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This paper deals with the development of an empirical model to describe the dynamics of milk acidification process for the production of acid casein. The model is based on the dynamics of the pH profiles during the acidification process since pH is a good indicator of the status of the precipitation. Data from laboratory experiments has been used to identify the parameters in the proposed model. Calibration and validation results, with an independent data set, show that the model is able to predict accurately the pH at different temperatures and acid addition rates. Furthermore, the model has been used in a simulation study as an advisory tool to suggest acid addition, proving the parsimony of the model.

Preserving water quality along a river basin is a multi-factorial task that depends on the management of sewers, wastewater treatment plants (WWTPs), and the dynamics of the river. This paper presents a control strategy of a reduced integrated network of WWTPs that can be used as a reference point for designing, evaluating and performing more complex strategies. The reduced network considers that the outflow of the sewers is gathered in one point from which it is distributed to three WWTPs that can treat and/or by-pass the wastewater into the river. The control strategy makes use of Model Predictive Control (MPC) and global optimization tools, such as Particle Swarm Optimization (PSO). The single input is first divided into three via PSO (one inflow per treatment plant) with a further partition into six discharging parts (two per treatment plant). Two scenarios in rain period were evaluated to improve the quality of the river regarding the concentrations of ammonium and nitrites. Simulation results showed that the control strategy improved the mean and the transient conditions of both concentrations. Further studies about the stability of the interconnected system should be evaluated.

The integration of wind energy into the electrical grid is complex due to the high variability of wind fields when electricity should be available all time. In this context, accurate wind power forecasts have to be given 48 h before. However, the major difficulty is that wind power is highly nonlinear and non-stationary. This paper proposes a methodology to cope with these two issues by a two folded model. First, the time-series are decomposed into a low and high frequency components to deal with non stationarity. Second, the nonlinearities are accounted by regimes defined by wind direction. The model called D-TARX is compared with other models with only regimes, only decomposition, and none. Results show that our model outperforms other models according to statistical criteria. The methodology is straightforward while more work could be performed to continue towards accurate wind power forecasts.

In literature metabolic stoichiometric matrix reduction is based on convex analysis by choosing the greatest triangle. This paper proposes a new methodology for the reduction of metabolic networks based on the concept of convex hull by optimization methods. Different polygons are tested to conjointly minimize the squared error (convex hull - experimental data) and maximize the convex hull area in order to reduce the set of metabolic reactions involved in the model. The advantage of this method relies on its ability to select different geometries in a simple manner with the knowledge of the elementary modes. A cybernetic model implementing the proposed optimization method is tested with data for bioethanol production by Saccharomyces cerevisiaegrowing on four substrates. Parameter estimation and model validation allow comparing the performance of the chosen polygons for reduction of metabolic pathways.

(BJOG. 2019;126:1491–1497)The use of marijuana has grown among pregnant women in the United States, increasing 62% between 2002 and 2014. Yet, the literature is mixed regarding the impact of marijuana use on perinatal outcomes in this population. Some studies have shown an association between marijuana and adverse pregnancy events, while others have found no associations. Also, studies have largely relied on the patient self-reporting her marijuana use—a limitation that may skew results because some study participants may be misclassified as nonusers. This study aimed to evaluate the association between maternal marijuana use and a composite adverse pregnancy outcome.