The improvement of passive fire protection of storage vessels is a key factor to enhance safety among the LPG distribution chain. A thermal and mechanical model based on finite elements simulations was developed to assess the behaviour of full size tanks used for LPG storage and transportation in fire engulfment scenarios. The model was validated by experimental results. A specific analysis of the performance of four different reference coating materials was then carried out, also defining specific key performance indicators (KPIs) to assess design safety margins in near-miss simulations. The results confirmed the wide influence of coating application on the expected vessel time to failure due to fire engulfment. Aquite different performance of the alternative coating materialswas evidenced. General correlationswere developed among the vessel time to failure and the effective coating thickness in full engulfment scenarios, providing a preliminary assessment of the coating thickness required to prevent tank rupture for a given time lapse. The KPIs defined allowed the assessment of the available safety margins in the reference scenarios analyzed and of the robustness of thermal protection design.
MULTIFILE
Amsterdam Airport Schiphol has faced capacity constraints, particularly during peak periods. At the security screening checkpoint, this is due to the growing number of passengers and a shortage of security staff. To improve operating performance, there is a need to integrate newer technologies that improve passing times. This research presents a discrete event simulation (DES) model for the inclusion of a shoe scanner at the security screening checkpoint at Amsterdam Airport Schiphol. Simulation is a frequently used method to assess the influence of process changes, which, however, has not been applied for the inclusion of shoe scanners in airport security screenings yet. The simulation model can be used to assess the implementation and potential benefits of an optical shoe scanner, which is expected to lead to significant improvements in passenger throughput and a decrease in the time a passenger spends during the security screening, which could lead to improved passenger satisfaction. By leveraging DES as a tool for analysis, this study provides valuable insights for airport authorities and stakeholders aiming to optimize security screening operations and enhance passenger satisfaction.
Objective: To explore predictors of dropout of patients with chronic musculoskeletal pain from an interdisciplinary chronic pain management programme, and to develop and validate a multivariable prediction model, based on the Extended Common- Sense Model of Self-Regulation (E-CSM). Methods: In this prospective cohort study consecutive patients with chronic pain were recruited and followed up (July 2013 to May 2015). Possible associations between predictors and dropout were explored by univariate logistic regression analyses. Subsequently, multiple logistic regression analyses were executed to determine the model that best predicted dropout. Results: Of 188 patients who initiated treatment, 35 (19%) were classified as dropouts. The mean age of the dropout group was 47.9 years (standard deviation 9.9). Based on the univariate logistic regression analyses 7 predictors of the 18 potential predictors for dropout were eligible for entry into the multiple logistic regression analyses. Finally, only pain catastrophizing was identified as a significant predictor. Conclusion: Patients with chronic pain who catastrophize were more prone to dropout from this chronic pain management programme. However, due to the exploratory nature of this study no firm conclusions can be drawn about the predictive value of the E-CSM of Self-Regulation for dropout.
