Abstract. When using process models for automation, correctness of the models is a key requirement. While many approaches concentrate on control flow verification only, correct data flow modeling is of similar importance. This paper introduces an approach for detecting and repairing modeling errors

Ontology population is prone to cause inconsistency because the populating process is imprecise or the populated data may conflict with the original data. By assuming that the intensional part of the populated DL-based ontology is fixed and each removable ABox assertion is given a removal cost, we

Ontology population is prone to cause inconsistency because the populating process is imprecise or the populated data may conflict with the original data. By assuming that the intensional part of the populated DL-based ontology is fixed and each removable ABox assertion is given a removal cost, we

procedures is proposed. The requested estimation is based on historical information provided by the electric company Iberdrola S.A. and its employees An expertise method has been developed in order to diagnose transformer conditions, model its aging process, and finally estimate its residual life

ABSTRACT The problem of identifying and repairing data errors has been an area of persistent focus in data management research. However, while traditional data cleaning techniques can be effective at identifying several data discrepancies, they disregard the fact that many errors are systematic

Hereditary nonpolyposis colon cancer (HNPCC) is a Mendelian dominant syndrome of bowel, endometrial, and other cancers and results from germline mutations in mismatch repair (MMR) genes. HNPCC is now best diagnosed on molecular grounds using MMR mutation screening, aided by microsatellite

and analysis of all measured data. The second part of the paper is presenting results from the main engine tests that are proving right maintenance actions after measuring relevant parameters and diagnosing present working conditions of the main engine. Presented model is uniting theoretical knowledge gained

to identify the nature of faults. During FD, different data types are collected, like error codes, diagnostic trouble codes faulty associated with the target system, repair verbatim, and so on. This collected data is then transmitted to the database. This data can be mined to construct diagnosis matrix models

Abstract not found

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accordance with the WHO definition of health, this article examines the alarming discord between the epidemiology of hypertension, type 2 diabetes mellitus (T2DM), and obesity and the low profile of noninvasive (nondrug) compared with invasive (drug) interventions with respect to their prevention, reversal and management. Herein lies the ultimate knowledge translation gap and challenge in 21st century health care. Although lifestyle modification has long appeared in guidelines for medically managing these conditions, this evidence-based strategy is seldom implemented as rigorously as drug prescription. Biomedicine focuses largely on reducing signs and symptoms; the effects of the problem rather than the problem. This article highlights the evidencebased rationale supporting prioritizing the underlying causes and contributing factors for hypertension and T2DM, and, in turn, obesity. We argue that a primary focus on maximizing health could eliminate all three conditions, at best, or, at worst, minimize