Nearing Retirement, July 2008

IPERS provides the security you need through guaranteed benefits. With IPERS, unlike other retirement plans, benefits aren’t tied to the performance of the stock market and you don’t need to be an experienced investor to make your retirement dreams a reality. Your IPERS benefits are only one part of your overall retirement savings. Your total retirement income will come from a combination of your IPERS benefits, social security, personal savings, and any other retirement plan benefits.

Nearing Retirement, July 2008

IPERS provides the security you need through guaranteed benefits. With IPERS, unlike other retirement plans, benefits aren’t tied to the performance of the stock market and you don’t need to be an experienced investor to make your retirement dreams a reality. Your IPERS benefits are only one part of your overall retirement savings. Your total retirement income will come from a combination of your IPERS benefits, social security, personal savings, and any other retirement plan benefits.

Issue review : IPERS retirement dividend payments.

The Iowa Public Employees' Retirement System, or IPERS, does not apply a traditional cost-of-living adjustment for retirement benefits. A retiree's monthly benefit payment is determined by a formula at the time of retirement and the amount does not change. Instead of adjusting the monthly benefit for inflation, the General Assembly creates two separate once-a-year payments for retirees, the November dividend for pre-1990 retirees and the favorable experience dividend, or FED for 1990 and later retirees. Available funding for the FED is estimated to be depleted within the next three years.

Regularization techniques for ill-posed inverse problems in data assimilation

Optimal state estimation from given observations of a dynamical system by data assimilation is generally an ill-posed inverse problem. In order to solve the problem, a standard Tikhonov, or L2, regularization is used, based on certain statistical assumptions on the errors in the data. The regularization term constrains the estimate of the state to remain close to a prior estimate. In the presence of model error, this approach does not capture the initial state of the system accurately, as the initial state estimate is derived by minimizing the average error between the model predictions and the observations over a time window. Here we examine an alternative L1 regularization technique that has proved valuable in image processing. We show that for examples of flow with sharp fronts and shocks, the L1 regularization technique performs more accurately than standard L2 regularization.

Recommendations relating to retirement and death benefit system of the University of Illinois.

Includes tipped in "Digest of legislative bill for University retirement system".

Affirmative action plan

Description based on: 2008/2009 ; title from cover.

Annual financial report summary for the fiscal year ended June 30 /

"Annual financial report summary [is] a synopsis of our comprehensive annual financial report."

Component unit annual financial report for the year ended ...

Report year ends June 30.

Panel.

Description based on: August 1990; title from caption.

Report on emerging investment managers /

Cover title.

Retired member guide, 1991.

Cover title.

Statute and rules /

Cover title.

Tax guide, 1991.

Cover title.

TRS report.

Title from caption.

Responsiveness of the frontal EMG for monitoring the sedation state of critically ill patients.

Background. Excessive sedation is associated with adverse patient outcomes during critical illness, and a validated monitoring technology could improve care. We developed a novel method, the responsiveness index (RI) of the frontal EMG. We compared RI data with Ramsay clinical sedation assessments in general and cardiac intensive care unit (ICU) patients. Methods. We developed the algorithm by iterative analysis of detailed observational data in 30 medical–surgical ICU patients and described its performance in this cohort and 15 patients recovering from scheduled cardiac surgery. Continuous EMG data were collected via frontal electrodes and RI data compared with modified Ramsay sedation state assessments recorded regularly by a blinded trained observer. RI performance was compared with EntropyTM across Ramsay categories to assess validity. Results. RI correlated well with the Ramsay category, especially for the cardiac surgery cohort (general ICU patients r¼0.55; cardiac surgery patients r¼0.85, both P,0.0001). Discrimination across all Ramsay categories was reasonable in the general ICU patient cohort [PK¼0.74 (SEM 0.02)] and excellent in the cardiac surgery cohort [PK¼0.92 (0.02)]. Discrimination between ‘lighter’ vs ‘deeper’ (Ramsay 1–3 vs 4–6) was good for general ICU patients [PK¼0.80 (0.02)] and excellent for cardiac surgery patients [PK¼0.96 (0.02)]. Performance was significantly better than EntropyTM. Examination of individual cases suggested good face validity. Conclusions. RI of the frontal EMG has promise as a continuous sedation state monitor in critically ill patients. Further investigation to determine its utility in ICU decision-making is warranted.