Central Fault Tolerance For Dual Database Server Real Time System

The aim of this article is to find an efficient method to detect fault in dualdatabase server which is working on critical environment real time system (such aspower and water distributed environment).In traditional dual database server the fault tolerance is embedded in eachserver. So when there is any defectiveness, each server try to uncover the error inseparate way. This led to increase the load on each server and job lateness.This paper proposes a central fault tolerant method for dual database serverthrough a centralized control, so that the fault will be more controlled andmanipulated and the load will be less in each server since problems detection andcorrection will not depend on dual server but it will be centralized.It showedpractically how the dual server worked under fault conditions and criticalenvironment such as distributed real time systems

Prediction of NSCLC recurrence from microarray data with GEP

Lung cancer is one of the deadliest diseases in the world. Non‐small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model