DO 690 John Wesley’s Theology Today

John Wesley’s Sermons: An Anthology. Ed. By Albert C. Outler and Richard P. Heitzenrater. Nashville: Abingdon Press, 1991. John Wesley. Ed. By Albert C. Outler. New York: Oxford University Press, 1964. John Wesley. A Plain Account of Christian Perfection. Kenneth J. Collins. John Wesley: A Theological Journey. Nashville: Abingdon Press, 2003. Kenneth J. Collins. The Theology of John Wesley: Holy Love and the Shape of Grace. Nashville: Abingdon Press, 2007. Howard A. Snyder. The Radical Wesley and Patterns for Church Renewal. Downers Grove, IL: InterVarsity Press, 1980. Those taking the course for 3 credits, add: Randy L. Maddox. Responsible Grace: John Wesley’s Practical Theology. Nashville: Kingswood Books, 1994.https://place.asburyseminary.edu/syllabi/1223/thumbnail.jp

Differing Death Scenarios: Self Esteem and Death Anxiety.

Previous research has found a correlation between death anxiety and self esteem. Researchers have found that both self-esteem and death anxiety play a significant role in an individual\u27s behavior. The purpose of this study was to investigate a correlation, if any, between death anxiety and self esteem using death related scenarios. It was hypothesized the high death anxiety groups will have lower self-esteem than the low death anxiety group, and that the low self-esteem group will have higher death anxiety than the high self-esteem group. Results of an ANOVA indicate that there is a significant difference between the high death anxiety group and low death anxiety group concerning self-esteem. The results also indicate that there is a significant difference between the low self-esteem group and the high self-esteem group concerning death anxiety. Overall it was found that there was significant negative correlation between death anxiety and self-esteem

Computational Discovery of Structured Non-coding RNA Motifs in Bacteria

This dissertation describes a range of computational efforts to discover novel structured non-coding RNA (ncRNA) motifs in bacteria and generate hypotheses regarding their potential functions. This includes an introductory description of key advances in comparative genomics and RNA structure prediction as well as some of the most commonly found ncRNA candidates. Beyond that, I describe efforts for the comprehensive discovery of ncRNA candidates in 25 bacterial genomes and a catalog of the various functions hypothesized for these new motifs. Finally, I describe the Discovery of Intergenic Motifs PipeLine (DIMPL) which is a new computational toolset that harnesses the power of support vector machine (SVM) classifiers to identify bacterial intergenic regions most likely to contain novel structured ncRNA and automates the bulk of the subsequent analysis steps required to predict function. In totality, the body of work will enable the large scale discovery of novel structured ncRNA motifs at a far greater pace than possible before

Why Dogs Stopped Flying: Poems

The solid rightness of image after image in Ken Brewer\u27s poetry was never better than in Why Dogs Stopped Flying. His familiar style is plain-spoken, his humor reliable and self-ironic. Yet, in this collection perhaps more than in his earlier work, the particularity of the poet\u27s insight into the physical world--and the warmth of his affection for it--combine to create an unexpected transcendence. Beasts and bodies are transformed in his lines, and our dim, unremarkable lives on this shadowed earth become somehow more luminous--small suns opening in the dark, small words to the moon.https://digitalcommons.usu.edu/usupress_pubs/1021/thumbnail.jp

Remote Sensing Applications to Support Sustainable Natural Resource Management

The original design of this dissertation project was relatively simple and straightforward. It was intended to produce one single, dynamic, classification and mapping system for existing vegetation that could rely on commonly available inventory and remote sensing data. This classification and mapping system was intended to provide the analytical basis for resource planning and management. The problems encountered during the first phase of the original design transformed this project into an extensive analysis of the nature of these problems and a decade-long remote sensing applications development endeavor. What evolved from this applications development process is a portion of what has become a system of systems to inform and support natural resource management. This dissertation presents the progression of work that sequentially developed a suite of remote sensing applications designed to address different aspects of the problems encountered with the original project. These remote sensing applications feature different resource issues, and resource components and are presented in separate chapters. Chapter one provides an introduction and description of the project evolution and chapter six provides a summary of the work and concluding discussion. Chapters two through five describe remote sensing applications that represent related, yet independent studies that are presented essentially as previously published. Chapter two evaluates different approaches to classifying and mapping fire severity using multi-temporal Landsat TM data. The recommended method currently represents the analytical basis for fire severity data produced by the USDA Forest Service and the US Geological Survey. Chapter three also uses multi-temporal Landsat data and compares quantitative, remote-sensing-based change detection methods for forest management related canopy change. The recommended method has been widely applied for a variety of forest health and disaster response applications. Chapter four presents a method for multi-source and multi-classifier regional land cover mapping that is currently incorporated in the USDA Forest Service Existing Vegetation Classification and Mapping Technical Guide. Chapter five presents a study using nearest neighbor imputation methods to generate geospatial data surfaces for simulation modeling of vegetation through time and space. While these results have not yet been successful enough to support widespread adoption and implementation, it is possible that these general methods can be adapted to perform adequately for simulation modeling data needs

Mapping of Forest Alliances and Associations Using Fuzzy Systems and Nearest Neighbor Classifiers

The study and management of biological communities depends on systems of classification and mapping for the organization and communication of resource information. Recent advances in remote sensing technology may enable the mapping forest plant associations using image classification techniques. But few areas outside Europe have alliances and associations described in detail sufficient to support remote sensing-based modeling. Northwestern Montana has one of the few plant association treatments in the United States compliant with the recently established National Vegetation Classification system. This project examined the feasibility of mapping forest plant associations using Landsat Enhanced Thematic Mapper data and advanced remote sensing technology and image classification techniques. Suitable reference data were selected from an extensive regional database of plot records. Fifteen percent of the plot samples were reserved for validation of map products, the remainder of plots designated as training data for map modeling. Key differentia for image classification were identified from a suite of spectral and biophysical variables. Fuzzy rules were formulated for partitioning physiognomic classes in the upper levels of our image classification hierarchy. Nearest neighbor classifiers were developed for classification of lower levels, the alliances and associations, where spectral and biophysical contrasts are less distinct. Maps were produced to reflect nine forest alliances and 24 associations across the study area. Error matrices were constructed for each map based on stratified random selections of map validation samples. Accuracy for the alliance map was estimated at 60%. Association classifiers provide between 54 and 86% accuracy within their respective alliances. Alternative techniques are proposed for aggregating classes and enhancing decision tree classifiers to model alliances and associations for interior forest types