Sensitivity Analysis of Transportation Production Costs in Indonesia

The transportation production cost (TPC) considerably has strong influence to the national economic condition. This paper focused on the analysis of the transportation production cost sensitivity in relation to the variation of the external affecting factor, which are fuel price, rupiah exchange rate and Bank of Indonesia interest rate. Based on the R2 values, the TPC components in general have significant correlation, with the fuel prices. However, they do not have high correlation to the fluctuation of interest rate and rupiah exchange rate. The sensitivity analysis shows that a 10% rise on fuel price would cause 6%, 2%, 7%, 2.4%, and 4.9% rise on the TPC of intercity bus, ferry ship, interisland ship, train, and airline, respectively

Robustness Evaluation of Computer-aided Clinical Trials for Medical Devices

Medical cyber-physical systems, such as the implantable cardioverter defibrillator (ICD), require evaluation of safety and efficacy in the context of a patient population in a clinical trial. Advances in computer modeling and simulation allow for generation of a simulated cohort or virtual cohort which mimics a patient population and can be used as a source of prior information. A major obstacle to acceptance of simulation results as a source of prior information is the lack of a framework for explicitly modeling sources of uncertainty in simulation results and quantifying the effect on trial outcomes. In this work, we formulate the Computer-Aided Clinical Trial (CACT) within a Bayesian statistical framework allowing explicit modeling of assumptions and utilization of simulation results at all stages of a clinical trial. To quantify the robustness of the CACT outcome with respect to a simulation assumption, we define δ-robustness as the minimum perturbation of the base prior distribution resulting in a change of the CACT outcome and provide a method to estimate the δ-robustness. We demonstrate the utility of the framework and how the results of δ-robustness evaluation can be utilized at various stages of a clinical trial through an application to the Rhythm ID Goes Head-to-head Trial (RIGHT), which was a comparative evaluation of the safety and efficacy of specific software algorithms across different implantable cardiac devices. Finally, we introduce a hardware interface that allows for direct interaction with the physical device in order to validate and confirm the results of a CACT for implantable cardiac devices

Global sensitivity analysis of the single particle lithium-ion battery model with electrolyte

The importance of global sensitivity analysis (GSA) has been well established in many scientific areas. However, despite its critical role in evaluating a model’s plausibility and relevance, most lithium ion battery models are published without any sensitivity analysis. In order to improve the lifetime performance of battery packs, researchers are investigating the application of physics based electrochemical models, such as the single particle model with electrolyte (SPMe). This is a challenging research area from both the parameter estimation and modelling perspective. One key challenge is the number of unknown parameters: the SPMe contains 31 parameters, many of which are themselves non-linear functions of other parameters. As such, relatively few authors have tackled this parameter estimation problem. This is exacerbated because there are no GSAs of the SPMe which have been published previously. This article addresses this gap in the literature and identifies the most sensitive parameter, preventing time being wasted on refining parameters which the output is insensitive to

A sensitivity analysis of the PAWN sensitivity index

The PAWN index is gaining traction among the modelling community as a sensitivity measure. However, the robustness to its design parameters has not yet been scrutinized: the size (N) and sampling (ε) of the model output, the number of conditioning intervals (n) or the summary statistic (θ). Here we fill this gap by running a sensitivity analysis of a PAWN-based sensitivity analysis. We compare the results with the design uncertainties of the Sobol’ total-order index (S*Ti). Unlike in S*Ti, the design uncertainties in PAWN create non-negligible chances of producing biased results when ranking or screening inputs. The dependence of PAWN upon (N, n, ε, θ) is difficult to tame, as these parameters interact with one another. Even in an ideal setting in which the optimum choice for (N, n, ε, θ) is known in advance, PAWN might not allow to distinguish an influential, non-additive model input from a truly non-influential model input

ICAN sensitivity analysis

A computer program called Integrated Composite Analyzer (ICAN) was used to predict the properties of high-temperature polymer matrix composites. ICAN is a collection of NASA Lewis Research Center-developed computer codes designed to carry out analysis of multilayered fiber composites. The material properties used as input to the program were those of the thermoset polyimide resin PMR-15 and the carbon fiber Celion 6000. The sensitivity of the predicted composite properties to variations in the resin and fiber properties was examined. In addition, the predicted results were compared with experimental data. In most cases, the effect of changes in resin and fiber properties on composite properties was reasonable. However, the variations in the composite strengths with the moisture content of the PMR-15 resin were inconsistent. The ICAN-predicted composite moduli agreed fairly well with experimental values, but the predicted composite strengths were generally lower than experimental values

Sensitivity Analysis of Flexible Provisioning

This technical report contains a sensitivity analysis to extend our previous work. We show that our flexible service provisioning strategy is robust to inaccurate performance information (when the available information is within 10% of the true value), and that it degrades gracefully as the information becomes less accurate. We also identify and discuss one particular case where inaccurate information may lead to undesirable losses in highly unreliable environments

Sensitivity Analysis of Average Inventory Level (AIL) at a Specialized Hospital

. Hospital inventory management play a very significant role in hospital's performance. Too much inventory wiil lead to excessive inventory cost but too low inventory might result in dissatisfactions of patiens and lack of performance of physicians or doctors. Economic order Quantity (EOQ) is an inventory control method that can help hospital to minimize total inventort cost. However, managers might find difficulties in deterimining the right amount of ordering and holding cost which are needed in calculating EOQ. This research is a case study from a class “A” specialized hospital. Inventory data of pharmaceutical items are calculated to measure the sensitivity of changes in ordering and holding cost to average inventory level which will lead to understock or overstock. Different ordering cost and holding cost will lead to different proportion of overstock and understock but will not give a significant differences. This research recommend EOQ model to be used in hospital inventory management inspite of the difficulty and hesitation of hospitals to estimate ordering and holding cost