In this research, a methodology consisting of an X-ray tube, one Pyrex-glass pipe, and two NaI detectors was investigated to determine the type of flow regimes and volume fractions of gas-oil-water three-phase flows. Three prevalent flow patterns—namely annular, stratified, and homogenous—in various volume percentages—10% to 80% with the step of 10%—were simulated by MCNP-X code. After simulating all the states and collecting the signals, the Fast Fourier Transform (FFT) was used to convert the data to the frequency domain. The first and second dominant frequency amplitudes were extracted to be used as the inputs of neural networks. Three Radial Basis Function Neural Networks (RBFNN) were trained for determining the type of flow regimes and predicting gas and water volume fractions. The correct detection of all flow regimes and the determination of volume percentages with a Mean Relative Error (MRE) of less than 2.02% shows that the use of frequency characteristics in determining these important parameters can be very effective. Although X-ray radiation-based two-phase flowmeters have a lot of advantages over the radioisotope-based ones, they suffer from lower measurement accuracy. One reason might be that the X-ray multi-energy spectrum recorded in the detector has been analyzed in a simple way. It is worth mentioning that the X-ray sources generate multi-energy photons despite radioisotopes that generate single energy photons, therefore data analyzing of radioisotope sources would be easier than X-ray ones. As mentioned, one of the problems researchers have encountered is the lower measurement accuracy of the X-ray, radiation-based three-phase flowmeters. The aim of the present work is to resolve this problem by improving the precision of the X-ray, radiation-based three-phase flowmeter using artificial neural network (ANN) and feature extraction techniques.

The rapid spread of the COVID-19 virus and the massive influx of patients exceeded the intensive care units capacity limit. Hospitals have engaged community partners to request resources or transfer patients to facilities with available resources. This setting fosters the need for rationing decisions to allocate scarce resources consistently, rather than basing decisions on clinicians’ intuition in the heat of the moment. In this paper, we identify the inter-hospital COVID-19 patients’ transfer criteria, and we apply a multi-criteria decision-making methodology to classify and rank these criteria in order to determine the transfer priority scores between hospitals. We then develop an optimisation model for patient systematic referral from saturated to recipient hospitals with contingency capacity expansion and a ventilator donation scheme. To deal with the uncertainty related to the number of ill patients requesting beds in hospitals, a two-stage stochastic model is presented and solved using the sample average approximation. A case with real data related to the COVID-19 French context is presented, which allows us to assess the resilience of the hospital network under the proposed solutions. The results show how local and regional coalitions can avert the saturation of the hospital system and reduce the number of rejected patients.

Currently, health care providers are competing in a consumer market under financial pressure and resources shortage. Thus, hospitals begin to form networks and develop more logistic cooperation between them in order to maximize the output/ throughput. In this paper, we study the case of the centralization of medical supplies in a French health care network. We analyze the potential gains of such cooperative scheme through multi criteria optimization approach associated to an integrated inventory- location- allocation problem. A procedure of three steps is suggested to solve the whole problem.

The work presented in this paper intends to show the impact of collaborative partnerships between the hospitals on the improvement of the healthcare supply chains configurations. In particular, we present an approach that support the implementation of new alternatives for inventory management ranging from the inventory centralization to the emergency lateral transshipment practices within a group of nearby hospitals. This approach was developed around a simulation-based optimization model in order to assess the cost savings and the operational efficiency of these collaborative schemes. The flexibility of our model allows its application to supply chains with different topologies, atypical cost characteristics and various rules for inventory sharing.