CLUSTERING OF SIMILAR HISTORICAL ALARM SUBSEQUENCES IN INDUSTRIAL CONTROL SYSTEMS USING ALARM SERIES AND CHARACTERISTIC COACTIVATIONS

Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations

Alarm flood similarity analysis (AFSA) methods are frequently used as a primary step for root-cause analysis, alarm flood pattern mining, and TREEMENDA TEA TREE CONDITIONER online operator support.AFSA methods have been promoted in several research activities in recent years.However, addressing an often-observed ambiguity of the order of alarms and

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Fibrodysplasia Ossificans Progressiva: A rare case series

Background: Fibrodysplasia ossificans progressiva is a rare autosomal dominant connective tissue disorder with a prevalence of 2 per million individuals.Activating mutation of ACVR1, a bone morphogenetic protein receptor causes ossification of extra-skeletal structures like ligaments, tendons, and aponeurosis.The characteristic findings are bilater

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A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data

Clear Cases BackgroundIdentifying new biomarkers of major depressive disorder (MDD) would be of great significance for its early diagnosis and treatment.Herein, we constructed a diagnostic model of MDD using machine learning methods.MethodsThe GSE98793 and GSE19738 datasets were obtained from the Gene Expression Omnibus database, and the limma R pa

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