Association of fever and severe clinical course in bronchiolitis

Little attention has been given to the relation between fever and the severity of bronchiolitis. Therefore, the relation between fever and the clinical course of 90 infants (59 boys, 31 girls) hospitalised during one season with bronchiolitis was studied prospectively. Fever (defined as a single recording > 38.0°C or two successive recording > 37.8°C) was present in 28 infants. These infants were older (mean age, 5.3 v 4.0 months), had a longer mean hospital stay (4.2 v2.7 days), and a more severe clinical course (71.0%v 29.0%) than those infants without fever. Radiological abnormalities (collapse/consolidation) were found in 60.7% of the febrile group compared with 14.8% of the afebrile infants. These results suggest that monitoring of body temperature is important in bronchiolitis and that fever is likely to be associated with a more severe clinical course and radiological abnormalities.

Application of regularization methods to damage detection in large scale plane frame structures using incomplete noisy modal data

This paper presents an approach for detecting local damage in large scale frame structures by utilizing regularization methods for ill-posed problems. A direct relationship between the change in stiffness caused by local damage and the measured modal data for the damaged structure is developed, based on the perturbation method for structural dynamic systems. Thus, the measured incomplete modal data can be directly adopted in damage identification without requiring model reduction techniques, and common regularization methods could be effectively employed to solve the developed equations. Damage indicators are appropriately chosen to reflect both the location and severity of local damage in individual components of frame structures such as in brace members and at beam-column joints. The Truncated Singular Value Decomposition solution incorporating the Generalized Cross Validation method is introduced to evaluate the damage indicators for the cases when realistic errors exist in modal data measurements. Results for a 16-story building model structure show that structural damage can be correctly identified at detailed level using only limited information on the measured noisy modal data for the damaged structure.