Mixed methods and its application in prehospital research: a systematic review

The utilization of mixed methods in prehospital research is relatively new. Its use may enhance research findings, but it is not without its challenges. This study used online databases to systemically search for literature relating to the application of mixed methods in prehospital research, in order to understand the place of mixed methods research in the prehospital setting. The prehospital field appears to be embracing mixed methods as an approach to research due to its ability to address health care questions in complex, diverse environments. However, supplemental literature in this area is limited, with mixed methods expertise being found in other health science areas. Researchers should endeavor to continue to contribute to this area through high-quality, rigorous mixed methods studies.

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

We collaborate with environmental scientists to study the hydrodynamics and water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the complex urban landform created by surrounding buildings. In this work, we study an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir with a limited number of wind sensors. Unlike existing sensor placement solutions that assume Gaussian process of target phenomena, this study measures the wind which inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons which follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind in the presence of surrounding buildings. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir surface in real time. 10 wind sensors are finally deployed around or on the water surface of an urban reservoir. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction approach provides accurate wind measurement which outperforms the state-of-the-art Gaussian model based or interpolation based approaches.

Development and measurement properties of a putting skill test for high-level golf

Despite a recent increase in the amount of research investigating performance in golf, a comprehensive putting skill test has not been reported in the peer-reviewed literature. In this study, the Golf Australia Putting Test (GAPT) was developed and a series of measurement properties were assessed. Elite (n = 18) and high-level amateur (HLA; n = 22) participants completed six single putts from various areas on six concentric circles (circle radii = 0.9, 1.5, 3.0, 4.6, 6.1 and 7.6 m). Using a scoring system that rewarded participants for holing putts from longer distances, the maximum score from a single round of the test (i.e. 36 putts) was 27 points. After two rounds of the test were completed by all players, a subsample of participants (elite, n = 15; HLA, n = 7) had their putting performance recorded during tournament play for a period of 90 days to assess criterion (predictive) validity of the test. The reliability, sensitivity and discriminative validity of the GAPT were also assessed. Better agreement between Rounds 1 and 2 scores was noted in the elite group, whilst reliability values were similar for both groups. Further, the GAPT scores were shown to predict players from the elite and high-ability groups with a low classification error. An equation for predicting on-course performance from GAPT scores was also developed. Findings from this study indicate that the GAPT is a valid and reliable tool for high-level players and the GAPT may be used for player evaluation in the field.

Effective methods and strategies for massive small files processing based on Hadoop

The Hadoop framework provides a powerful way to handle Big Data. Since Hadoop has inherent defects of high memory overhead and low computing performance in processing massive small files, we implement three methods and propose two strategies for solving small files problem in this paper. First, we implement three methods, i.e., Hadoop Archives (HAR), Sequence Files (SF) and CombineFileInputFormat (CFIF), to compensate the existing defects of Hadoop. Moreover, we propose two strategies for meeting the actual needs of different users. Finally, we evaluate the efficiency of the implemented methods and the validity of the proposed strategies. The experimental results show that our methods and strategies can improve the efficiency of massive small files processing, thereby enhancing the overall performance of Hadoop. © 2014 ISSN 1881-803X.