Issued Patents in a University’s Institutional Repository

Beginning in 2016, patents issued by the U.S. Patent and Trademark Office (USPTO) granted to Oklahoma State University were included in SHAREOK (https://shareok.org/.). The joint institutional repository for the Oklahoma State University Libraries (OSU) and the University of Oklahoma Libraries (OU), SHAREOK serves as the home for the intellectual output of both communities and will ultimately include digital dissertations, faculty publications, digital special collections, open access publications, and open educational resources. Including patents has increased the depth of the collection and allows them to be searched or indexed by date, author, title, and subject/classification. Using DSpace software, the contents of SHAREOK are crawled by Google, also the Library’s online catalog. Identifying patents by assignee or owner can only be done comprehensively using the Advanced Search feature in the USPTO’s Patent Full-Text and Image Database (PatFT) for issued patents or the Patent Application Full-Text and Image Database (AppFT) for pending patents or patent applications. In-house tools at the USPTO including PubWEST may also be used for comprehensive research. The following article gives an overview of patents as intellectual property and outlines the value of patents in the institutional repository, the process to identify the patents issued to Oklahoma State University over time, and the creation of the metadata structures to house the data in DSpace. Similar projects were accomplished at Clemson and Rice Universities. With various practices and software at each institution, there are no best practices at this time. Each project offers insights as to the possible ways of accomplishing similar results

Update on Geospatial Patterns of Antecedent Behavior among Perpetrators in the American Terrorism Study (ATS)

As part of the Terrorism and Extremist Violence in the United States (TEVUS) database integration effort, researchers at the Terrorism Research Center in Fulbright College at the University of Arkansas and the University of Oklahoma have been adding: 1) federal terrorism court cases and associated data and 2) incident and antecedent geospatial data from these court cases to the American Terrorism Study (ATS). The goal of the project is to examine geospatial patterns in perpetrator behavior and determine if the patterns identified in earlier studies have changed significantly. The ATS allows examination of a number of different units of analysis. Analyses may examine: (1) characteristics of federal terrorism court cases; (2) the characteristics of persons indicted in each court case or involved in incidents, otherwise referred to as indictees; (3) characteristics of incidents and planned incidents; and (4) antecedent activities that lead up to the incident and are necessary to carry it out and/or achieve the goals of the persons or groups

SPATIALLY EXPLICIT MODEL OF AREAS BETWEEN SUITABLE BLACK BEAR HABITAT IN EAST TEXAS AND BLACK BEAR POPULATIONS IN LOUISIANA, ARKANSAS, AND OKLAHOMA

Although black bears (Ursus americanus, Ursus americanus luteolus) were once found throughout the south-central United States, unregulated harvest and habitat loss resulted in severe range retractions and by the beginning of the twentieth century populations in Oklahoma, Louisiana, Texas and Arkansas were nearing extirpation. In response to these losses, translocation programs were initiated in Arkansas (1958-1968 & 2000-2006) and Louisiana (1964-1967 & 2001-2009). These programs successfully restored bears to portions of Louisiana and Arkansas, and, as populations in Arkansas began dispersing, to Oklahoma. In contrast, east Texas remains unoccupied despite the existence of suitable habitat in the region. To facilitate the establishment of a breeding population in east Texas, I sought to identify suitable habitat which bears could use for dispersal between known bear locations in Louisiana, Arkansas and Oklahoma and the east Texas recovery units. I utilized Maxent, a machine learning software, to model habitat suitability in this region. I collected known black bear presence locations (n=18,241) from state agencies in Louisiana, Oklahoma, Arkansas and east Texas and filtered them to reduce spatial autocorrelation (n=664). I also collected spatial data sets based on known black bear ecology to serve as environmental predictor variables. The model was developed at 30-m resolution and encompassed 417,076 km 2. The final model was selected to minimize model over-fitting while maintaining a high test Area Under the Receiver Operating Curve (AUC TEST)score. For final model interpretation and analysis, I used the 10th percentile training threshold available in Maxent which excludes the lowest 10% of predicted presence suitability scores from the binary predictive map, thus resulting in a more conservative predictive map. The final 10th percentile model predicted 43.7% of the pixels in the study area as suitable and 53.7 % percent of the pixels identified as potential recovery units by Kaminski et al. (2013, 2014) as suitable. To focus management efforts, I identified three movement zones with a high proportion of suitable habitat within which connectivity analyses were performed. Suitable patches greater than or equal to 12 km2 were classified within ArcGIS as stepping stone patches. Buffers of 3,500 m were generated around these patches to determine the level of functional connectivity in each zone. The final Maxent model confirmed that suitable bear habitat exists between source populations and the east Texas recovery units. The importance of percent of mast producing forest, percentage of cultivated crops and percentage of protected lands reflect what is known about basic bear biology and ecology. Furthermore, 153 stepping stone patches were identified within the movement zones, demonstrating that there is a reasonable chance of bears naturally dispersing to east Texas using the habitat identified in this study. Thus, protection of existing bear habitat and the stepping stone patches identified in this study should be a priority for managers seeking to facilitate natural bear recolonization of east Texas

DroughtScape- Fall 2011

Drought Preparedness Community Options Drought Likely to Persist, Intensify Across South Database Will Help Planners Find Options Impacts: Ag Losses, Fire, Water Restrictions NDMC Welcomes International Visitors New Drought Impact Reporter Online Sim-Drought, Available Now at Select Agencie

The Effect of Tort Reform on Tort Case Filings

Does so-called tort reform decrease tort case filings? In Texas and other states that have enacted numerous rounds of tort reform, the answer appears to be a resounding yes, at least as of the year 2000. More recent evidence from Oklahoma supports that conclusion and provides an interesting case study within the tort reform juggernaut. During at least the past twenty years, tort reformers have achieved substantial legislative successes and, some would argue, public relations victories. Yet their desire for more reform seems insatiable, and their legislative agenda rarely sleeps. Tort reform bills bloom perennially in the Oklahoma legislature, and numerous significant changes in liability rules, restrictions on remedies, and procedural innovations were enacted in 2002, 2003, and 2004. Despite their apparent success, tort reformers spun these victories as losses and vowed to press on. One omnibus tort reform bill passed by the legislature in 2007 was immediately vetoed by the governor. Its supporters, apparently undeterred, resurrected most of the provisions from the defeated 2007 bill and reintroduced them in 2008. One might reasonably ask whether anyone has stopped to see what, if anything, the enacted reforms have already wrought, before advocating even more sweeping changes. This article will make a small contribution toward answering that question

Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.Comment: 5th Geotechnical Earthquake Engineering and Soil Dynamics Conference, Austin, TX, USA, June 10-13. (2018

HELIN Consortium LORI Grant United States Online History_Digital Repository Sites

Description of state online history,digital repository sites, repared by: Emily Cuellar and Thomas Evans, Rhode Island State Library, July, 201

Biogeography of Endemic Dragonflies of the Ozark-Ouachita Interior Highlands

A common pattern across many taxonomic groups is that relatively few species are widespread while the majority are restricted in their geographic ranges. Such species distributions are used to inform conservation status, which poses unique challenges for rare or cryptic species. Further, priority status is often designated within geopolitical boundaries, which may include only a portion of a species range. This, coupled with lack of distributional data, has resulted in species being designated as apparently rare throughout some portions of their range, which may not accurately reflect their overall conservation need. The Interior Highlands region of the central United States harbors a rich diversity of flora and fauna, many of which are regional endemics. Among these are four dragonfly species considered Species of Greatest Conservation Need: Ouachita spiketail (Cordulegaster talaria), Ozark Emerald (Somatochlora ozarkensis), Westfall’s snaketail (Ophiogomphus westfalli), and Ozark clubtail (Gomphurus ozarkensis). I combined species distribution modeling with field surveys to better understand the current biogeography for the two species with ample presence data (S. ozarkensis and G. ozarkensis). Additionally, models were used to project species’ distributions under two climate change scenarios of differing severity. To assess reliability of model predictions, I used two machine learning algorithms commonly used with limited, presence-only data. Current areas of suitability predicted by both algorithms largely overlapped for each species. An analysis of variable contribution showed congruence in important environmental predictors between models. Field validation of these models resulted in new detections for both species showing their utility in guiding future surveys. Future projections across two climate change scenarios showed the importance of maintaining current suitable areas as these will continue to be strongholds for these species under climate change

The Geographic Distribution of US Executions

We review statistical patterns of the geographic distribution of US executions, compare them to homicides, and demonstrate extremely high degrees of concentration of executions in the modern period compared to previous historical periods. We further show that this unprecedented level of concentration has been increasing over the past 20 years. We demonstrate that it is virtually uncorrelated with factors related to homicides. Finally, we show that it corresponds to a statistical distribution associated with “self-reinforcing” processes: a power-law or exponential distribution. These findings stand whether we look at individual counties within death-penalty states, across the 50 states of the United States, or look at the international distribution of executions across countries in recent years. The substantive conclusion from the statistical patterns observed is that these cannot be explained merely by random variation around some general average. Rather, localities start down a path, then are reinforced in their pathways. There appears to be little to no logic about why certain counties are the high-use counties, whereas the vast majority have never executed a single individual in 40 years of experience with the modern death penalty, often in spite of thousands of homicides. Our research indicates that a main determinant of whether an individual will be executed is not the crime they commit, but the jurisdiction’s experience with executing others. This is not acceptable—legally, morally, or constitutionally

Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations

Cloud optical depth is one of the most poorly observed climate variables. The new “cloud mode” capability in the Aerosol Robotic Network (AERONET) will inexpensively yet dramatically increase cloud optical depth observations in both number and accuracy. Cloud mode optical depth retrievals from AERONET were evaluated at the Atmospheric Radiation Measurement program’s Oklahoma site in sky conditions ranging from broken clouds to overcast. For overcast cases, the 1.5 min average AERONET cloud mode optical depths agreed to within 15% of those from a standard ground‐based flux method. For broken cloud cases, AERONET retrievals also captured rapid variations detected by the microwave radiometer. For 3 year climatology derived from all nonprecipitating clouds, AERONET monthly mean cloud optical depths are generally larger than cloud radar retrievals because of the current cloud mode observation strategy that is biased toward measurements of optically thick clouds. This study has demonstrated a new way to enhance the existing AERONET infrastructure to observe cloud optical properties on a global scale