Platelet concentrates made from cell separators are used more frequently due to less donor exposure and leucodepletion. This retrospective study was done to compare plateletpheresis done on two cell separators: Baxter CS 3000 plus and Haemonetics MCS 3p. Plateletpheresis procedures, done from January 1997 to April 2002, were included in the study. One hundred and seven procedures were done on Haemonetics MCS 3p using SDP protocol, 49 procedures were done on Haemonetics MCS 3p using PLP protocol, and 107 were done on Baxter CS 3000 plus. Pre-procedure donor's platelet count and haemoglobin were comparable in all the groups. Platelet yield was comparable in PLP (6.44 x 10(11) platelets) and SDP (5.27 x 10(11)) protocols, but significantly less in Baxter (4.05 x 10(11) platelets, P < 0.001 for PLP and P < 0.05 for SDP). Efficiency of platelet removal was statistically significantly different in all the groups (P < 0.0001), however it was more in PLP (PLP-55.02%, SDP-47.38%, Baxter 38.98%). A significant number of products (19.51%) of Baxter failed to comply platelet count of product < or = 2,435 x 10(9)/l compared to 5.13% in PLP and 1.23% in SDP group; 36.96% of units from PLP and 28% from SDP qualified for split products compared to 1.18% of Baxter. PLP protocol of Haemonetics MCS 3p gives better platelet yield compared to Baxter CS 3000 plus and SDP protocol of Haemonetics MCS 3p.

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A program’s bug, fault, or mistake that results in unintended results is known as a software defect or fault. Software flaws are programming errors due to mistakes in the requirements, architecture, or source code. Finding and fixing bugs as soon as they arise is a crucial goal of software development that can be achieved in various ways. So, selecting a handful of optimal subsets of features from any dataset is a prime approach. Indirectly, the classification performance can be improved through the selection of features. A novel approach to feature selection (FS) has been developed, which incorporates the Golden Jackal Optimization (GJO) algorithm, a meta-heuristic optimization technique that draws on the hunting tactics of golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, and Naive Bayes, will aid in selecting a subset of relevant features from software fault prediction datasets. To evaluate the accuracy of this algorithm, we will compare its performance with other feature selection methods such as FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), and FSACO (Ant Colony Optimization). The result that we got from FSGJO is great for almost all the cases. For many of the results, FSGJO has given higher classification accuracy. By utilizing the Friedman and Holm tests, to determine statistical significance, the suggested strategy has been verified and found to be superior to prior methods in selecting an optimal set of attributes.