents often encounter with regard to the types of children that are placed in their home (e.g., in terms of age, race, gender, and special needs); and (c) the experiences of parents that have adopted via child welfare in terms of contact and openness with birth family members. Engaged and informal discussion of the implications of these findings for families and clinicians, as well as Q&A, will follow these brief research presentations

Requested, Recommended and Allowed Returns to Equity: Serendipity or Substance

R. Charles Moyer is Professor of Finance and Chairman of the Area of Finance in the College of Business Administration. Texas Tech University. Raymond E. Spudeck is an Assistant Professor in the Department of Finance, College of Commerce and lndlustry at the University of Wyoming. David B. Cox is an Instructor in the Area of Finance in the College of Business Administration at Texas Tech University

Assessing rotation-invariant feature classification for automated wildebeest population counts

Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future

Estimation of Diversity and Community Structure Through Restriction-Fragment-Length-Polymorphism Distribution Analysis of Bacterial 16s Ribosomal-RNA Genes from a Microbial Mat at an Active, Hydrothermal Vent System, Loihi Seamount, Hawaii

PCR was used to amplify (eu)bacterial small-subunit (16S) rRNA genes from total-community genomic DNA. The source of total-community genomic DNA used for this culture-independent analysis was the microbial mats from a deep-sea, hydrothermal vent system, Pele\u27s Vents, located at Loihi Seamount, Hawaii. Oligonucleotides complementary to conserved regions in the 16S rRNA-encoding DNA (rDNA) of bacteria were used to direct the synthesis of PCR products, which were then subcloned by blunt-end ligation into phagemid vector pBluescript II. Restriction fragment length polymorphism patterns, created by using tandem tetrameric restriction endonucleases, revealed the presence of 12 groups of 16S rRNA genes representing discrete operational taxonomic units (OTUs). The rank order abundance of these putative OTUs was measured, and the two most abundant OTUs accounted for 72.9% of all of the 16S rDNA clones. Among the remaining 27.1% of the 16S rDNA clones, none of the 10 OTUs was represented by more than three individual clones. The cumulative OTU distribution for 48 bacterial 16S rDNA clones demonstrated that the majority of taxa represented in the clone library were detected, a result which we assume to be an estimate of the diversity of bacteria in the native hydrothermal vent habitat. 16S rDNA fingerprinting of individual clones belonging to particular OTUs by using an oligonucleotide probe that binds to a universally conserved region of the 16S rDNA fragments was conducted to confirm OTU specificity and 16S rDNA identity