Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand

The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.

Targeting the EP1 receptor reduces Fas ligand expression and increases the antitumor immune response in an in vivo model of colon cancer

Despite studies demonstrating that inhibition of cyclooxygenase-2 (COX-2)-derived prostaglandin E2 (PGE2) has significant chemotherapeutic benefits in vitro and in vivo, inhibition of COX enzymes is associated with serious gastrointestinal and cardiovascular side effects, limiting the clinical utility of these drugs. PGE2 signals through four different receptors (EP1–EP4) and targeting individual receptor(s) may avoid these side effects, while retaining significant anticancer benefits. Here, we show that targeted inhibition of the EP1 receptor in the tumor cells and the tumor microenvironment resulted in the significant inhibition of tumor growth in vivo. Both dietary administration and direct injection of the EP1 receptor-specific antagonist, ONO-8713, effectively reduced the growth of established CT26 tumors in BALB/c mice, with suppression of the EP1 receptor in the tumor cells alone less effective in reducing tumor growth. This antitumor effect was associated with reduced Fas ligand expression and attenuated tumor-induced immune suppression. In particular, tumor infiltration by CD4+CD25+Foxp3+ regulatory T cells was decreased, whereas the cytotoxic activity of isolated splenocytes against CT26 cells was increased. F4/80+ macrophage infiltration was also decreased; however, there was no change in macrophage phenotype. These findings suggest that the EP1 receptor represents a potential target for the treatment of colon cancer.