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Experiments show that manipulating the pulse sequence during c-HiPIMS, i.e. the tμon and tμoff times (explained in the glossary) allows for an increase of the deposition rate; increases of up to 150% are reported here for selected conditions.
For selected conditions, ignition delay simulations were conducted to highlight and discuss the deficiencies of the literature models and the potential areas for model improvements, especially at low temperatures.
Instead, we performed duplicate experiments for selected conditions.
* Patients who were hospitalized for >2 days for selected conditions.
Table 2 gives the constraints on the parameters and optimal parameter values for selected conditions.
Many tools have been developed for services for selected conditions delivered within OPDs, including malaria, STIs, and TB.
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We also contacted national experts for the selected conditions, for validation of our data and asked them to give their own estimates based on their experience in the various fields.
Logistic regression models were used to estimate predicted probabilities for the occurrence of an inpatient hospital stay for the selected conditions or death for individual patients.
For example, the overall rate of hospitalisation for the selected conditions or death in 2012 was 4.2%.
For the selected conditions, pooled unadjusted mean physical component summary scores were: 44.4 for hypertension, 38.9 for ischaemic heart disease, 35.9 for heart failure, 39.5 for lower back pain, 36.0 for osteoarthritis and 36.5 for rheumatoid arthritis.
Our results show that for the selected conditions, and uncertainty ranges, the surface nitridation reaction probability has the strongest impact on the model outcomes.
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