Classification of stars is essential to investigate the characteristics and behavior of stars. Performing classifications manually is error-prone and time-consuming. Machine learning provides a computerized solution to handle huge volumes of data with minimal human input. k-Nearest Neighbor (kNN) is one of the simplest supervised learning approaches in machine learning. This paper aims at studying and analyzing the performance of the kNN algorithm on the star dataset. In this paper, we have analyzed the accuracy of the kNN algorithm by considering various distance metrics and the range of k values. Minkowski, Euclidean, Manhattan, Chebyshev, Cosine, Jaccard, and Hamming distance were applied on kNN classifiers for different k values. It is observed that Cosine distance works better than the other distance metrics on star categorization.

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Background: Thromboembolism is one of the major complications associated with central venous catheters (CVC) which is sub-clinical most of the time, hence undiagnosed leading to various life-threatening complications. Studies on catheter related thrombosis (CRT) are conducted on patients with malignant conditions and seldom been done on diabetics and hypertensives who are more commonly encountered in day to day practice. The aim of our study was to determine the rate of occurrence of CRT formation in diabetics and hypertensives. Material and Methods: This prospective, observational, hospital-based study was conducted after obtaining clearance from institutional ethics committee. The study was carried out on 105 patients requiring CVC insertion as part of their treatment. They were divided into three groups, Group D, Group H and Group C each consisting of 35 patients with type II diabetes mellitus, hypertension and American Society of Anaesthesiologists (ASA) class 1 patients respectively. Right internal jugular vein (IJV) was cannulated and the occurrence of thrombus and its progression was noted using serial colour Doppler sonography on third, sixth and on the dayof catheter removal. Statistical analysis done using SPSS 22 version software. ANOVA and Chi-square tests were used. P<0.05 considered statistically significant. Results: The rate of CRT was 34.3%, 20%, 8.57% in Group D, Group H and Group C respectively. The rate of thrombus formation was statistically significant in group D compared to group C. Conclusions: CRT formation was observed in all the groups with statistically significant proportion in diabetics compared to ASA 1 patients.

Introduction- Propofol vials are often used in parts or are opened and left unattended. This has lead to blood stream infections, surgical site infections and acute febrile episodes. A prospective observational study was undertaken to know the incidence and pattern of bacterial growth in samples of Propofol in tropical climate. Materials and methods- Samples were collected from vials of propofol of different brands, both with and without edetate at different time intervals with relation to room temperature. Each sample of 1ml were inoculated in Brain Heart Infusion (BHI) and incubated for 48hours. Presence of bacterial growth and their pattern were studied. Statistical analysis used- Paired t test for categorical variables and for non categorical variables Levine's test and Pearson correlation. Results- Overall 42.26% of samples showed bacterial growth. The incidence was more in samples of propofol without edetate (43.75%) compared to samples with edetate (41.97 %). Most common organism was Staphylococcus aureus, followed by Enterococcus, Acinetobacter, Bacillus species, Pseudomonas and Staphylococcus citrus. Conclusion- Propofol vial once opened favours bacterial colonisation and growth. Adding edetate to propofol has not shown much benefit in decreasing the incidence.