Evidence-based clinical engineering : machine learning algorithms for prediction of defibrillator performance

Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performance accuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treatments. Even with the increase of technological sophistication of devices, incidents involving defibrillator malfunction are unfortunately not rare. To address this, we have developed an automated system based on machine learning algorithms that can predict performance of defibrillators and possible performance failures of the device which can affect performance. To develop an automated system, with high accuracy, overall dataset containing safety and performance measurements data was acquired from periodical safety and performance inspections of 1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public healthcare institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number of samples, 974 of them were used during system development and 247 samples were used for subsequent validation of system performance. During system development, 5 different machine learning algorithms were used, and resulting systems were compared by obtained performance

Sweet maize (Zea mays l. Saccharata) weeds infestation, yield and yield quality affected by different crop densities

Weeds are among main limiting factors in sweet maize production. Commercially grown sweet corn hybrids (Zea mays saccharata Sturt.) vary widely in competitive ability against weeds which interference differentially affects yield and ear traits important to processing and fresh markets. A total of 28 sweet corn hybrids of different FAO maturity groups have been developed at the Maize Research Institute, Zemun Polje, and released by the the Commission for the Variety Releasing. In order to obtain high yields of good quality the scientists have been searching for the most appropriate growing practices. Therefore the objective of the present study was to determine the effect of four plant densities (40 000, 50 000, 60 000, and 70 000 plants/ha) on the level of weed infestation, yield and shelling percentage of four sweet maize hybrids (ZP 424su, ZP 462su, ZP 504su, and ZP 521su) in 2008 and 2009. Total fresh weight and the total number of weeds decreased with increasing sowing density, which was more prominent in 2008. Results of the analysis of variance showed that investigated factors, year, sowing density and hybrid had significant influence on fresh ear yield and shelling percentage. Sowing density affected fresh ear yield in the way that the denser sowing was, the higher yield was. Hybrids ZP 424su and ZP 462su gave higher fresh ear yields than other two. However, hybrids with less fresh ear yield (ZP 504su and ZP 521su) gave better shelling percentage. These results suggest that ZP 424su and ZP 462su can be preferable for fresh consumption and ZP 504su and ZP 521su for processing