The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report December 2011

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report March 20, 2009

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report October 7, 2009

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report December 11, 2009

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report March 11, 2010

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report October 11, 2010

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report December 6, 2010

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report December 2012

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report December 7, 2001

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report December 2013

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report March 25, 2011

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

The Revenue Estimating Conference (REC) Estimate of General Fund Receipts Report December 12, 2013

State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

Shoulder pathology: estimating dominant upper-limb activity using 3D kinematic sensors.

Introduction. Quantification of daily upper-limb activity is a key determinant in evaluation of shoulder surgery. For a number of shoulder diseases, problem in performing daily activities have been expressed in terms of upper-limb usage and non-usage. Many instruments measure upper-limb movement but do not focus on the differentiations between the use of left or right shoulder. Several methods have been used to measure it using only accelerometers, pressure sensors or video-based analysis. However, there is no standard or widely used objective measure for upper-limb movement. We report here on an objective method to measure the movement of upper-limb and we examined the use of 3D accelerometers and 3D gyroscopes for that purpose. Methods. We studied 8 subjects with unilateral pathological shoulder (8 rotator cuff disease: 53 years old ± 8) and compared them to 18 control subjects (10 right handed, 8 left handed: 32 years old ± 8, younger than the patient group to be almost sure they don_t have any unrecognized shoulder pathology). The Simple Shoulder Test (SST) and Disabilities of the Arm and Shoulder Score (DASH) questionnaires were completed by each subject. Two modules with 3 miniature capacitive gyroscopes and 3 miniature accelerometers were fixed by a patch on the dorsal side of the distal humerus, and one module with 3 gyroscopes and 3 accelerometers were fixed on the thorax. The subject wore the system during one day (8 hours), at home or wherever he/she went. We used a technique based on the 3D acceleration and the 3D angular velocities from the modules attached on the humerus. Results. As expected, we observed that for the stand and sit postures the right side is more used than the left side for a healthy right-handed person(idem on the left side for a healthy left-handed person). Subjects used their dominant upper-limb 18% more than the non-dominant upper-limb. The measurements on patients in daily life have shown that the patient has used more his non affected and non dominant side during daily activity if the dominant side = affected shoulder. If the dominant side affected shoulder, the difference can be showed only during walking period. Discussion-Conclusion. The technique developed and used allowed the quantification of the difference between dominant and non dominant side, affected and unaffected upper-limb activity. These results were encouraging for future evaluation of patients with shoulder injuries, before and after surgery. The feasibility and patient acceptability of the method using body fixed sensors for ambulatory evaluation of upper limbs kinematics was shown.

Simulated-annealing-based conditional simulation for the local-scale characterization of heterogeneous aquifers

Simulated-annealing-based conditional simulations provide a flexible means of quantitatively integrating diverse types of subsurface data. Although such techniques are being increasingly used in hydrocarbon reservoir characterization studies, their potential in environmental, engineering and hydrological investigations is still largely unexploited. Here, we introduce a novel simulated annealing (SA) algorithm geared towards the integration of high-resolution geophysical and hydrological data which, compared to more conventional approaches, provides significant advancements in the way that large-scale structural information in the geophysical data is accounted for. Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data. This is the only place where geophysical information is utilized in our algorithm, which is in marked contrast to other approaches where model perturbations are made through the swapping of values in the simulation grid and agreement with soft data is enforced through a correlation coefficient constraint. Another major feature of our algorithm is the way in which available geostatistical information is utilized. Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information. Thus we allow the larger-scale subsurface features resolved by the geophysical data to have much more due control on the output realizations. Further, since the only component of the SA objective function required in our approach is a covariance constraint at small lags, our method has improved convergence and computational efficiency over more traditional methods. Here, we present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on a synthetic data set, and then applied to data collected at the Boise Hydrogeophysical Research Site.

Estimating traveltimes and groundwater flow patterns using 3D time-lapse crosshole ERT imaging of electrical resistivity fluctuations induced by infiltrating river water

The infiltration of river water into aquifers is of high relevance to drinking-water production and is a key driver of biogeochemical processes in the hyporheic and riparian zone, but the distribution and quantification of the infiltrating water are difficult to determine using conventional hydrological methods (e.g., borehole logging and tracer tests). By time-lapse inverting crosshole ERT (electrical resistivity tomography) monitoring data, we imaged groundwater flow patterns driven by river water infiltrating a perialpine gravel aquifer in northeastern Switzerland. This was possible because the electrical resistivity of the infiltrating water changed during rainfall-runoff events. Our time-lapse resistivity models indicated rather complex flow patterns as a result of spatially heterogeneous bank filtration and aquifer heterogeneity. The upper part of the aquifer was most affected by the river infiltrate, and the highest groundwater velocities and possible preferential flow occurred at shallow to intermediate depths. Time series of the reconstructed resistivity models matched groundwater electrical resistivity data recorded on borehole loggers in the upper and middle parts of the aquifer, whereas the resistivity models displayed smaller variations and delayed responses with respect to the logging data. in the lower part. This study demonstrated that crosshole ERT monitoring of natural electrical resistivity variations of river infiltrate could be used to image and quantify 3D bank filtration and aquifer dynamics at a high spatial resolution.