What's the potential impact of casino tax increases on wagering handle: estimates of the price elasticity of demand for casino gaming.

This study estimates the price elasticity of demand for casino gaming. A demand model is estimated with data from a panel of 50 casinos operating in Illinois, Indiana, Iowa, and Missouri between 1991 and 2005. The model isolates the impact of changes in the casino win percentage or price on the wagering handle, controlling for the impact of other operating, economic, and regulatory determinants of the wagering handle. The model estimates suggest that the wagering handle in the short run is inelastic to price changes, and that in the long run the wagering handle is unit elastic if not somewhat inelastic.Casinos

Soft and Hard Implant Fabrication Using 3D-Bioplotting TM

At the Freiburger Materialforschungszentrum we have developed a new process (3DBioplotting TM) that permits most kind of polymers and biopolymers to be used in 3D scaffold design, including hydrogels (e.g. collagen, agar), polymer melts (e.g. PLLA, PGA, PCl) and twocomponent systems (e.g. chitosan, fibrin). Cells can be incorporated within the construction process, making this an ideal Rapid Prototyping technique for Organ Printing. Tailor-made biodegradable soft or hard scaffolds can so be fabricated in a short time using individual computer-tomography data from the patient. In-vitro tests showed promising results and in-vivo experiments are now under observation.Mechanical Engineerin

The efficacy of extended-release eprinomectin for the reduction of horn flies, face flies, and fecal egg counts of parasitic nematodes in replacement beef heifers

The purpose of this study was to evaluate the efficacy of extended-release eprinomectin against horn flies, face flies, and fecal egg counts of parasitic nematodes in crossbreed replacement beef heifers. Fifty-four heifers were randomly placed into three treatment groups (N=18 heifers/treatment). Group 1 was administered the labeled dosage of extended-release eprinomectin on day 0. Group 2 acted as the negative control. Group 3 received the anthelmintic injection once a quarter of the heifers in the group reached the threshold treatment level for horn flies (N=200 flies/animal; day 41). Nematode infections were measured via fecal egg counts while horn and face flies were visually monitored. Evaluation of pregnancy status was recorded at study conclusion (day 144). Due to the high number of face flies, heifers were treated with insecticide dusts twice during the study (day 54 and 69). Low egg counts for all treatment groups reflected minimal parasite burden. Extended-release eprinomectin had little influence on face flies given that all groups were above threshold at days 40, 48, and 54. At study conclusion, horn fly population was lower (P

Pilot Planning Grant

Report summarizing key findings of focus groups assessing Georgians' attitudes and opinions regarding the development of a plan for providing affordable insurance coverage statewide

Variable Powder Flow Rate Control in Laser Metal Deposition Processes

This paper proposes a novel technique, called Variable Powder Flow Rate Control (VPFRC), for the regulation of powder flow rate in laser metal deposition processes. The idea of VPFRC is to adjust the powder flow rate to maintain a uniform powder deposition per unit length even when disturbances occur (e.g., the motion system accelerates and decelerates). Dynamic models of the powder delivery system motor and the powder transport system (i.e., five–meter pipe, powder dispenser, and cladding head) are first constructed. A general tracking controller is then designed to track variable powder flow rate references. Since the powder flow rate at the nozzle exit cannot be directly measured, it is estimated using the powder transport system model. The input to this model is the DC motor rotation speed, which is estimated on–line using a Kalman filter. Experiments are conducted to examine the performance of the proposed control methodology. The experimental results demonstrate that VPFRC is successful in maintaining a uniform track morphology, even when the motion control system accelerates and decelerates.Mechanical Engineerin

Performance Comparison of Knowledge-Based Dose Prediction Techniques Based on Limited Patient Data.

PurposeThe accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and support vector regression based on limited patient training data.MethodsStatistical voxel dose learning, spectral regression, and support vector regression were used to predict the dose of noncoplanar intensity-modulated radiation therapy (4π) and volumetric-modulated arc therapy head and neck, 4π lung, and volumetric-modulated arc therapy prostate plans. Twenty cases of each site were used for k-fold cross-validation, with k = 4. Statistical voxel dose learning bins voxels according to their Euclidean distance to the planning target volume and uses the median to predict the dose of new voxels. Distance to the planning target volume, polynomial combinations of the distance components, planning target volume, and organ at risk volume were used as features for spectral regression and support vector regression. A total of 28 features were included. Principal component analysis was performed on the input features to test the effect of dimension reduction. For the coplanar volumetric-modulated arc therapy plans, separate models were trained for voxels within the same axial slice as planning target volume voxels and voxels outside the primary beam. The effect of training separate models for each organ at risk compared to all voxels collectively was also tested. The mean squared error was calculated to evaluate the voxel dose prediction accuracy.ResultsStatistical voxel dose learning using separate models for each organ at risk had the lowest root mean squared error for all sites and modalities: 3.91 Gy (head and neck 4π), 3.21 Gy (head and neck volumetric-modulated arc therapy), 2.49 Gy (lung 4π), and 2.35 Gy (prostate volumetric-modulated arc therapy). Compared to using the original features, principal component analysis reduced the 4π prediction error for head and neck spectral regression (-43.9%) and support vector regression (-42.8%) and lung support vector regression (-24.4%) predictions. Principal component analysis was more effective in using all/most of the possible principal components. Separate organ at risk models were more accurate than training on all organ at risk voxels in all cases.ConclusionCompared with more sophisticated parametric machine learning methods with dimension reduction, statistical voxel dose learning is more robust to patient variability and provides the most accurate dose prediction method

Historical epidemiology and the structural analysis of mortality

Attempts to explain long-term variations in pre-transitional Western European mortality in terms of changing living standards have met with little success, and this has led to the view that such variations were biologically, or climatically determined. This conclusion can, however, be avoided by a fuller specification of the determinants of exposure to infection that incorporates the dimensions of spatial structure. This paper advances a model of the proximate determinants of exposure and resistance to infection, and derives predictions for the mortality patterns of pretransitional metropolitan centres that are tested against data from London c1670–1830. The latter generally bear out the predictions of the model whilst also demonstrating the importance of certain features of England’s political economy over this period

Asymptotic expansions in the conditional central limit theorem

AbstractLet Xn, n∈N, be i.i.d. with mean 0, variance 1, and E(¦Xn¦r) < ∞ for some r ⩾ 3. Assume that Cramér's condition is fulfilled. We prove that the conditional probabilities P(1√n Σi = 1n Xi ⩽ t¦B) can be approximated by a modified Edgeworth expansion up to order o(1n(r − 2)2)), if the distances of the set B from the σ-fields σ(X1, …, Xn) are of order O(1n(r − 2)2)(lg n)β), where β < −(r − 2)2 for r∉N and β < −r2 for r∈N. An example shows that if we replace β < −(r − 2)2 by β = −(r − 2)2 for r∉N(β < −r2 by β = −r2 for r∈N) we can only obtain the approximation order O(1n(r − 2)2)) for r∉N(O(lg lgnn(r − 2)2)) for r∈N)