Audit report on the Student Health Facility Revenue Bond Funds of Iowa State University of Science and Technology for the year ended June 30, 2011

Audit report on the Student Health Facility Revenue Bond Funds of Iowa State University of Science and Technology for the year ended June 30, 2011

Agreed-upon procedures report on the Iowa State Center Business Office of Iowa State University of Science and Technology for the year ended June 30, 2011

Agreed-upon procedures report on the Iowa State Center Business Office of Iowa State University of Science and Technology for the year ended June 30, 2011

Audit report of the accompanying Schedule of Debt Service and Coverage for Iowa State University of Science and Technology as of February 10, 2012 for the Athletic Facilities Revenue Bond Funds

Audit report of the accompanying Schedule of Debt Service and Coverage for Iowa State University of Science and Technology as of February 10, 2012 for the Athletic Facilities Revenue Bond Funds

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Self-similarity of complex networks and hidden metric spaces

We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.

Report on a review of selected application controls over the Iowa State University of Science and Technology Facilities Planning and Management - Facilities Administrative Management Information System for the period of April 18, 2011 through May 16, 2011

Report on a review of selected application controls over the Iowa State University of Science and Technology Facilities Planning and Management - Facilities Administrative Management Information System for the period of April 18, 2011 through May 16, 2011

Report of Recommendations to Iowa State University of Science and Technology, Ames, Iowa, for the year ended June 30, 2011

Report on Iowa State University of Science and Technology, Ames, Iowa, for the year ended June 30, 2011

Report on a review of selected general and application controls over the Iowa State University of Science and Technology Room and Board System for the period of April 9, 2012 through May 1, 2012

Report on a review of selected general and application controls over the Iowa State University of Science and Technology Room and Board System for the period of April 9, 2012 through May 1, 2012

Audit report of Iowa State University of Science and Technology as of and for the year ended June 30, 2012

Audit report of Iowa State University of Science and Technology as of and for the year ended June 30, 2012

Report of Recommendations to Iowa State University of Science and Technology, Ames, Iowa, for the year ended June 30, 2012

Report on Iowa State University of Science and Technology, Ames, Iowa, for the year ended June 30, 2012

Audit report of the Schedule of Debt Service and Coverage for Iowa State University of Science and Technology for the Dormitory Revenue Refunding Bonds for the year ended June 30, 2012

Audit report of the Schedule of Debt Service and Coverage for Iowa State University of Science and Technology for the Dormitory Revenue Refunding Bonds for the year ended June 30, 2012

FY2011 Annual Report Research, Intelligent Transportation Systems, and Technology Transfer Activities

Transportation research makes a difference for Iowans and the nation. Implementation of cost-effective research projects contributes to a transportation network that is safer, more efficient, and longer lasting. Working in cooperation with our partners from universities, industry, other states, and FHWA, as well as participation in the Transportation Research Board (TRB), provides benefits for every facet of the DOT. This allows us to serve our communities and the traveling public more effectively. Pooled fund projects allow leveraging of funds for higher returns on investments. In 2011, Iowa led thirteen active pooled fund studies, participated in twenty-one others, and was wrapping-up, reconciling, and closing out an additional 6 Iowa Led pooled fund studies. In addition, non-pooled fund SPR projects included approximately 8 continued, 9 new, and over a dozen reoccurring initiatives such as the technical transfer/training program. Additional research is managed and conducted by the Office of Traffic and Safety and other departments in the Iowa DOT.

FY2012 Annual Report Research, Intelligent Transportation Systems, and Technology Transfer Activities

Transportation research makes a difference for Iowans and the nation. Implementation of cost-effective research projects contributes to a transportation network that is safer, more efficient, and longer lasting. Working in cooperation with our partners from universities, industry, other states, and FHWA, as well as participation in the Transportation Research Board (TRB), provides benefits for every facet of the DOT. This allows us to serve our communities and the traveling public more effectively. Pooled fund projects allow leveraging of funds for higher returns on investments. In 2011, Iowa led thirteen active pooled fund studies, participated in twenty-one others, and was wrapping-up, reconciling, and closing out an additional 6 Iowa Led pooled fund studies. In addition, non-pooled fund SPR projects included approximately 8 continued, 9 new, and over a dozen reoccurring initiatives such as the technical transfer/training program. Additional research is managed and conducted by the Office of Traffic and Safety and other departments in the Iowa DOT.

Association of T-zone reticular networks and conduits with ectopic lymphoid tissues in mice and humans.

Ectopic or tertiary lymphoid tissues (TLTs) are often induced at sites of chronic inflammation. They typically contain various hematopoietic cell types, high endothelial venules, and follicular dendritic cells; and are organized in lymph node-like structures. Although fibroblastic stromal cells may play a role in TLT induction and persistence, they have remained poorly defined. Herein, we report that TLTs arising during inflammation in mice and humans in a variety of tissues (eg, pancreas, kidney, liver, and salivary gland) contain stromal cell networks consisting of podoplanin(+) T-zone fibroblastic reticular cells (TRCs), distinct from follicular dendritic cells. Similar to lymph nodes, TRCs were present throughout T-cell-rich areas and had dendritic cells associated with them. They expressed lymphotoxin (LT) β receptor (LTβR), produced CCL21, and formed a functional conduit system. In rat insulin promoter-CXCL13-transgenic pancreas, the maintenance of TRC networks and conduits was partially dependent on LTβR and on lymphoid tissue inducer cells expressing LTβR ligands. In conclusion, TRCs and conduits are hallmarks of secondary lymphoid organs and of well-developed TLTs, in both mice and humans, and are likely to act as important scaffold and organizer cells of the T-cell-rich zone.