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Articles by C. Lucas
Total Records ( 4 ) for C. Lucas
  Y. J. C. Woudenberg , C. Lucas , C. Latour and W. J. M. Scholte op Reimer
  Aim  To explore which factors are associated with psychological insulin resistance in insulin-naive patients with Type 2 diabetes in primary care.

Methods  A sample of 101 insulin-naive patients with Type 2 diabetes completed self-administered questionnaires including demographic and clinical characteristics, the Insulin Treatment Appraisal Scale and the Center for Epidemiological Studies Depression scale. Psychological insulin resistance was denoted by negative appraisal of insulin (Insulin Treatment Appraisal Scale).

Results  Thirty-nine per cent of the sample were unwilling to accept insulin therapy. Unwilling participants perceived taking insulin more often as a failure to control their diabetes with tablets or lifestyle compared with willing participants (59 vs. 33%), unwilling participants were more reluctant to accept the responsibilities of everyday management of insulin therapy (49 vs. 24%). Multiple linear regression analysis revealed that depression and objection to lifelong insulin therapy were independently associated with psychological insulin resistance.

Conclusions  In this study in primary care, depression and objection to lifelong insulin therapy are associated with psychological insulin resistance. Analysis of the objection to the indefiniteness of insulin therapy showed a sense of limitation of daily life and loss of independence that should not be underestimated. Insulin should be offered as a means to improve health as this might facilitate the acceptance of insulin therapy.

  A. Fereidunian , H. Lesani , C. Lucas and M. Lehtonen
  This research letter introduces a novel framework for the implementation of Adaptive Autonomy for Intelligent Electronic Devices (IEDs). The study aims at achieving an optimum function allocation between IEDs and humans in automation systems. The function allocation should be adapted to the changes in environmental conditions, thus referring to as Adaptive Autonomy (AA). Performance Shaping Factors (PSFs) concept is utilized to represent the environmental conditions. Moreover, Experts Judgment method is used, to tackle the complex issue of human reliability assessment. The framework is implemented to the power distribution automation system of the Greater Tehran Electricity Distribution Company (GTEDC) and the obtained results are discussed then. Furthermore, the trends of the IED autonomy levels are investigated versus the situation criticality and the automation stages. Apart from introducing a novel implementation framework for IEDs AA, this letter discusses on the application-oriented issues, due to the context-based nature of the Human-Automation Interaction (HAI). The developmental relevance of this study is significant, as it is performed in a Metropolitan area of an Asian/Middle Eastern developing country, thus generalize-able to the similar applications to a reasonable extend.
  M. Zakermoshfegh , S.A.A.S. Neyshabouri and C. Lucas
  The main objective in Conceptual Rainfall-Runoff (CRR) model calibration is to find a set of optimal model parameter values that provides a best fit between observed and estimated flow hydrographs, where the traditional trial and error manual calibration is very tedious and time consuming. Recently in multi dimensional combinatorial optimization problems, meta-heuristic algorithms have shown an encouraging performance with a low computational cost. In this study as a new application of Particle Swarm Optimization (PSO) algorithm, it is applied to automatic calibration of HEC-1 lumped CRR model and the methodology is tested in two example applications: a synthetic hypothetical example and a real case study for the Gorganrood river basin in the north of Iran. The results show encouraging performance of the proposed automated methodology.
  A. Fereidunian , M.A. Zamani , H. Lesani , C. Lucas and M. Lehtonen
  Earlier we introduced a novel framework for implementation of Adaptive Autonomy (AA). This study presents an expert system realization of the AA framework, referred to as Adaptive Autonomy Expert System (AAES). The proposed AAES is based on the extracted rules from the Expert’s Judgment on proper Levels of Automation (LOA) for various environmental conditions, modeled as Performance Shaping Factors (PSFs). Decision fusion method is used as expert system inference engine, where eight decision fusion methods are developed as prospective ones. The AAES is realized in the practical case of electric power Utility Management Automation (UMA) for the Greater Tehran Electricity Distribution Company (GTEDC). The practical list of PSFs and the judgments of GTEDC’s experts are used as the expert system rule base in this research. The results of implementing the proposed AAES to GTEDC’s network are evaluated according to two criteria: average error and error margin. Five out of eight decision fusion methods are proven to be suitable inference engines, due to both criteria. Evaluation of the results shows that the proposed AAES can estimate proper LOAs for GTEDC’s UMA system, which change due to the changes in PSFs; thus providing a dynamic (adaptive) LOA scheme for UMA.
 
 
 
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