Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications.

New process developments linked to Power to X (energy storage or energy conversion to another form of energy) require tools to perform process monitoring. The main gases involved in these types of processes are H2, CO, CH4, and CO2. Because of the non-selectivity of the sensors, a multi-sensor matrix has been built in this work based on commercial sensors having very different transduction principles, and, therefore, providing richer information. To treat the data provided by the sensor array and extract gas mixture composition (nature and concentration), linear (Multi Linear Regression-Ordinary Least Square "MLR-OLS" and Multi Linear Regression-Partial Least Square "MLR-PLS") and non-linear (Artificial Neural Network "ANN") models have been built. The MLR-OLS model was disqualified during the training phase since it did not show good results even in the training phase, which could not lead to effective predictions during the validation phase. Then, the performances of MLR-PLS and ANN were evaluated with validation data. Good concentration predictions were obtained in both cases for all the involved analytes. However, in the case of methane, better prediction performances were obtained with ANN, which is consistent with the fact that the MOX sensor's response to CH4 is logarithmic, whereas only linear sensor responses were obtained for the other analytes. Finally, prediction tests performed on one-year aged sensor platforms revealed that PLS model predictions on aged platforms mainly suffered from concentration offsets and that ANN predictions mainly suffered from a drop of sensitivity.

A cluster of three cases of botulism due to Clostridium baratii type F, France, August 2015.

A cluster of three cases of food-borne botulism due to Clostridium baratii type F occurred in France in August 2015. All cases required respiratory assistance. Consumption of a Bolognese sauce at the same restaurant was the likely source of contamination. Clostridium baratii was isolated both from stool specimens from the three patients and ground meat used to prepare the sauce. This is the second episode reported in France caused by this rare pathogen.

Inspiratory pressure-volume curves obtained using automated low constant flow inflation and automated occlusion methods in ARDS patients with a new device.

OBJECTIVE: To compare the inspiratory volume pressure (VP) curves of the respiratory system (rs) produced by static occlusion (OCC) and dynamic low constant flow inflation (LCFI) methods using a new device in acute respiratory distress syndrome (ARDS) patients. SETTING: A multidisciplinary 24-bed ICU in a tertiary university hospital. PATIENTS: Eleven intubated and mechanically ventilated patients with ARDS. MEASUREMENTS AND RESULTS: OCC and LCFI methods were performed using the same ventilator, which had been specifically implemented for this purpose. LCFI of 5, 10, and 15 l/min and OCC were applied in a random order at zero end-expiratory positive pressure. Airway pressure was measured both proximal (P(ao)) and distal (P(tr)) to the endotracheal tube. Lower inflection point (LIP) and maximal slope (C(max,rs)) were estimated using unbiased iterative linear regressions. LIP(rs) was obtained in all patients under LCFI and in nine patients under OCC. With LCFI of 5, 10, 15 l/min and OCC the average LIP(rs) values were 12.2 +/- 3.9, 12.9 +/- 4, 14.3 +/- 3.4, and 11.9 cm H(2)O for P(ao) and 11.9 +/- 3.9, 11.5 +/- 3.3, 12.5 +/- 3.4 and 11.8 +/- 4.4 for P(tr), respectively. Only the mean values of LIP(rs) for P(ao) with LCFI at 15 l/min were significantly different from those obtained for OCC. The C(max,rs) values found with the two methods were similar. CONCLUSIONS: An LCFI less than or equal to 10 l/min seems to be a quick, safe, and reliable method to determine LIP(rs) and C(max,rs) at the bedside.