Hospitalization causes due to iron overload in beta-Thalassemia in Gorgan, Iran

Objective: To evaluate causes of hospitalization (due to complications of iron overload and other causes) in beta-Thalassemic patients. Methodology: This study was performed on 244 patients with major beta-Thalassemia admitted in Taleghani hospital of Gorgan between 2000 and 2007. Causes of hospitalization (due to complications of iron overload and other causes) were evaluated. Data were analyzed with SPSS software. Results: The most common causes of hospitalization due to iron overload were diabetes mellitus (31.6%) and heart failure (16.4%). The most common clinical findings were weakness and fatigue. Conclusion: We perceive increased frequency of diabetes mellitus in this center compared to other studies in Iran and abroad. Therefore glucose tolerance test and genotypic research for IVS II nt 745 are recommended in Thalassemic patient in this area

Machine learning-based prediction of a BOS reactor performance from operating parameters

A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors. View Full-Tex