Infrastructure and Transportation Task Force Report, August 2008

The Rebuild Iowa Infrastructure and Transportation Task Force is acutely aware of the critical role infrastructure plays in Iowa’s communities, the lives of the residents, and the economic well-being of the state. With encouragement to the Rebuild Iowa Advisory Commission (RIAC) for its consideration of great need for infrastructure and transportation repairs, the Task Force provides its assessment and recommendations. As the RIAC fulfills its obligations to guide the recovery and reconstruction in Iowa, infrastructure and transportation must be recognized for its impact on all Iowans. The tornadoes, storms, and floods were devastating to infrastructure and transportation systems across the state. The damage did not distinguish between privately-owned and public assets. The significance of the damage emerges further with the magnitude of the damage estimates. Infrastructure includes components that some might initially overlook, such as communication systems, landfills, and water treatment. The miles of damaged roads and bridges are more evident to many Iowans. Given the reliance on infrastructure systems, many repairs are already underway, though gaps have emerged in the funding for repairs to certain infrastructure systems.

Department of Transportation Performance Plan, FY 2009

Agency Performance Plan

Iowa Communications Network Performance Plan, FY 2009

Agency Performance Plan, Iowa Communications Network

On bounds for network revenue management

The Network Revenue Management problem can be formulated as a stochastic dynamic programming problem (DP or the\optimal" solution V *) whose exact solution is computationally intractable. Consequently, a number of heuristics have been proposed in the literature, the most popular of which are the deterministic linear programming (DLP) model, and a simulation based method, the randomized linear programming (RLP) model. Both methods give upper bounds on the optimal solution value (DLP and PHLP respectively). These bounds are used to provide control values that can be used in practice to make accept/deny decisions for booking requests. Recently Adelman [1] and Topaloglu [18] have proposed alternate upper bounds, the affine relaxation (AR) bound and the Lagrangian relaxation (LR) bound respectively, and showed that their bounds are tighter than the DLP bound. Tight bounds are of great interest as it appears from empirical studies and practical experience that models that give tighter bounds also lead to better controls (better in the sense that they lead to more revenue). In this paper we give tightened versions of three bounds, calling themsAR (strong Affine Relaxation), sLR (strong Lagrangian Relaxation) and sPHLP (strong Perfect Hindsight LP), and show relations between them. Speciffically, we show that the sPHLP bound is tighter than sLR bound and sAR bound is tighter than the LR bound. The techniques for deriving the sLR and sPHLP bounds can potentially be applied to other instances of weakly-coupled dynamic programming.

European research funding and regional technological capabilities: Network composition analysis

We use network and correspondence analysis to describe the compositionof the research networks in the European BRITE--EURAM program. Our mainfinding is that 27\% of the participants in this program fall into one oftwo sets of highly ``interconnected'' institutions --one centered aroundlarge firms (with smaller firms and research centers providing specializedservices), and the other around universities--. Moreover, these ``hubs''are composed largely of institutions coming from the technologically mostadvanced regions of Europe. This is suggestive of the difficulties of attainingEuropean ``cohesion'', as technically advanced institutions naturally linkwith partners of similar technological capabilities.

An enhanced concave program relaxation for choice network revenue management

The network choice revenue management problem models customers as choosing from an offer-set, andthe firm decides the best subset to offer at any given moment to maximize expected revenue. The resultingdynamic program for the firm is intractable and approximated by a deterministic linear programcalled the CDLP which has an exponential number of columns. However, under the choice-set paradigmwhen the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has beenproposed but finding an entering column has been shown to be NP-hard. In this paper, starting with aconcave program formulation based on segment-level consideration sets called SDCP, we add a class ofconstraints called product constraints, that project onto subsets of intersections. In addition we proposea natural direct tightening of the SDCP called ?SDCP, and compare the performance of both methodson the benchmark data sets in the literature. Both the product constraints and the ?SDCP method arevery simple and easy to implement and are applicable to the case of overlapping segment considerationsets. In our computational testing on the benchmark data sets in the literature, SDCP with productconstraints achieves the CDLP value at a fraction of the CPU time taken by column generation and webelieve is a very promising approach for quickly approximating CDLP when segment consideration setsoverlap and the consideration sets themselves are relatively small.

Iowa Department of Transportation, Strategic Plan, 2008-2012, 2008

This report outlines the strategic plan for Department of Transportation, goals and mission.

Iowa Communications Network, Strategic Plan, 2007

This report outlines the strategic plan for Iowa Communications Network, goals and mission.

Fiscal Year 2008 Annual Report of the Iowa Communications Network, December 2008

This is the Annual Report for Fiscal Year 2007 (July 1, 2007-June 30, 2008) for the Iowa Communications Network.

Iowa Department of Transportation, Strategic Plan, 2008-2012,[revised, final], 2008

This report outlines the strategic plan for Department of Transportation, goals and mission.

Iowa Department of Transportation Office of Contracts Letting Summary - Fiscal 2008

Report of the letting activity of the Iowa Department of Transportation's Office of Contracts required by Iowa Code Section 307.12(15).

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Utilização da Virtual Private Network Caso da Universidade Jean Piaget de Cabo Verde

O trabalho científico realizado no âmbito dos regulamentos dos cursos de graduação para a obtenção de grau de licenciatura na Universidade Jean Piaget de Cabo Verde, pretende analisar a utilização da VPN, mais concretamente na Universidade Jean Piaget de Cabo Verde. Durante a elaboração desse trabalho será feito uma breve introdução sobre redes de computadores, sobre segurança na rede, um enquadramento teórico sobre VPN, bem como a sua contextualização num ambiente organizacional, mostrando as várias vantagens do seu uso, custo e beneficio e por fim o caso prático na Universidade Jean Piaget.

Red meat and cancer risk in a network of case-control studies focusing on cooking practices.

BACKGROUND: Consumption of red meat has been related to increased risk of several cancers. Cooking methods could modify the magnitude of this association, as production of chemicals depends on the temperature and duration of cooking. METHODS: We analyzed data from a network of case-control studies conducted in Italy and Switzerland between 1991 and 2009. The studies included 1465 oral and pharyngeal, 198 nasopharyngeal, 851 laryngeal, 505 esophageal, 230 stomach, 1463 colon, 927 rectal, 326 pancreatic, 3034 breast, 454 endometrial, 1031 ovarian, 1294 prostate and 767 renal cancer cases. Controls included 11 656 patients admitted for acute, non-neoplastic conditions. Odds ratios (ORs) and confidence intervals (CIs) were estimated by multiple logistic regression models, adjusted for known confounding factors. RESULTS: Daily intake of red meat was significantly associated with the risk of cancer of the oral cavity and pharynx (OR for increase of 50 g/day = 1.38; 95% CI: 1.26-1.52), nasopharynx (OR = 1.29; 95% CI: 1.04-1.60), larynx (OR = 1.46; 95% CI: 1.30-1.64), esophagus (OR = 1.46; 95% CI: 1.23-1.72), colon (OR = 1.17; 95% CI: 1.08-1.26), rectum (OR = 1.22; 95% CI:1.11-1.33), pancreas (OR = 1.51; 95% CI: 1.25-1.82), breast (OR = 1.12; 95% CI: 1.04-1.19), endometrium (OR = 1.30; 95% CI: 1.10-1.55) and ovary (OR = 1.29; 95% CI: 1.16-1.43). Fried meat was associated with a higher risk of cancer of oral cavity and pharynx (OR = 2.80; 95% CI: 2.02-3.89) and esophagus (OR = 4.52; 95% CI: 2.50-8.18). Risk of prostate cancer increased for meat cooked by roasting/grilling (OR = 1.31; 95% CI: 1.12-1.54). No heterogeneity according to cooking methods emerged for other cancers. Nonetheless, significant associations with boiled/stewed meat also emerged for cancer of the nasopharynx (OR = 1.97; 95% CI: 1.30-3.00) and stomach (OR = 1.86; 95% CI: 1.20-2.87). CONCLUSIONS: Our analysis confirmed red meat consumption as a risk factor for several cancer sites, with a limited impact of cooking methods. These findings, thus, call for a limitation of its consumption in populations of Western countries.

Discovering Historic Iowa Transportation Milestones, February 1999

In studying and forming an understanding of Iowa’s transportation history, we must surely develop a reverence for the lifestyles which preceded ours. Achieving a greater understanding of our past, we are better prepared to plan our future as we say farewell to the 20th century and move forward into the 21st. In the words of Oliver Wendell Holmes: “I believe the greatest thing in this world is not so much where we stand as in what direction we are moving... and the further backward you can look, the farther forward you can see.” To help Iowans gain a better understanding of our state’s rich transportation past, the Iowa Department of Transportation has produced a collection of materials including this booklet, the 1999-2000 Iowa Transportation Map, a video entitled A History of Iowa’s Rivers, Roads, Rails and Runways, and a traveling photo exhibit. In addition, Iowa is fortunate to have many local organizations, and the state-owned museum and historic sites, working to preserve the history of our state. Listed in the back section of this booklet are the addresses and descriptions of many sites that offer visitors an opportunity to see a wide range of transportation- related artifacts. The information and photographs are organized in time sequence. However, you will note that many of the transportation eras overlap. The booklet begins with Iowa’s pre-settlement era and concludes with the historic highway and transit program that was signed into law by President Clinton in 1998. As you can imagine, condensing this much Iowa history into a booklet-sized resource was a monumental challenge. Making selections and abbreviating the information resulted in some difficult editorial choices. However, we hope this publication will inspire readers to learn more about our rich, rewarding past and to visit and experience some of Iowa’s historic sites.