Reliability and Validity of the Child Health QuestionnairePF-50 for European Children with Cerebral Palsy

Objective: To evaluate the psychometric performance of the Child Health Questionnaire (CHQ) in children with cerebral palsy (CP).
Method: 818 parents of children with CP, aged 8–12 from nine regions of Europe completed the CHQ (parent form 50 items). Functional abilities were classified using the five-level Gross Motor Function Classification Scheme (Levels I–III as ambulant; Level IV–V as nonambulant CP).
Results: Ceiling effects were observed for a number of subscales and summary scores across all Gross Motor Function Classification System levels, whilst floor effects occurred only in the physical functioning scale (Level V CP). Reliability was satisfactory overall. Confirmatory factor analysis (CFA) revealed a seven-factor structure for the total sample of children with CP but with different factor structures for ambulant and nonambulant children.
Conclusion: The CHQ has limited applicability in children with CP, although with judicious use of certain domains for ambulant and nonambulant children can provide useful and comparable data about child health status for descriptive purposes.

Room Temperature, Electrochemical Generation of Ozone with 50% Current Efficiency in 0.5M Sulfuric Acid at Cell Voltages <3V.

The electrochemical generation of ozone by Ni/Sb-SnO2 anodes immersed in 0.5M H2SO4 was assessed in both flow and recycle systems using the same electrochemical cell. The anodes were found to exhibit current efficiencies of up to 50% for ozone generation under flow conditions at room temperature, with an optimum mole ratio in the precursor solutions of ca. 500:8:3 Sn:Sb:Ni and optimum cell voltage of 2.7V. A comparison of the data obtained under flow and recycle conditions suggests that the presence of ozone in the anolyte inhibits its formation. The minimum electrical energy cost achieved, of 18 kWh kg1 compares favorably with estimated costs for Cold Corona Discharge generally reported in the literature, especially when the very significant advantages of electrochemical ozone generation are taken into account.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.