Evaluation of Contact Stresses in Bearings Made of Al – Beryl Metal Matrix Composites by Finite Element Method

AbstractIn the present investigation, interference fitted assemblies were analyzed using finite element method to evaluate contact stresses. The main objective of this research work is to develop metal matrix composite of commercially available pure aluminum reinforced with different weight percentage of Beryl to attain a most desirable property combination for bearings. A detailed analysis on the effect of bearing material on contact stresses was undertaken. The work covers the analysis based on Hertzian contact stresses. An appropriate finite element model was developed to analyze the pattern of contact stresses in the interference assemblies. Ansys workbench was used as a tool to construct the model and to perform analysis. The model was simulated by applying a pressure of 100MPa and at different speeds of the shaft. A comparative study on the effect of bearing materials such as bronze, Al-SiC and Al-Beryl MMCs on contact stresses were clearly demonstrated. It has been found that the contact stresses in the bearings made of Al-Beryl metal matrix composite was in the range of 4678.7 to 4680Pa at different speeds which was very much less when compared to the bronze and Al- SiC MMC. The results clearly demonstrated Al-Beryl can be used as one of the most suitable materials for fabricating bushes

Use of garenoxacin: a new generation antibiotic for surgical infections

Background: The management of skin and skin structure infections (SSSI) still continues to be global challenge. USFDA has given strong recommendation for adequate empirical antibiotic coverage to avoid further complication of the wounds. Wound complications, especially in the diabetic population, patients with low immunity remains a big challenge though other factors like site of the wound, age of the patients also play an important role.Methods: A retrospective observational study was conducted to analyze clinical utility of garenoxacin for surgical prophylaxis. A total of 100 patients, 30 patients with diabetic foot and 70 patients with post-surgical intraabdominal wounds who were prescribed garenoxacin 2×200 mg as stat dose prophylactically. Swab culture from the wound slough and drain tube samples were sent for culture/sensitivity on day 0, day 5, and day 7. Wound healing was evaluated by estimating slough discharge, size of the wound, vascularity, and overall healing. They were categorized as treatment failure group, when sough/drain-discharge reduction was ≤50%, improved if sough/drain-discharge reduction was 50-75% and cure when sough/drain-discharge reduction was 75-100%.Results: The healthy granulation tissue was observed post 7 days therapy of garenoxacin 2×200 mg in diabetic foot ulcer (DFU) patients when administered empirically before surgical debridement. In patients with post-operative infectious intraabdominal wounds, the most common isolated organisms were Enterococcus, Acinetobacter and Klebsiella. Post garenoxacin therapy used as switch therapy empirically for 5 days resulted in 100% sterile culture. While evaluating slough/drain-discharge in DFU patients, 84% patients showed cure and 16% showed improvement at the end of day 7 and in patients with post-operative infectious intra-abdominal wounds cure was observed in 86% patients showed cure and 14% patient showed improvement. No side effects were reported during the study.Conclusions: Administration of garenoxacin used as empirical therapy for surgical prophylaxis and as switch therapy in patients with DFU s and post-surgical infectious wounds for the period of 5-7 days has been found effective indicating its wide spectrum of action

Planar reorientation maneuvers of space multibody systems using internal controls

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77104/1/AIAA-11411-971.pd

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers