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 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 Fiscal Year 2008 Report of Savings by Using Video Conferencing Through Iowa Communications Network to the Iowa General Assembly Pursuant to Iowa Code § II 84 Acts and Joint Resolutions Enacted at the 1994 Regular Session of the 75th General Assembly of the State of Iowa Iowa Code §8D.10 Report of Savings by State Agencies

Iowa Code § 8D.10 requires certain state agencies prepare an annual report to the General Assembly certifying the identified savings associated with that state agency’s use of the Iowa Communications Network (ICN). This report covers estimated cost savings related to video conferencing via ICN for the Iowa Department of Transportation (DOT). In FY 2008, the DOT did not conduct any sessions utilizing ICN’s video conferencing system. Therefore, no cost savings were calculated for this report.

Theimpact of industry collaboration on research: Evidence from engineering academics in the UK

We study the impact of university-industry research collaborations on academicoutput, in terms of productivity and direction of research. We report findings froma longitudinal dataset on all the researchers from the engineering departments inthe UK in the last 20 years. We control for the endogeneity caused by the dynamicnature of research and the existence of reverse causality. Our results indicate thatresearchers with industrial links publish significantly more. Productivity, though,is higher for low levels of industry involvement. Moreover, growing ties with theindustry skew research towards a more applied approach.

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.

Anorexie: évaluation et prise en charge somatiques [Anorexia: somatic assessment and management].

Anorexia nervosa, which affects about 2-3% of the general population, is the psychiatric illness with the highest rate of mortality. The management is often complex, requiring multiple stakeholders on the patient's physical and psychiatric. The new specialized centre "abC" (anorexia-bulimia, Centre vaudois) was created with the objective of providing quality services to patients involved and to provide a network facilitating the interaction between physicians and specialized institutions. This is an inter-institutional and interdisciplinary collaboration born of the CHUV and the eHnv (Hospitalized Institutions in Nord Vaudois). The abC includes an outpatient pole (CHUV) and a hospital unit on the site of Saint Loup. At term, it will include a day centre (CHUV).