Report on a special investigation of the Engineering Communications and Marketing Department (ECM) of Iowa State University of Science and Technology for the period January 1, 2003 through December 31, 2007

Report on a special investigation of the Engineering Communications and Marketing Department (ECM) of Iowa State University of Science and Technology for the period January 1, 2003 through December 31, 2007

A finite-population revenue management model and a risk-ratio procedure for the joint estimation of population size and parameters

Many dynamic revenue management models divide the sale period into a finite number of periods T and assume, invoking a fine-enough grid of time, that each period sees at most one booking request. These Poisson-type assumptions restrict the variability of the demand in the model, but researchers and practitioners were willing to overlook this for the benefit of tractability of the models. In this paper, we criticize this model from another angle. Estimating the discrete finite-period model poses problems of indeterminacy and non-robustness: Arbitrarily fixing T leads to arbitrary control values and on the other hand estimating T from data adds an additional layer of indeterminacy. To counter this, we first propose an alternate finite-population model that avoids this problem of fixing T and allows a wider range of demand distributions, while retaining the useful marginal-value properties of the finite-period model. The finite-population model still requires jointly estimating market size and the parameters of the customer purchase model without observing no-purchases. Estimation of market-size when no-purchases are unobservable has rarely been attempted in the marketing or revenue management literature. Indeed, we point out that it is akin to the classical statistical problem of estimating the parameters of a binomial distribution with unknown population size and success probability, and hence likely to be challenging. However, when the purchase probabilities are given by a functional form such as a multinomial-logit model, we propose an estimation heuristic that exploits the specification of the functional form, the variety of the offer sets in a typical RM setting, and qualitative knowledge of arrival rates. Finally we perform simulations to show that the estimator is very promising in obtaining unbiased estimates of population size and the model parameters.

Audit report on Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Audit report on Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of Recommendations to Iowa State University of Science and Technology, Ames, Iowa, for the year ended June 30, 2008

Report on Iowa State University of Science and Technology, Ames, Iowa, for the year ended June 30, 2008

Schedule of Debt Service and Coverage for Iowa State University of Science and Technology for the Academic Building Revenue Bond Funds for the year ended June 30, 2009

Schedule of Debt Service and Coverage for Iowa State University of Science and Technology for the Academic Building Revenue Bond Funds for the year ended June 30, 2009

Audit report on the Student Health Facility Revenue Bond Funds of Iowa State University of Science and Technology for the year ended June 30, 2009

Audit report on the Student Health Facility Revenue Bond Funds of Iowa State University of Science and Technology for the year ended June 30, 2009

Audit report of Iowa State University of Science and Technology, Ames, Iowa, (Iowa State University) for the year ended June 30, 2009

Audit report of Iowa State University of Science and Technology, Ames, Iowa, (Iowa State University) for the year ended June 30, 2009

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, 2009

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, 2009

Electroencephalography (EEG) for neurological prognostication after cardiac arrest and targeted temperature management; rationale and study design.

BACKGROUND: Electroencephalography (EEG) is widely used to assess neurological prognosis in patients who are comatose after cardiac arrest, but its value is limited by varying definitions of pathological patterns and by inter-rater variability. The American Clinical Neurophysiology Society (ACNS) has recently proposed a standardized EEG-terminology for critical care to address these limitations. METHODS/DESIGN: In the TTM-trial, 399 post cardiac arrest patients who remained comatose after rewarming underwent a routine EEG. The presence of clinical seizures, use of sedatives and antiepileptic drugs during the EEG-registration were prospectively documented. DISCUSSION: A well-defined terminology for interpreting post cardiac arrest EEGs is critical for the use of EEG as a prognostic tool. TRIAL REGISTRATION: The TTM-trial is registered at ClinicalTrials.gov (NCT01020916).

Report on a review of selected general and application controls over the Iowa State University of Science and Technology (Iowa State University) Purchase Order/Requisition System for the period of March 20 through April 28, 2009

Report on a review of selected general and application controls over the Iowa State University of Science and Technology (Iowa State University) Purchase Order/Requisition System for the period of March 20 through April 28, 2009

Report of Recommendations to Iowa State University of Science and Technology, Ames, Iowa, for the year ended June 30, 2009

Report on Iowa State University of Science and Technology, Ames, Iowa, for the year ended June 30, 2009

Audit report on the Student Health Facility Revenue Bond Funds of Iowa State University of Science and Technology for the year ended June 30, 2010

Audit report on the Student Health Facility Revenue Bond Funds of Iowa State University of Science and Technology for the year ended June 30, 2010

State of genomics and epigenomics research in the perspective of HIV cure.

PURPOSE OF REVIEW: One of the seven key scientific priorities identified in the road map on HIV cure research is to 'determine the host mechanisms that control HIV replication in the absence of therapy'. This review summarizes the recent work in genomics and in epigenetic control of viral replication that is relevant for this mission. RECENT FINDINGS: New technologies allow the joint analysis of host and viral transcripts. They identify the patterns of antisense transcription of the viral genome and its role in gene regulation. High-throughput studies facilitate the assessment of integration at the genome scale. Integration site, orientation and host genomic context modulate the transcription and should also be assessed at the level of single cells. The various models of latency in primary cells can be followed using dynamic study designs to acquire transcriptome and proteome data of the process of entry, maintenance and reactivation of latency. Dynamic studies can be applied to the study of transcription factors and chromatin modifications in latency and upon reactivation. SUMMARY: The convergence of primary cell models of latency, new high-throughput quantitative technologies applied to the study of time series and the identification of compounds that reactivate viral transcription bring unprecedented precision to the study of viral latency.

Report on a review of selected general and application controls over the Iowa State University of Science and Technology Accounts Receivable System for the period of March 29, 2010 through May 6, 2010

Report on a review of selected general and application controls over the Iowa State University of Science and Technology Accounts Receivable System for the period of March 29, 2010 through May 6, 2010

Audit report of Iowa State University of Science and Technology, Ames, Iowa (Iowa State University) as of and for the years ended June 30, 2010 and 2009

Audit report of Iowa State University of Science and Technology, Ames, Iowa (Iowa State University) as of and for the years ended June 30, 2010 and 2009