The main goal of this study is to identify knowledge gaps and uncertainties in Quantitative Risk Assessments (QRA) for CO2 pipelines and to assess to what extent those gaps and uncertainties affect the final outcome of the QRA. The impact of methodological choices and uncertain values for input parameters on the results of QRA’s have been assessed through an extensive literature review and by using commercially available release, dispersion and effect models. It is made apparent that over the full life cycle of a QRA knowledge gaps and uncertainties are present that may have large scale impact on the accuracy of assessing risks of CO2 pipelines. These encompass the invalidated release and dispersion models, the currently used failure rates, choosing the type of release to be modeled and the dose-effect relationships assumed. Also recommendations are presented for the improvement of QRA’s for CO2 pipelines.
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Full text met HU account. In this article we report a study into the Dutch probation service about the question whether structured decision making about case management plans does or does not improve the quality of these plans, and subsequently improves the effectiveness of offender supervision. Two samples of nearly 300 case management plans each were compared. In the first sample a tool for risk/needs assessment was used to assess the risks and needs but decision making about the subsequent case management plan was not structured (RISc2-sample). In the second sample professionals used the same tool for risk and needs assessment but now it also contained a section for structured decision making about the case management plan (RISc3-sample). Results showed that in the RISc3-sample the quality of the plans was significantly better than in the RISc2-sample: a better match between criminogenic needs and goals, a better match between goals of the offender and goals in the plan, more focus on strengthening social bonds, and a better match between risk of recidivism and intensity of the plan. Some significant correlations between the quality of the plans and the effectiveness of offender supervision were found, indicating that improving case management plans by structured decision support indeed can contribute to probation practice.
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This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.
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