An efficient method to avoid path lookup in file access auditing in IO path to improve file system IO performance

One of the biggest challenges in metadata management schemes that sit outside the filesystem layer is their ability to index meaningful path information of files that are being referenced in an external system like a database or in a metadata journal file. Path to a file is a critical requirement that allows both meaningful interpretation of the locality of the file and its metadata and also secondly allows for more efficient user mode services that can transform the file or its metadata. Additionally path information is very essential in compliance systems where audit logs need to tell what happened to a file and where it is located. However when the data path is being audited from layers such as protocols, it becomes harder to reconstruct the entire path information for all the files given that the protocol layers do not directly integrate with the underlying Filesystem. The protocol layers would then need to rely on system cache to get the path data and sometimes this may not be possible making it required for the protocol to actually do an expensive reverse path walk, reconstructing the path. This actually heavily degrades the performance of the system. In this paper we discuss a mechanism that allows us to record enough information about the file using the unique ID of itself and its parent in the protocol layer such that if and when required the path information can be reconstructed based on a reliable reverse lookup in a database or a file based journal system. The idea is to have enough information to reconstruct the path at a later time and outside the system where the information was initially originated from. The paper also talks of keeping this system consistent under all conditions

Friction Stir Welding of Aluminium Alloys

This chapter investigates on the characterization of friction stir welded dissimilar aluminium alloys AA2024 with AA5052, AA2024 with AA6061 and AA 5052 with AA6061. Five tool designs were employed with first two dissimilar combinations to analyze the influence of rotation and traverse speed over microstructural and mechanical properties. H13 tool steel was used as tool material with various pin profiles which includes cylindrical, cylindrical-threaded, squared, tapered and stepped types. In the dissimilar welding of AA 2024 with AA 5052, sound welds were produced with stepped pin tool. In the dissimilar welding of AA 2024 with AA 6061, ratio between tool shoulder to diameter of tool pin was the most influential factor. Welded joints failed in the Heat affected zone (HAZ) of 6061 where the hardness values were comparatively less. In dissimilar welding of AA 5052 with AA6061, cylindrical pin tool was used at a constant speed of 710 rpm and at different feed rates of 28 and 40 mm/min. Micro structural examination showed variation of grain size in every zone and their influence on mechanical properties. Correlating mechanical and metallurgical properties, the optimized process parameters of speed and feed were identified to be 710 rpm and 28 mm/min respectively for all attempted dissimilar combinations

A Comparative Performance Analysis of Hybrid and Classical Machine Learning Method in Predicting Diabetes

Diabetes mellitus is one of medical science’s most important research topics because of the disease’s severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the homogenous model, which comprises Random Forest, AdaBoost, XGBoost, Extra Trees, Gradient Booster, and the heterogeneous model that uses stacking ensemble methods. The stacking ensemble or stacked generalization approach is a meta-classifier in which multiple learners collaborate for prediction. The performance of the homogeneous hybrid models, Stacked Generalization and the classic machine learning methods such as Naive Bayes and Multilayer Perceptron, k-Nearest Neighbour, and support vector machine are compared. The experimental analysis using Pima Indians and the early-stage diabetes dataset demonstrates that the hybrid models achieve higher accuracy in diagnosing diabetes than the classical models. In the comparison of all the hybrid models, the heterogeneous model using the Stacked Generalization approach outperformed other models by achieving 83.9% and 98.5%. Doi: 10.28991/ESJ-2023-07-01-08 Full Text: PD

Investigation on the Flexural Behaviour of Steel Cold Formed Built up Sections

For the past few decades, substantial progress has been made in material properties and construction methodology, which demands in need of development of stronger and lighter members in structural steel applications. The demands of increase in strength and reduction in weight of sections leads to development of structures which are slender and also stability plays a major role in design. The main goal of this study is to develop and investigate the performance of build-up steel I beam sections with corrugated webs. This study focuses on analysis of flexural behaviour and failure modes of plain web, rectangular, trapezoidal web and triangular web in beams by experimental investigations using three point load test and analytical investigations using ANSYS software. From experimental and analytical analysis, triangular corrugated web beam perform better compared to all section. The experimental results obtained are more similar to analytical results obtained by ANSYS software with only slight deviations. The failure modes in both experimental investigation and analytical analysis are similar

Significance of 18S rDNA specific primers in the identification of genus Dunaliella

The cells survive in extreme marine environments has received significant interest due to their high valuable compounds. In the present attempt, a total of six different isolates of Dunaliella isolated from the salt pans of Andhra Pradesh, India were identified based on their morphology and cultural characteristics. Besides, the isolates were subjected to molecular identification using 18S rDNA specific primers. Out of the six isolate one was never amplified with the any of species specific primers used hence it was partially sequenced and submitted in GenBank. This study obviously describes the incidence of non carotenogenic strains (never turn from green to red) of Dunaliella bardawil and Dunaliella parva in natural environment