Objective: To design and test a treatment regimen which is clinically responsive, readily available, cost effective, and applicable especially to children and women of child bearing age. Design Setting: A prospective ...Objective: To design and test a treatment regimen which is clinically responsive, readily available, cost effective, and applicable especially to children and women of child bearing age. Design Setting: A prospective cohort study. Setting: Two major postgraduate teaching hospitals: one in Tripoli, Libya and the other in Jeddah, Saudi Arabia. Participants: Fifty-seven patients with 79 keloids, referred from Plastic Surgery Units between April 1996 and January 2005. Main Outcome Measure: Degree of flattening of the keloidal lesion and symptomatic recovery. Results: Result of treatment has been analyzed using unified set criteria. Seventy-seven percent of this cohort had complete response. 19% of cases had partial response, 50% acknowledged the treatment outcome had been “satisfactory” and 44% had an “acceptable” outcome. There was no significant acute or delayed reaction. Conclusion: The technique appears universally adaptable, cost effective, and can safely be prescribed for children and women of child-bearing age. In spite of prolonged treatment course, compliance was excellent.展开更多
Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification...Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.展开更多
文摘Objective: To design and test a treatment regimen which is clinically responsive, readily available, cost effective, and applicable especially to children and women of child bearing age. Design Setting: A prospective cohort study. Setting: Two major postgraduate teaching hospitals: one in Tripoli, Libya and the other in Jeddah, Saudi Arabia. Participants: Fifty-seven patients with 79 keloids, referred from Plastic Surgery Units between April 1996 and January 2005. Main Outcome Measure: Degree of flattening of the keloidal lesion and symptomatic recovery. Results: Result of treatment has been analyzed using unified set criteria. Seventy-seven percent of this cohort had complete response. 19% of cases had partial response, 50% acknowledged the treatment outcome had been “satisfactory” and 44% had an “acceptable” outcome. There was no significant acute or delayed reaction. Conclusion: The technique appears universally adaptable, cost effective, and can safely be prescribed for children and women of child-bearing age. In spite of prolonged treatment course, compliance was excellent.
文摘Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.
