A practical guide for operational validation of discrete simulation models

As the number of simulation experiments increases, the necessity for validation and verification of these models demands special attention on the part of the simulation practitioners. By analyzing the current scientific literature, it is observed that the operational validation description presented in many papers does not agree on the importance designated to this process and about its applied techniques, subjective or objective. With the expectation of orienting professionals, researchers and students in simulation, this article aims to elaborate a practical guide through the compilation of statistical techniques in the operational validation of discrete simulation models. Finally, the guide's applicability was evaluated by using two study objects, which represent two manufacturing cells, one from the automobile industry and the other from a Brazilian tech company. For each application, the guide identified distinct steps, due to the different aspects that characterize the analyzed distributions. © 2011 Brazilian Operations Research Society.

Content Validation of the Operational Definitions of the Nursing Diagnoses of Activity Intolerance, Excess Fluid Volume, and Decreased Cardiac Output in Patients With Heart Failure

Objectives To consensually validate the operational definitions of the nursing diagnoses activity intolerance, excessive fluid volume, and decreased cardiac output in patients with decompensated heart failure. Method Consensual validation was performed in two stages: analogy by similarity of defining characteristics, and development of operational definitions and validation with experts. Results A total of 38 defining characteristics were found. Operational definitions were developed and content-validated. One hundred percent of agreement was achieved among the seven experts after five rounds. Ascites was added in the nursing diagnosis excessive fluid volume. Conclusion The consensual validation improves interpretation of human response, grounding the selection of nursing interventions and contributing to improved nursing outcomes. Implications for Practice Support the assessment of patients with decompensated heart failure. Objetivos Realizar a validacAo consensual das definicoes operacionais dos diagnosticos de enfermagem Intolerancia a atividade, Volume de liquidos excessivo e Debito cardiaco diminuido em pacientes com insuficiencia cardiaca descompensada. Metodo ValidacAo consensual em duas etapas: Analogia de semelhanca das caracteristicas definidoras e desenvolvimento de definicoes operacionais e validacAo com expertst. Resultados Foram encontradas 38 caracteristicas definidoras para os diagnosticos de enfermagem. Suas definicoes operacionais foram desenvolvidas e seu conteudo validado. Os resultados mostram que houve 100% de concordancia entre os sete experts apos cinco rodada. As definicoes operacionais foram classificadas com base no nivel de concordanica. Ascite foi acrescentada ao diagnostico Volume de liquidos excessivo. ConclusAo A validacAo consensual melhora a interpretacAo das respostas humanas, embasando a selecAo de intervencoes de enfermagem e contribuindo para melhorar os resultados. Implicacoes Para A Pratica Apoio a avaliacAo dos pacientes com insuficiencia cardiaca descompensada.

GDP 4.0 transfer to SGP 3.0 for SCIAMACHY no2 column processing: Verification with SDOAS / GDOAS prototype algorithms and delta-validation with NDACC / UV-visible network data

Until mid 2006, SCIAMACHY data processors for the operational retrieval of nitrogen dioxide (NO2) column data were based on the historical version 2 of the GOME Data Processor (GDP). On top of known problems inherent to GDP 2, ground-based validations of SCIAMACHY NO2 data revealed issues specific to SCIAMACHY, like a large cloud-dependent offset occurring at Northern latitudes. In 2006, the GDOAS prototype algorithm of the improved GDP version 4 was transferred to the off-line SCIAMACHY Ground Processor (SGP) version 3.0. In parallel, the calibration of SCIAMACHY radiometric data was upgraded. Before operational switch-on of SGP 3.0 and public release of upgraded SCIAMACHY NO2 data, we have investigated the accuracy of the algorithm transfer: (a) by checking the consistency of SGP 3.0 with prototype algorithms; and (b) by comparing SGP 3.0 NO2 data with ground-based observations reported by the WMO/GAW NDACC network of UV-visible DOAS/SAOZ spectrometers. This delta-validation study concludes that SGP 3.0 is a significant improvement with respect to the previous processor IPF 5.04. For three particular SCIAMACHY states, the study reveals unexplained features in the slant columns and air mass factors, although the quantitative impact on SGP 3.0 vertical columns is not significant.