Manufacturing of a Burner Plate by Diffusion Bonding to Investigate Premixed Fuel‐Rich Oxy‐Fuel Flames at Increased Pressure and Preheating

Combustion of hydrocarbons with pure oxygen as oxidizer is used, e.g., in high-temperature processes such as the partial oxidation (POX) of hydrocarbons to produce synthesis gas of high purity. Due to the prevailing temperatures, active cooling is required for many parts. For laboratory-scale experiments, the dimensions of key parts are too small for conventional manufacturing processes. One example is the manufacturing of a burner plate especially developed for POX processes. The complex geometries of several hundreds of burner nozzles and perpendicular cooling channels across the diameter of the burner plate cannot be manufactured in a conventional way. For this burner, the advantage of chemical etching of thin sheet material and stacking of multiple sheet layouts was used to assemble the layout of the burner. The burner plate was then diffusion-bonded, allowing the complex design to be realized. The partial oxidation of CH$_4$/O$_2$ flames at the laboratory scale could thus be studied under industrially relevant conditions

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

<p>Abstract</p> <p>Background</p> <p>Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).</p> <p>Methods</p> <p>A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.</p> <p>Results</p> <p>The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.</p> <p>Conclusions</p> <p>Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.</p