Gasoline Prices: Cyclical Trends and Market Developments

Gasoline prices experience volatility often credited to fluctuations in the crude oil market, but gasoline is subject to its own supply and demand pressures. Cyclical trends such as seasonal changes in refining costs, production adjustments, and changes in demand contribute to gasoline price movements over a typical year. Recently, however, market developments not influenced by seasonal fluctuations have affected prices. From 2010 to 2014, increased access to cost-advantaged domestic sources of crude oil has expanded domestic gasoline production, and evolving consumption patterns in the United States and abroad have altered both import and export demand. Between January 2005 and September 2008, the producer price index for gasoline trended generally higher. (See chart 1.) The onset of the Great Recession pressured producer prices lower in the fourth quarter of 2008, a 67.8-percent drop, before prices started to rebound in early 2009. By mid-2011, prices reached prerecession levels and remained in a tight range before dropping more than 50 percent in the latter half of 2014 and early 2015. This Beyond the Numbers article examines the many factors that contributed to shifting producer gasoline prices from 2005 through 2014

Global and radial variations in the efficiency of massive star formation among galaxies

In order to determine the regions within galaxies which give rise to the most efficient star formation and to test the hypothesis that galaxies with high infrared luminosities per unit molecular mass are efficiently producing high mass stars, researchers have undertaken an H alpha imaging survey in galaxies whose CO distributions have been measured as part of the Five College Radio Astronomy Observatory (FCRAO) Extragalactic CO Survey. From these images researchers have derived global H alpha fluxes and distributions for comparison with far infrared radiation (FIR) fluxes and CO fluxes and distributions. Here, researchers present results on the global massive star formation efficiency (SFE = L sub H sub alpha/M(H2)) as a function of morphological type and environment, and on the radial distribution of the SFE within both peculiar and isolated galaxies. On the basis of comparison of the global L sub H sub alpha/M(H2) and L sub FIR/M(H2) for 111 galaxies, researchers conclude that environment rather than morphological type has the strongest effect on the global efficiency of massive star formation. Based on their study of a small sample, they find that the largest radial gradients are observed in the interacting/peculiar galaxies, indicating that environment affects the star formation efficiency within galaxies as well

Rising to the Challenge: The Strategies of Social Service Intermediaries

During the past decade, "intermediary organizations" have proliferated across the nonprofit sector. These organizations are typically positioned between funding entities (e.g., government agencies, foundations and corporations) and direct service providers. Intermediaries play an important roll in connecting organizations that share a common interest--and working to enhance the services these organizations provide, build larger service networks, promote quality standards, and monitor programs on behalf of funders

Optimal Clustering under Uncertainty

Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing a probabilistic framework based on the theory of random labeled point processes and characterizing a Bayes clusterer that minimizes the number of misclustered points. The Bayes clusterer is analogous to the Bayes classifier. Whereas determining a Bayes classifier requires full knowledge of the feature-label distribution, deriving a Bayes clusterer requires full knowledge of the point process. When uncertain of the point process, one would like to find a robust clusterer that is optimal over the uncertainty, just as one may find optimal robust classifiers with uncertain feature-label distributions. Herein, we derive an optimal robust clusterer by first finding an effective random point process that incorporates all randomness within its own probabilistic structure and from which a Bayes clusterer can be derived that provides an optimal robust clusterer relative to the uncertainty. This is analogous to the use of effective class-conditional distributions in robust classification. After evaluating the performance of robust clusterers in synthetic mixtures of Gaussians models, we apply the framework to granular imaging, where we make use of the asymptotic granulometric moment theory for granular images to relate robust clustering theory to the application.Comment: 19 pages, 5 eps figures, 1 tabl

Integrating Information Literacy into the Virtual University: A Course Model

published or submitted for publicatio

Multidimensional Recovery Among an Opioid Use Disorder Outpatient Treatment Population

Background: Given the current opioid crisis, recovery from opioid use disorder (OUD) warrants attention. SAMHSA’s working definition of recovery highlights dimensions that support recovery including health, home, community, and purpose. Recovery capital captures factors that support recovery within these dimensions and has been associated with recovery outcomes. Prior research highlights possible gender differences in recovery outcomes. Objective: 1) Describe and compare recovery capital among an OUD outpatient treatment population by gender; 2) Identify the relationship between recovery capital and length of time in treatment within this population. Methods: Patients (n=126) taking medication for OUD at a single outpatient substance use treatment clinic completed an electronic, cross-sectional survey (July-September 2019). The Brief Assessment of Recovery Capital (BARC-10) assessed recovery components. Length of current treatment episode was abstracted from Virginia’s Prescription Monitoring Program. Descriptive statistics were calculated. Chi square and Mann Whitney-U were used to test differences by gender. Multivariate linear regression was conducted. Results: Participants (n=126) were 45.3% men and 54.7% women. Most identified as Black (67.7%) and were single (69.0%). Compared to men, women were younger (38.8711.31 vs. 47.0712.12; p\u3c.001) and more likely to be unemployed (60.9% vs. 42.1%; p=.037). Mean BARC-10 score was 45.08 (9.73) and did not vary by gender. Several BARC-10 individual items within the purpose recovery dimension differed by gender (p\u3c.05). More social support was associated with higher BARC-10 score (p\u3c.001); length of treatment was not (p=.599). Conclusions: Recovery capital was high and gender differences minimal. Individuals receiving medication for OUD can initiate and sustain recovery.https://scholarscompass.vcu.edu/gradposters/1061/thumbnail.jp