The Fuzzy Economic Order Quantity Problem with a Finite Production Rate and Backorders

The track of developing Economic Order Quantity (EOQ) models with uncertainties described as fuzzy numbers has been very lucrative. In this paper, a fuzzy Economic Production Quantity (EPQ) model is developed to address a specific problem in a theoretical setting. Not only is the production time finite, but also backorders are allowed. The uncertainties, in the industrial context, come from the fact that the production availability is uncertain as well as the demand. These uncertainties will be handled with fuzzy numbers and the analytical solution to the optimization problem will be obtained. A theoretical example from the process industry is also given to illustrate the new model

Fuzzy linear programs with optimal tolerance levels

It is usually supposed that tolerance levels are determined by the decision maker a priori in a fuzzy linear program (FLP). In this paper we shall suppose that the decision maker does not care about the particular values of tolerance levels, but he wishes to minimize their weighted sum. This is a new statement of FLP, because here the tolerance levels are also treated as variables

Serum biomarkers of brain injury after uncomplicated cardiac surgery: Secondary analysis from a randomized trial

BACKGROUND: Postoperative cognitive dysfunction is common after cardiac surgery. Postoperative measurements of brain injury biomarkers may identify brain damage and predict cognitive dysfunction. We describe the release patterns of five brain injury markers in serum and plasma after uncomplicated cardiac surgery. METHODS: Sixty-one elective cardiac surgery patients were randomized to undergo surgery with either a dextran-based prime or a crystalloid prime. Blood samples were taken immediately before surgery, and 2 and 24 hours after surgery. Concentrations of the brain injury biomarkers S100B, glial fibrillary acidic protein (GFAP), tau, neurofilament light (NfL) and neuron-specific enolase (NSE)) and the blood-brain barrier injury marker β-trace protein were analyzed. Concentrations of brain injury biomarkers were correlated to patients' age, operation time, and degree of hemolysis. RESULTS: No significant difference in brain injury biomarkers was observed between the prime groups. All brain injury biomarkers increased significantly after surgery (tau +456% (25th-75th percentile 327%-702%), NfL +57% (28%-87%), S100B +1145% (783%-2158%), GFAP +17% (-3%-43%), NSE +168% (106%-228%), while β-trace protein was reduced (-11% (-17-3%). Tau, S100B and NSE peaked at 2h, NfL and GFAP at 24h. Postoperative concentrations of brain injury markers correlated to age, operation time, and/or hemolysis. CONCLUSION: Uncomplicated cardiac surgery with cardiopulmonary bypass is associated with an increase in serum/plasma levels of all the studied injury markers, without signs of blood-brain barrier injury. The biomarkers differ markedly in their levels of release and time course. Further investigations are required to study associations between perioperative release of biomarkers, postoperative cognitive function and clinical outcome

Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values

Problems with incomplete data and missing values are common and important in real-world machine learning scenarios, yet often underrepresented in the research field. Particularly data related to healthcare tends to feature missing values which must be handled properly, and ignoring any incomplete samples is not an acceptable solution. The Extreme Learning Machine has demonstrated excellent performance in a variety of machine learning tasks, including situations with missing values. In this paper, we present an application to predict the onset of Huntington’s disease several years in advance based on data from MRI brain scans. Experimental results show that such prediction is indeed realistic with reasonable accuracy, provided the missing values are handled with care. In particular, Multiple Imputation ELM achieves exceptional prediction accuracy