Worst-case Events Research Articles

From the Editor-in-Chief: Worst Case Events Require Excellent Nephrology Nurses

From the Editor-in-Chief: Worst Case Events Require Excellent Nephrology Nurses

ASHP's second cold chain forum tackles excursion management challenges and optimization.

ASHP’s Executive Forum on Cold Chain Management met virtually October 11 to share insights on how best to receive, store, and move medications in accordance with regulatory expectations and product integrity requirements—and what to do when things go wrong. “Short-term temperature excursions do occur and will occur when you store a product and in transfer through the supply chain,” acknowledged Desmond Hunt, principal scientific liaison for the U.S. Pharmacopeia (USP) in Rockville, MD. “We’re hoping, as an industry, that we go from temperature excursion panic to temperature excursion management, knowing that these things are going to occur and planning for those worst-case events.” Hunt was the first speaker at the session “Health-Systems Design to Optimize Cold Chain Management.” The event was organized by the ASHP Innovation Center and supported by a grant from Cold Chain Technologies. The executive forum previously convened April 19, 2022, to define the current state of cold chain management and how it affects health systems. A third session is scheduled for spring 2023 and will focus on future directions of cold chain management in health systems.

The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies

Abstract. The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.

Machine learning, economic regimes and portfolio optimisation

In portfolio models, the depiction of future outcomes depends upon a representative accounting of economic conditions. There is much evidence that crash periods display much different patterns than normal markets, suggesting that forecasting models ought to be based on multiple regimes. We apply two techniques from machine learning in our empirical study to improve robustness: 1) trend-filtering - to distinguish regimes possessing relatively homogeneous patterns; 2) a shrinkage/cross validation approach within a factor analysis of performance. A scenario-based portfolio model is proposed and designed to address multiple regimes. The worst-case events are well described within the framework, as compared with mean-variance Markowitz models that treat equally all historical performance.

Measurement-Based Worst-Case Execution Time Estimation Using the Coefficient of Variation

Extreme Value Theory (EVT) has been historically used in domains such as finance and hydrology to model worst-case events (e.g., major stock market incidences). EVT takes as input a sample of the distribution of the variable to model and fits the tail of that sample to either the Generalised Extreme Value (GEV) or the Generalised Pareto Distribution (GPD). Recently, EVT has become popular in real-time systems to derive worst-case execution time (WCET) estimates of programs. However, the application of EVT is not straightforward and requires a detailed analysis of, and customisation for, the particular problem at hand. In this article, we tailor the application of EVT to timing analysis. To that end, (1) we analyse the response time of different hardware resources (e.g., cache memories) and identify those that may lead to radically different types of execution time distributions. (2) We show that one of these distributions, known as mixture distribution, causes problems in the use of EVT. In particular, mixture distributions challenge not only properly selecting GEV/GPD parameters (i.e., location, scale and shape) but also determining the size of the sample to ensure that enough tail values are passed to EVT and that only tail values are used by EVT to fit GEV/GPD. Failing to select these parameters has a negative impact on the quality of the derived WCET estimates. We tackle these problems, by (3) proposing Measurement-Based Probabilistic Timing Analysis using the Coefficient of Variation (MBPTA-CV), a new mixture-distribution aware, WCET-suited MBPTA method that builds on recent EVT developments in other fields (e.g., finance) to automatically select the distribution parameters that best fit the maxima of the observed execution times. Our results on a simulation environment and a real board show that MBPTA-CV produces high-quality WCET estimates.

Is There a Largest ESD Event?

Ground-based electron charging tests are commonly performed as part of satellite design qualification. The objective of the test is to determine the severity of the resulting electrostatic discharges (ESD) and, via subsequent analysis, determine the hazard they pose to the health and function of the satellite. ESD tests generate large data sets because of the substantial and random variation in ESD amplitudes. The variability is readily apparent in time series scatter plots. The average amplitude, the range of amplitudes (standard deviation), and a worst case event in a set of data are immediately seen. If a failure threshold is known, the margin between that threshold and both the average and worst case events can be shown. Common practice is to compare either the worst case observed event or a 3-sigma upper limit to the failure threshold when assessing the risk of ESD-induced failure. Several samples of ESD test data are reviewed, and the cumulative probability distributions are shown to follow a power law relationship. Applying a Gaussian distribution to the data implies failure is nearly impossible, whereas the power law shows failure is certain because the power law has fatter tails in the extreme event end of the distribution. The consequences of fat tails for ESD risk assessment are discussed.

LNG decision making approaches compared

Hazard zones associated with LNG handling activities have been a major point of contention in recent terminal development applications. Debate has reflected primarily worst case scenarios and discussion of these. This paper presents results from a maximum credible event approach. A comparison of results from several models either run by the authors or reported in the literature is presented. While larger scale experimental trials will be necessary to reduce the uncertainty, in the interim a set of base cases are suggested covering both existing trials and credible and worst case events is proposed. This can assist users to assess the degree of conservatism present in quoted modeling approaches and model selections.

System-level design hardening based on worst-case ASET Simulations

We present experimental and simulation results on single-event transients in an analog subsystem for satellite electronic equipment. Investigations based on worst-case transient events, simulated with transistor-level circuit models, suggest design modifications for hardening.

Electrical Damping and its Effect on Accumulate Fatigue Life Expenditure of Turbine-Generator Shafts Following Worst-Case Supply System Disturbances

The paper examines the effect electrical phenomena has on damping torsional vibrations of turbine-generator shafts resulting from severe disturbances on the system supply. The effect system impedance and power factor has on time constants for decay of predominant shaft torsional vibrations and on fatigue life expenditure of the shaft following worstcase events is investigated. The impact pole slipping resulting from delayed fault clearance has on peak shaft torque and on accumulative fatigue life expenditure of the shaft is also examined.

Air Quality Screening Model with Long-Term Averaging

A simple screening model is presented for estimating maximum ground level concentrations of air pollutants from single elevated buoyant sources of emissions. The model, which incorporates plausible error margins, is based on the Gaussian dispersion formula. Maximum longer-term (3, 8, 24 h) concentrations are estimated using a joint probability analysis of the persistence of meteorological worst case events.