An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

CMV colitis masquerading as colon cancer--an unusual presentation of acquired immunodeficiency syndrome

To determine whether there is any correlation between sudden decrease in barometric pressure and onset of labor, a non-experimental, retrospective study at a 948-bed tertiary care hospital was done. Pregnant patients of 36 weeks gestation or more who presented with spontaneous onset of labor during the 48 hours surrounding the 12 occurrences of significant drop in barometric pressure in 1992 were included in the study. Significantly more occurrences of onset of labor were identified in the 24 hours after a drop in barometric pressure than were identified in the 24 hours prior to the drop in barometric pressure (P < 0.05). Therefore, the overall number of labor onsets increased in the 24 hours following a significant drop in barometric pressure.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.     

A behavioral medicine intervention in persons with HIV

This study used a pretest-posttest preexperimental design to examine the effect of a 10-week behavioral medicine support group intervention in a sample of persons with HIV. Using Solomon's psychoneuroimmunologic framework, the 10-week behavioral medicine program focused on the mind/body interaction, the relaxation response, coping with illness, hardiness, and nutrition. Pearson correlation coefficients and t tests were performed on the pre- and postintervention measures of hardiness, social support, immune function, and perceived health status. Results of the study indicated that hardiness (preintervention) and CD4 counts (pre- and postintervention) were significantly correlated with health status; however, CD4 counts decreased over the course of the behavioral medicine program. Implications for nursing and recommendations for further research are discussed.     

Fetus-in-fetu with malignant recurrence

Fetus-in-fetu is an unusual condition in which a vertebrate fetus is enclosed within the abdomen of another fetus. These occurrences are usually benign. This report describes an instance of malignant recurrence after resection of a fetus-in-fetu.     

Effect of race on outcome after kidney and kidney-pancreas transplantation in type 1 diabetic patients

OBJECTIVE: The racial impact on graft outcome is not well defined in diabetic recipients. The purpose of this study is to analyze our experience with kidney-alone (A) and kidney-pancreas (KP) transplantation in type 1 diabetic recipients and evaluate the impact of racial disparity on outcome. RESEARCH DESIGN AND METHODS: The records of 217 kidney transplants (118 KA, 99 KP) performed on type 1 diabetic patients between 1985 and 1995 at the Medical University of South Carolina and the University of Texas Medical Branch were reviewed. RESULTS: A total of 53 (31%) white patients and 15 (33%) black patients experienced at least one episode of biopsy-proven acute rejection of the renal graft (NS). Patient survival at 1, 2, and 5 years was similar in white (92, 87, 69%) and black (91, 91, 69%) patients (NS). Kidney graft survival at 1, 2, and 5 years in the KA group was 72, 62, and 42% in blacks, compared with 79, 76, and 53% in whites (NS). Kidney graft survival at 1, 2, and 5 years in the KP group was 92, 92, and 74% in blacks, compared with 83, 77, and 58% in whites (NS). Pancreas graft survival at 1, 2, and 5 years was 81, 81, and 81% in blacks, compared with 81, 75, and 62% in whites (NS). Cox regression analysis revealed that donor age > or = 40 years increased the risk of renal graft failure 6.2-fold (P = 0.0001), whereas the addition of a pancreas transplant to a kidney and a living-related transplant decreased the risk of failure of the kidney graft 0.2 (P = 0.005) and 0.1 times (P = 0.005). CONCLUSIONS: Our results suggest that when compared with whites, there may be a trend toward an improved kidney and pancreas graft outcome in blacks undergoing KP transplants. These findings suggest that diabetes may override the risk factors that account for the pronounced disparity in outcome observed between nondiabetic white and black recipients.