Error Scenarios Research Articles

Investigating the Repeatability of the Extracted Factors in Relation to the Type of Rotation Used, and the Level of Random Error: A Simulation Study

Factor analysis (FA) is the most commonly used pattern recognition methodology in social and health research. A technique that may help to better retrieve true information from FA is the rotation of the information axes. The purpose of this study was to evaluate whether the selection of rotation type affects the repeatability of the patterns derived from FA, under various scenarios of random error introduced, based on simulated data from the Standard Normal distribution. It was observed that when applying promax non - orthogonal rotation, the results were more repeatable as compared to the orthogonal rotation, irrespective of the level of random error introduced in the model.

Dosimetric impact of random spot positioning errors in intensity modulated proton therapy plans of small and large volume tumors

To study dosimetric impact of random spot positioning errors on the clinical pencil beam scanning proton therapy plans. IMPT plans of 10 patients who underwent proton therapy for tumors in brain or pelvic regions representing small and large volumes, respectively, were included in the study. Spot positioning errors of 1 mm, -1 mm or ±1 mm were introduced in these clinical plans by modifying the geometrical co-ordinates of proton spots using a script in the MATLAB programming environment. Positioning errors were simulated to certain numbers of (20%, 40%, 60%, 80%) randomly chosen spots in each layer of these treatment plans. Treatment plans with simulated errors were then imported back to the Raystation (Version 7) treatment planning system and the resultant dose distribution was calculated using Monte-Carlo dose calculation algorithm.Dosimetric plan evaluation parameters for target and critical organs of nominal treatment plans delivered for clinical treatments were compared with that of positioning error simulated treatment plans. For targets, D95% and D2% were used for the analysis. Dose received by optic nerve, chiasm, brainstem, rectum, sigmoid, and bowel were analyzed using relevant plan evaluation parameters depending on the critical structure. In case of intracranial lesions, the dose received by 0.03 cm3 volume (D0.03 cm3) was analyzed for optic nerve, chiasm and brainstem. In rectum, the volume of it receiving a dose of 65 Gy(RBE) (V65) and 40 Gy(RBE) (V40) were compared between the nominal and error introduced plans. Similarly, V65 and V63 were analyzed for Sigmoid and V50 and V15 were analyzed for bowel. The maximum dose variation in PTV D95% (1.88 %) was observed in a brain plan in which the target volume was the smallest (2.7 cm3) among all 10 plans included in the study. This variation in D95% drops down to 0.3% for a sacral chordoma plan in which the PTV volume is significantly higher at 672 cm3. The maximum difference in OARs in terms of absolute dose (D0.03 cm3) was found in left optic nerve (9.81%) and the minimum difference was observed in brainstem (2.48%). Overall, the magnitude of dose errors in chordoma plans were less significant in comparison to brain plans. The dosimetric impact of different error scenarios in spot positioning becomes more prominent for treatment plans involving smaller target volume compared to plans involving larger target volumes. Provides information on the dosimetric impact of various possible spot positioning errors and its dependence on the tumor volume in intensity modulated proton therapy.

The generalized equivalence of regularization and min\u2013max robustification in linear mixed models

The connection between regularization and min–max robustification in the presence of unobservable covariate measurement errors in linear mixed models is addressed. We prove that regularized model parameter estimation is equivalent to robust loss minimization under a min–max approach. On the example of the LASSO, Ridge regression, and the Elastic Net, we derive uncertainty sets that characterize the feasible noise that can be added to a given estimation problem. These sets allow us to determine measurement error bounds without distribution assumptions. A conservative Jackknife estimator of the mean squared error in this setting is proposed. We further derive conditions under which min-max robust estimation of model parameters is consistent. The theoretical findings are supported by a Monte Carlo simulation study under multiple measurement error scenarios.

Numerical modelling and design exploration of a novel sandwich structure designed for low velocity impact

In this paper, a novel composite sandwich structure made entirely from self-reinforced PET (poly-ethylene terephthalate matrix & fiber) is studied as a competitive candidate for LVI (low velocity impact) due to the material’s apparently good energy absorption characteristics and core shape. An important advantage in using this ductile material for the cellular core and for the outer sheets is that the interface between them is done by using the same PET matrix, with no additional adhesives. The structure is also fully recyclable, thus reducing the impact on the environment. Another significant aspect is the unique core design, conceived to store a large amount of elastic deformation. This is useful in low velocity impact scenarios where initial shape recovery becomes important for appearance reasons (i.e. no visual damage after impact) and for functional reasons (i.e. recovering aerodynamic shape). The structure study implies an optimization algorithm for an LVI simulation to be set up based on the finite element method, connected to a design exploration software. By using the design space exploration process, the optimum parameter set for the cellular core can be obtained with less resources than by using the traditional trial and error scenarios and expose the relations between these parameters with no previous parametrical analytical study.

Optimal Day-ahead Scheduling of Islanded Microgrid Considering Risk-based Reserve Decision

Due to the lack of support from the main grid, the intermittency of renewable energy sources (RESs) and the fluctuation of load will derive uncertainties to the operation of islanded microgrids (IMGs). It is crucial to allocate appropriate reserve capacity for the economic and reliable operation of IMGs. With the high penetration of RESs, it faces both economic and environmental challenges if we only use spinning reserve for reserve support. To solve these problems, a multi-type reserve scheme for IMGs is proposed according to different operation characteristics of generation, load, and storage. The operation risk due to reserve shortage is modeled by the conditional value-at-risk (CVaR) method. The correlation of input variables is considered for the forecasting error modeling of RES and load, and Latin hypercube sampling (LHS) is adopted to generate the random scenarios of the forecasting error, so as to avoid the dimension disaster caused by conventional large-scale scenario sampling approaches. Furthermore, an optimal day-ahead scheduling model of joint energy and reserve considering risk-based reserve decision is established to coordinate the security and economy of the operation of IMGs. Finally, the comparison of numerical results of different schemes demonstrate the rationality and effectiveness of the proposed scheme and model.

Refractive error scenario in paediatric age group (7-15 years) in Assam: A hospital based study

Aim: To study the prevalence of various types of Uncorrected Refractive errors in a paediatric age group (7-15 years) attending a tertiary care centre. Materials and Methods: A hospital based cross sectional study was carried out to evaluate proportion of various types of uncorrected refractive errors in age group of 7–15yrs. in a tertiary eye care centre for a period of 3 months. Total of 4000 children were screened for presence of refractive error after taking informed consent from their parents. Children were then grouped into different types of refractive errors based on the results of cycloplegic refraction. Results: Out 4000 children, the most common age with refractive error was 10-12 years followed by 13-15 years and the commonest refractive error was astigmatism (55%) followed by myopia (34%). The most common presenting symptom was headache followed by visual disturbance. Significant family history was noted among group with prevalence of positive family history in 17% of study group. Discussion & amp, Conclusion: Astigmatism & Myopia is the commonest types of refractive error in children that was consistent with other Indian studies. Study suggests the significance of early school based screen to reduce the burden of childhood ocular morbidity because of uncorrected refractive error. Keywords: Uncorrected refractive error, Childhood blindness, Assam, Scenario.

Are all errors created equal? Testing the effect of error characteristics on learning from errors in three countries

ABSTRACT Errors can be a source of learning. However, little is known to what extent learning from errors depends on error characteristics and the context in which the error was made. We tested the assumption that more learning occurs from errors with severe consequences and when the error was made by oneself. We further investigated if and how learning from errors and organizational error culture differs between countries. In two vignette studies (Study 1, N = 118 from the United States; Study 2, N = 588 from the United States, Hungary, and Germany), participants responded to error scenarios that happened to employees at work. As expected, people learned more from errors in terms of affective error learning (Studies 1 and 2) and cognitive error learning (Study 1) if consequences were severe and if the error was made by themselves. Furthermore, we found differences between countries (Study 2) in that participants from the United States learned more from errors and reported more error management culture than participants from Hungary or Germany. Furthermore, the relationship of country and learning was mediated by error management culture. With our studies, we aim to contribute to a better exploitation of the learning potential inherent in errors.

Comparison of Treatment Plan Robustness Between Dynamic Trajectory Radiotherapy and VMAT

Dynamic trajectory radiotherapy (DTRT) was recently developed in our institute for a non-coplanar arc delivery technique utilizing additionally to VMAT table and collimator rotations. Recent studies on the dosimetric performance suggest a substantial improvement in organs at risks (OARs) sparing when compared with conventional C-arm VMAT delivery. In this work, the treatment plan robustness for DTRT and VMAT is compared with respect to setup errors. Robustness was assessed by generating DVH bands as well as so-called robustness maps (rMaps) by means of a false/true evaluation of certain dosimetric user-defined limits considered acceptable for selected structures with respect to a range of setup errors considered. The robustness was assessed for a clinically motivated head and neck, a lung and a prostate case. For each case systematic translational (ranging from -6 to 6 mm) and rotational (ranging from -3° to 3°) setup error scenarios were defined and for each scenario Monte Carlo dose calculations for the DTRT and VMAT treatment plans were performed. The resulting dose distributions served as input for the robustness assessment determining DVH bands and rMaps. The acceptance conditions for the OARs were defined such that the mean doses for parallel OARs and near maximum doses for serial OARs do not deteriorate more than 2 Gy. For the CTV V95% a deterioration of 1% was allowed in order to pass the robustness criteria. For the isotropic CTV to PTV margins 4 mm, 5 mm and 10 mm were applied for the head and neck, the prostate and the lung case, respectively, except for the dorsal direction of the prostate case, where 3 mm was used. For the head and neck and the prostate case the percentage of setup error scenarios passing the acceptance criteria were 29% and 63% (VMAT) and 23% and 58% (DTRT) for translations and 60% and 100% (VMAT) and 62% and 100% (DTRT) for rotations, respectively. Depending on the direction of the translation setup error, the VMAT plan is slightly more robust. However, for the lung case the VMAT treatment plan is more robust leading to passing rates of 48% (VMAT) and 20% (DTRT) for translations and 83% (VMAT) and 55% (DTRT) for rotations, respectively. In all cases, the robustness determined by the rMaps is dominated by the acceptance criteria for the OARs and not by the ones for the CTV. The results of this study indicate that DTRT treatment plans using additional dynamic table and collimator rotations leading to complex trajectories result in similar systematic setup error robustness as VMAT treatment plans for the head and neck and the prostate case. For the lung case investigated, the VMAT treatment plans show an improved robustness.

Regression calibration to correct correlated errors in outcome and exposure.

Measurement error arises through a variety of mechanisms. A rich literature exists on the bias introduced by covariate measurement error and on methods of analysis to address this bias. By comparison, less attention has been given to errors in outcome assessment and nonclassical covariate measurement error. We consider an extension of the regression calibration method to settings with errors in a continuous outcome, where the errors may be correlated with prognostic covariates or with covariate measurement error. This method adjusts for the measurement error in the data and can be applied with either a validation subset, on which the true data are also observed (eg, a study audit), or a reliability subset, where a second observation of error prone measurements are available. For each case, we provide conditions under which the proposed method is identifiable and leads to consistent estimates of the regression parameter. When the second measurement on the reliability subset has no error or classical unbiased measurement error, the proposed method is consistent even when the primary outcome and exposures of interest are subject to both systematic and random error. We examine the performance of the method with simulations for a variety of measurement error scenarios and sizes of the reliability subset. We illustrate the method's application using data from the Women's Health Initiative Dietary Modification Trial.

On the Transmission Probabilities in Quantum Key Distribution Systems Over FSO Links

In this paper, we investigate the transmission probabilities in three cases (depending only on the legitimate receiver, depending only the eavesdropper, and depending on both legitimate receiver and eavesdropper) in quantum key distribution (QKD) systems over free-space optical links. To be more realistic, we consider a generalized pointing error scenario, where the azimuth and elevation pointing error angles caused by stochastic jitters and vibrations in the legitimate receiver platform are independently distributed according to a non-identical normal distribution. Taking these assumptions into account, we derive approximate expressions of transmission probabilities by using the Gaussian quadrature method. To simplify the expressions and get some physical insights, some asymptotic analysis on the transmission probabilities is presented based on asymptotic expression for the generalized Marcum Q-function when the telescope gain at the legitimate receiver approaches to infinity. Moreover, from the asymptotic expression for the generalized Marcum Q-function, the asymptotic outage probability over Beckmann fading channels (a general channel model including Rayleigh, Rice, and Hoyt fading channels) can be also easily derived when the average signal-to-noise ratio is sufficiently large, which shows the diversity order and array gain.

A new training scheme for neural network based non-linear channel equalizers in wireless communication system using Cuckoo Search Algorithm

Widespread use of the internet has resulted in a massive rise in the data rate of a wireless communication system. Eventually, to mitigate the effect of inter-symbol interference (ISI) in multipath wireless channels, designing a channel equalizer is becoming more demanding. For severe non-linear distortion, the neural network (NN) based non-linear channel equalizers provide superior performance than the adaptive filter based linear equalizers. The NN equalizers are generally trained with back-propagation algorithm which has limitations of slower convergence, local minima stagnation and sensitivity to initial parameters. To overcome these limitations, this paper proposes a training scheme using Cuckoo Search Algorithm (CSA) for functional link artificial NN (FLANN) based channel equalizers. The proposed training scheme has a better ability to escape from local minima, higher exploitation and exploration capabilities. To choose the optimum values of the parameters, the sensitivity analysis of the CSA based approach is performed with its key parameters. Furthermore, three non-linear channels have been simulated to demonstrate the equalization performance of the CSA based training scheme and the results have been compared with recent and well-established algorithms. The simulations confirm that the proposed training scheme performs substantially better than existing metaheuristic algorithms in terms of BER and MSE performance. To show the robustness of the CSA based method, the burst error scenario has been considered and results proved that the method is more successful in handling such scenarios when compared to other methods. The performance of the proposed scheme has been validated for a wide range of signal-to-noise ratio through simulation studies and it is observed that the scheme outperforms the other algorithms in poor SNR conditions as well. Also, to examine the statistical significance of the results provided by the proposed scheme, the Wilcoxon test is performed and the test reveals that the obtained results are statistically significant.

Analytical probabilistic modeling of dose-volume histograms.

Radiotherapy, especially with charged particles, is sensitive to executional and preparational uncertainties that propagate to uncertainty in dose and plan quality indicators, for example, dose-volume histograms (DVHs). Current approaches to quantify and mitigate such uncertainties rely on explicitly computed error scenarios and are thus subject to statistical uncertainty and limitations regarding the underlying uncertainty model. Here we present an alternative, analytical method to approximate moments, in particular expectation value and (co)variance, of the probability distribution of DVH-points, and evaluate its accuracy on patient data. We use Analytical Probabilistic Modeling (APM) to derive moments of the probability distribution over individual DVH-points based on the probability distribution over dose. By using the computed moments to parameterize distinct probability distributions over DVH-points (here normal or beta distributions), not only the moments but also percentiles, that is, α - DVHs, are computed. The model is subsequently evaluated on three patient cases (intracranial, paraspinal, prostate) in 30- and single-fraction scenarios by assuming the dose to follow a multivariate normal distribution, whose moments are computed in closed-form with APM. The results are compared to a benchmark based on discrete random sampling. The evaluation of the new probabilistic model on the three patient cases against a sampling benchmark proves its correctness under perfect assumptions as well as good agreement in realistic conditions. More precisely, ca. 90% of all computed expected DVH-points and their standard deviations agree within 1% volume with their empirical counterpart from sampling computations, for both fractionated and single fraction treatments. When computing α - DVH, the assumption of a beta distribution achieved better agreement with empirical percentiles than the assumption of a normal distribution: While in both cases probabilities locally showed large deviations (up to ±0.2), the respective - DVHs for α={0.05,0.5,0.95} only showed small deviations in respective volume (up to ±5% volume for a normal distribution, and up to 2% for a beta distribution). A previously published model from literature, which was included for comparison, exhibited substantially larger deviations. With APM we could derive a mathematically exact description of moments of probability distributions over DVH-points given a probability distribution over dose. The model generalizes previous attempts and performs well for both choices of probability distributions, that is, normal or beta distributions, over DVH-points.

A distributionally robust bidding strategy for a wind-storage aggregator

This paper derives distributionally robust optimal bidding strategies for a wind-storage aggregator that participates as a price-maker in the day-ahead market, and a deviator in the balancing market. The market power is modeled by using a bi-level structure, which is finally transformed into a mixed integer linear programming model that can be solved using off-the-shelf solvers. The uncertainty in the distribution of wind generation forecast error is incorporated using distributionally robust optimization, which optimizes the decisions against the expectation over the worst-case distribution of the uncertainty. An autoregressive-moving-average model is used to learn the historical forecast error, to generate forecast error scenarios and then to extract the statistical information of these scenarios to describe the potential distributions of the uncertain forecast error. A case study based on one-year Nordpool data validates the effectiveness of the proposed model, shows the impacts of market power, renewable subsidies and aggregation levels, and compares its performance to that of the perfect information case and the case of bidding at day-ahead forecast.

Classification of the source of treatment deviation in proton therapy using prompt-gamma imaging information.

Prompt-gamma imaging (PGI)-based range verification has been successfully implemented in clinical proton therapy recently and its sensitivity to detect treatment deviations is currently investigated. The cause of treatment deviations can be multiple - for example, computed tomography (CT)-based range prediction, patient setup, and anatomical changes. Hence, it would be beneficial, if PGI-based verification would not only detect a treatment deviation but would also be able to directly identify its most probable source. Here, we propose a heuristically derived decision tree approach to automatically classify the sources of range deviation in proton pencil-beam scanning (PBS) treatments of head and neck tumors based on range information obtained with a PGI slit camera. The decision tree model was iteratively generated on a training dataset of different anatomical complexities, for an anthropomorphic head phantom and patient CT data (planning and control CTs) from five patients. Different range prediction errors, setup changes and relevant and nonrelevant anatomical changes were introduced or derived from control CTs, summing up to a total of 98 training scenarios. Independent validation was performed for another 98 scenarios, derived solely from patient CT data of another seven patients. PBS head and neck treatment plans were generated for the nominal scenario. For all PBS spots in the investigated field, PGI profiles were simulated using a dedicated analytical model of the slit camera for the nominal as well as the different error scenarios. From comparison of PGI profiles for nominal and error scenarios, a spot-wise range shift after spot aggregation with a kernel of 7mm sigma was determined for each error scenario. The heuristic approach includes a prefiltering of the most suitable PBS spots for PGI treatment verification. From the validation, the accuracy, sensitivity, and specificity of the model were determined. A five-step consecutive filter was developed to preselect PBS spots. On average, 25% of spots (1044 spots) remained as input for the classification model. The derived heuristic decision tree model is based on five parameters: The coefficient of determination (R2 ), the slope and intercept of the linear regression between PGI-derived range shifts and the respectively predicted proton ranges for the investigated PBS spots, as well as the average and standard deviation of the PGI-derived shifts. With this approach, 94 of 98 error scenarios could be classified correctly in validation (accuracy of 96%). A sensitivity and specificity of 100% and 86% were reached. In this simulation study it was demonstrated that the source of a treatment deviation can be identified from simulated noiseless PGI information in head and neck tumor treatments with high sensitivity and specificity. The application, refinement, and evaluation of the approach on measured PGI data will be the next step to show the clinical feasibility of PGI-based error source classification.

Consequence risk analysis using operating procedure event trees and dynamic simulation

Faults due to human errors cost the petrochemical industry billions of dollars every year and can have adverse environmental consequences. Unquantified human error probabilities exist during process state transitions performed each day by process operators using standard operating procedures. Managing the risks associated with operating procedures is an essential part of managing the overall safety risk. Additional operator training and safety education cannot eliminate all such faults due to human errors; therefore, we propose an operating procedure event tree (OPET) like analysis with branches and events specifically designed to perform risk analysis on operating procedures. The OPET method adapts event trees to analyze the risk due to human error while performing operating procedures. We consider human error scenarios during the procedure and determine the likely consequences by applying dynamic simulation. The modified event tree provides an estimate of the error frequencies.Operating procedure steps were developed, and potential operator faults were determined for two typical equipment switching procedures found in chemical plant operations. Then, dynamic simulation using Aspen HYSYS software was applied to determine the overpressure related consequences of each fault. Finally, the error frequencies resulting from those scenarios were analyzed using operating procedure event trees. We found that a typical ethylene plant gas header would overpressure with 0.6% frequency per manual dryer switch. Since dryer switches occur from every few days up to once per shift, these results suggest that dryer switching should be automated to ensure safe and environmentally friendly operation. Process dryer switching performed manually by operators opening and closing gate valves can be automated with control valves and a distributed control system. A sample distillation column was found to overpressure with 0.85% frequency per manual reflux pump switch.

Feasibility of patient specific quality assurance for proton therapy based on independent dose calculation and predicted outcomes

PurposePatient specific quality assurance (PSQA) is required to verify the treatment delivery and the dose calculation by the treatment planning system (TPS). The objective of this work is to demonstrate the feasibility to substitute resource consuming measurement based PSQA (PSQAM) by independent dose recalculations (PSQAIDC), and that PSQAIDC results may be interpreted in a clinically relevant manner using normal tissue complication probability (NTCP) and tumor control probability (TCP) models. Methods and materialsA platform for the automatic execution of the two following PSQAIDC workflows was implemented: (i) using the TPS generated plan and (ii) using treatment delivery log files (log-plan). 30 head and neck cancer (HNC) patients were retrospectively investigated. PSQAM results were compared with those from the two PSQAIDC workflows. TCP/NTCP variations between PSQAIDC and the initial TPS dose distributions were investigated. Additionally, for two example patients that showed low passing PSQAM results, eight error scenarios were simulated and verified via measurements and log-plan based calculations. For all error scenarios ΔTCP/NTCP values between the nominal and the log-plan dose were assessed. ResultsResults of PSQAM and PSQAIDC from both implemented workflows agree within 2.7% in terms of gamma pass ratios. The verification of simulated error scenarios shows comparable trends between PSQAM and PSQAIDC. Based on the 30 investigated HNC patients, PSQAIDC observed dose deviations translate into a minor variation in NTCP values. As expected, TCP is critically related to observed dose deviations. ConclusionsWe demonstrated a feasibility to substitute PSQAM with PSQAIDC. In addition, we showed that PSQAIDC results can be interpreted in clinically more relevant manner, for instance using TCP/NTCP.

Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics

Learning nonlinear turbulent dynamics from partial observations is an important and challenging topic. In this article, an efficient learning algorithm based on the expectation-maximization approach is developed for a rich class of complex nonlinear turbulent dynamics using short training data. Despite the significant nonlinear and non-Gaussian features in these models, the analytically solvable conditional statistics allows the development of an exact and accurate nonlinear optimal smoother for recovering the hidden variables, which facilitates an efficient learning of these fully nonlinear models with extreme events. Then three additional ingredients are incorporated into the basic algorithm for improving the learning process. First, the physics constraint that requires the conservation of energy in the quadratic nonlinear terms is taken into account. It plays an important role in preventing the finite-time blowup of the solution and various pathological behavior of the recovered model. Second, a judicious block decomposition is applied to many large-dimensional nonlinear systems. It greatly accelerates the calculation of high-dimensional conditional covariance matrix and provides an extremely cheap parallel computation for learning the model parameters. Third, sparse identification of the complex turbulent models is combined with the learning algorithm that leads to parsimonious models. Numerical tests show the skill of the algorithm in learning the nonlinear dynamics and non-Gaussian statistics with extreme events in both perfect model and model error scenarios. It is also shown that in the presence of noise and partial observations, the model is not uniquely identified. Different nonlinear models all perfectly capture the key non-Gaussian features and obtain the same ensemble forecast skill of the observed variables as the perfect model, but they may have distinct model responses to external perturbations.

The evil demon in the lab: skepticism, introspection, and introspection of introspection

In part one, I clarify the crucial notion of “introspection”, and give novel cases for the coherence of scenarios of local and global deception about how we access our own minds, drawing on empirical work. In part two, I evaluate a series of skeptical arguments based on such scenarios of error, and in each case explain why the skeptical argument fails. The first main upshot is that we should not over-estimate what it takes to introspect: introspection need not be accurate, or non-inferential, or exclusive of perception, or even exclusive of confabulation. The second main upshot is that, while skeptical challenges by figures such as Carruthers, Doris, and Schwitzgebel are rich and empirically informed, these skeptical challenges founder on how they are epistemologically under-informed.

An analysis of fatal iatrogenic therapeutic errors reported to United States poison centers

Objective This is a descriptive study evaluating fatal iatrogenic and in-hospital medication errors reported to United States poison centers. Methods A retrospective evaluation of the National Poison Data System from 2000–2017 of all therapeutic errors with a scenario coded as iatrogenic/healthcare professional or occurring in a healthcare facility. Death abstracts were reviewed for details of the exposure and therapeutic error scenarios were recoded or added to the case as appropriate. Cases, where death was considered not related to the exposure, were excluded. Additionally, we created one additional scenario (rate-related) and one additional route of administration (intrathecal) to better describe the cases. Results A total of 172 cases were evaluated. The majority of the patients were female (52.3%) with a median age of 58.5 years (range: 2 days to 96 years). The most commonly reported medication error was “other incorrect dose” (22.7%) followed by other/unknown error (15.1%). The route of exposure was primarily parenteral (54.9%), followed by ingestion (30.2%), then intrathecal (7.0%). The most common medications were cardiac drugs, chemotherapeutics, opioids, anticoagulants, and sedative-hypnotic/antipsychotics. Conclusions Iatrogenic and in-hospital medication errors have been studied extensively with goals to reduce their occurrence. Specific controls to prevent incorrect dosing routes, 10-fold overdoses, and incorrect intrathecal administration have been instituted. Despite interventions, all three of these therapeutic errors continued to occur in 2017, suggesting that more preventive controls should be instituted.

Improving SWE Estimation With Data Assimilation: The Influence of Snow Depth Observation Timing and Uncertainty

AbstractSnow depth observations can be leveraged with data assimilation (DA) to improve estimation of snow density and snow water equivalent (SWE). A key consideration for mission and campaign design is how snow depth retrieval characteristics (including observation timing/frequency and sampling error) influence SWE accuracy and uncertainty in a DA framework. To quantify these effects, we implement a particle filter (PF) assimilation technique to assimilate depth and validate this approach against observed snow density and SWE at 49 snow telemetry sites across 9 years. We sample from continuous in situ snow depth records to test a range of measurement timing and sampling error scenarios representative of remote sensing capabilities. Assimilation reduces density bias by over 40% and SWE bias by over 70% across climate zones and in both wet and dry years. There is little incremental benefit to SWE accuracy when assimilating more than one depth observation near peak accumulation. SWE estimates are less sensitive to observation timing than sampling error. Alternatively, more frequent depth observations improve melt‐out date timing and reduce SWE uncertainty, a key consideration when evaluating the operational utility of DA. In matching depth observations, the PF mostly acts to increase model precipitation inputs, while not systematically shifting other parameter values or forcings across the climate zones represented with the study sites. This demonstrates that precipitation is the largest source of model error. With DA, density errors are still nontrivial (above 10%), illuminating the need for further improvements to modeled density to estimate SWE within specified error limits.