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INTRODUCTION The objectives of this work were to assess the clinical knowledge of clinicians in the accident and emergency (A&E) departments in England & Wales and evaluate the current trend for the acute management of radiologically normal, but clinically suspected, fractures of the scaphoid.SUBJECTS AND METHODS We conducted a telephone survey on 146 A&E senior house officers (SHOs) in 50 different hospitals. This survey assessed the clinicians' experience, their clinical and radiological diagnostic methods, and their initial treatment of suspected scaphoid fractures. RESULTS The majority (55.8%) of SHOs performed only one clinical test to diagnose suspected scaphoid fractures. Overall, 41% were unable to cite the number of the radiographic views taken and only 10% of departments have direct access to further radiological investigation. There is wide variation in the early treatment of this injury, with the scaphoid cast used most commonly (46%). The majority of SHOs (89%) were unable to describe the features of immobilisation. The mean follow-up period was 10 days, and 53% of cases were followed-up by the senior staff in A&E. Of SHOs, 54% were not aware of any local guidelines for the management of suspected scaphoid fractures in their departments, and 92% were not aware of the existence of the 1992 British Association for Accident and Emergency Medicine (BAEM) guidelines. CONCLUSIONS The clinical knowledge and the management of suspected scaphoid fractures in A&E are unsatisfactory. We, therefore, suggest that the dissemination of up-to-date guidelines could help to educate clinicians to provide better care to the patients.

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Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.