This study identifies strategies for enhancing access to Illinois libraries' holdings, where the strategies ensure the best stewardship possible for state and federal grant dollars for library automation and resources.
Teen birth (TB) imposes serious health and economic burdens to both individuals and government. Various attempts have been made to overcome TB such as teen pregnancy prevention evidence-based programs. However, these programs might have declined teen birth rate (TBR), most of which do not address the influencing socio-economic factors linked to areas where teenagers live. This study is aimed at investigating socio-economic factors contributing to TB and identify their geographical disparities. The methodology was developed using the vulnerability theory to examine the complex relationship between TB and socio-economic factors. Principal Component Analysis (PCA) and Geographically Weighted Regression (GWR) were employed to analyze census data. Findings suggest that socio-economically disadvantaged minorities, including unemployed black and uneducated Hispanic, are more vulnerable to TB. Additionally, geographic locations of communities where such teenager live are recognized. The outcomes verified the utility of the vulnerability theory to predict the geographical locations of vulnerable teens that can be leveraged by policymakers to allocate more health resources and perform place-specific interventions to effectively reduce TBR.
Patent relating to methods for generating numerical values indicative of frequencies of selected features in objects, and a computer system implementing the method.
This document is the final report with the summary of the findings, conclusions, and recommendations for consideration by the Telecommunications Infrastructure Fund Board (TIFB) Library Working Group as part of the Needs Assessment Study of Texas Academic, Public and School Libraries. The report is the work of the Needs Assessment Study Team, Texas Center for Digital Knowledge at the University of North Texas. The study was conducted during the period of February through September 15, 2002. Funding for the study was provided by Telecommunications Infrastructure Fund Board
Evaluating the impact of learning from climate data, in addition to bridge data, on the performance of concrete deck condition rating prediction is critical for identifying the right data needed to enhance bridge maintenance decision making. Few studies have considered such an evaluation and utilized a small size of samples that prevent revealing the knowledge hidden within the big size of data. Although, such evaluation over big data seems quite necessary, class imbalance problem makes it challenging. To alleviate such a problem, five states, including Alabama, Iowa, New York, Pennsylvania, and South Carolina, were selected as the case study. Not only are the states located in three different climatically consistent regions defined by the National Ocean and Atmospheric Administration (NOAA), but also their concrete deck conditions ratings are somewhat balanced. To conduct the evaluation, this research developed the bridge data set pertaining to 56,288 bridges across the afore-mentioned states through employing the GIS technology. The bridge data set contains bridge data derived from National Bridge Inventory (NBI), and climate data derived from Parameter-elevation Relationships on Independent Slopes Model (PRISM) climate maps and NOAA. Then, two machine learning algorithms, including random forest and GBM, were trained - with and without climate data - and their prediction performances were compared. The results indicated that: (1) random forest outperforms GBM with an accuracy of 63.3%, and (2) the change in the prediction performance after further learning from climate data was marginal since the accuracy reached to 64.9%.
This white paper introduces evaluation concepts and describes outcome-based evaluation. The Institute for Museum and Library Services (IMLS) logic model is described in the context of developing a program evaluation plan. Four case studies illustrate how outputs, outcomes and indicators can be used to produce programs results.
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