ABSTRACT
In this work, Ni−W/B nanocomposite coatings were successfully fabricated on low carbon steel by using pulse current (PC) electrodeposition. The effects of the frequency and duty cycle on the microstructure, wear resistance, and microhardness of the coatings were studied. The results obtained show that the distribution and content of boron particles (>4 wt.%) in the PC electrodeposition coatings are significantly better than those of direct current (DC) electrodeposition coatings (less than 4 wt.%). The hardness results reveal that the highest microhardness of 1122 HV can be obtained at a frequency of 100 Hz and duty cycle of 30%. Furthermore, the relationship between the microstructure and mechanical properties was discussed.
ABSTRACT
BACKGROUND: In the age of information superhighway, big data play a significant role in information processing, extractions, retrieving and management. In computational biology, the continuous challenge is to manage the biological data. Data mining techniques are sometimes imperfect for new space and time requirements. Thus, it is critical to process massive amounts of data to retrieve knowledge. The existing software and automated tools to handle big data sets are not sufficient. As a result, an expandable mining technique that enfolds the large storage and processing capability of distributed or parallel processing platforms is essential. METHOD: In this analysis, a contemporary distributed clustering methodology for imbalance data reduction using k-nearest neighbor (K-NN) classification approach has been introduced. The pivotal objective of this work is to illustrate real training data sets with reduced amount of elements or instances. These reduced amounts of data sets will ensure faster data classification and standard storage management with less sensitivity. However, general data reduction methods cannot manage very big data sets. To minimize these difficulties, a MapReduce-oriented framework is designed using various clusters of automated contents, comprising multiple algorithmic approaches. RESULTS: To test the proposed approach, a real DNA (deoxyribonucleic acid) dataset that consists of 90 million pairs has been used. The proposed model reduces the imbalance data sets from large-scale data sets without loss of its accuracy. CONCLUSIONS: The obtained results depict that MapReduce based K-NN classifier provided accurate results for big data of DNA.
Subject(s)
DNA/chemistry , Datasets as Topic , Algorithms , HumansABSTRACT
BACKGROUND: This prospective non-randomized clinical study was done to compare Off-pump and On-pump myocardial revascularization by Troponin I release in patients undergoing first elective coronary artery bypass graft used to evaluate myocardial injury. METHODS: One hundred an twenty patients were non-randomly assigned to a Off-pump or On-pump myocardial revascularization group. Cardiac Troponin I (CTnI) were measured in serial venous blood samples drawn preoperatively in both groups. In On-pump group after aortic unclamping at 12 and 24 hours and in Off-pump group after the last distal anastomosis at 12 and 24 hours. RESULTS: The total amount of CTnI release were significantly higher in On-pump group than in Off-pump group. In On-pump group it was 2.1 +/- 1.9 (mean +/- SD) ng/ml vs in Off-pump group it was 1.0 +/- 1.7 (mean +/- SD) ng/ml at 12 hours and in On-pump group it was 1.6 +/- 1.6 (mean +/- SD) ng/ml vs. in Off-pump group it was .9 +/- 1.6 (mean +/- SD) ng/ml at 24 hours (P < 0.0001 for the pattern). CONCLUSION: The lower release of CTnI in the Off-pump myocardial revascularization group indicates that the arrested heart coronary revascularization group causes more damage to the heart due to cardiopulmonary bypass than Off-pump myocardial revascularization group.
