Key Error Sources Research Articles

Towards high‐definition vector map construction based on multi‐sensor integration for intelligent vehicles: Systems and error quantification

AbstractA lightweight, high‐definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment can degrade the accuracy of HDVM, necessitating a reliable error quantification. To address these challenges, this paper presents an open‐source and generic HDVM generation pipeline that integrates the global navigation satellite system (GNSS), inertial navigation system (INS), light detection and ranging (LiDAR), and camera. The pipeline begins by extracting semantic information from raw images using the Swin Transformer. The absolute 3D information of semantic objects is then retrieved using depth from the 3D LiDAR, and pose estimation from GNSS/INS integrated navigation system. Vector information (VI), such as lane lines, is extracted from the semantic information to construct the HDVM. To assess the potential error of the HDVM, this paper systematically quantifies the impacts of two key error sources, segmentation and LiDAR‐camera extrinsic parameter error. An error propagation scheme is first formed to illustrate how these errors fundamentally influence the accuracy of the HDVM. The effectiveness of the proposed pipeline is demonstrated through our codeavailable at https://github.com/ebhrz/HDMap. The performance is verified using typical datasets, including indoor garages and complex urban scenarios.

Recurrent grammatical errors in student Russian-to-English consecutive interpreting

Interpreting student errors have little been accorded full-scale treatment. The objective of the article is, then, to provide a classification of and explain some of the most recurrent grammatical errors made by students in Russian-to-English consecutive interpreting. Our findings suggest that all grammatical errors in student consecutive interpreting fall into four groups: errors in the use of articles; errors in the use of tenses; errors in the use of voice; and errors in the use of number. Among the key sources of grammatical errors in consecutive interpreting are inadequate linguistic competence and performance, i.e. tendency to transfer grammatical patterns specific to a native language into a foreign language, unpredictable syntax, quick speech motor actions; and psychological and cognitive factors, i.e. principles of analogy, severe time constraints. Error analysis in student consecutive interpreting can make a positive contribution into the practice of training interpreters as it reveals the problem triggers. Additionally, inquiries in error analysis can make valuable contributions in a linguistic typology of interpreting errors in typologically and structurally different languages, i.e. language-specific and language-pair specific errors.

Evaluating error sources to improve precision in the co-registration of underwater 3D models

Change detection is an essential and widely used approach for investigating ecosystem dynamics. Multi-temporal 3D models increasingly underpin photogrammetry-based analyses of change for many ecologically relevant attributes. To detect change, it is necessary to accurately align 3D models collected at different times using a process referred to as co-registration. However, achieving precise co-registration is difficult in underwater habitats due to practical challenges intrinsic to surveying them. These include a lack of accurate georeferencing information, variable light, turbidity and weather conditions, and diving restrictions dictated by the diver's pressure exposure over time. Here we present an efficient co-registration workflow for 3D models that directly addresses these challenges, derived from underwater structure-from-motion methods. To test our approach, we used 3D models from across a wide range of coral reef habitats covering all those that one may encounter in shallow reefs (15 m depth and above). We then identified and empirically estimated four key sources of error: co-registration, 3D processing, image acquisition, and reference and scaling features (RSF) placement, and quantified their relative contributions to the overall error. Our proposed co-registration workflow had a mean precision of 1.37 ± 16.55 mm. Image acquisition and RSF placement errors contributed the most to the total workflow error (37% and 53%, respectively), while the contribution of co-registration and 3D processing errors was minimal (3% and 7%, respectively). As a result of our analysis, we provide ‘good practice’ guidelines to reduce errors associated with photogrammetric workflows and to facilitate efficient and reliable detection of 3D change in complex underwater ecosystems.

Nonbonded Force Field Parameters from MBIS Partitioning of the Molecular Electron Density Improve Binding Affinity Predictions of the T4-Lysozyme Double Mutant.

The use of computer simulation for binding affinity prediction is growing in drug discovery. However, its wider use is constrained by the accuracy of the free energy calculations. The key sources of error are the force fields used to depict molecular interactions and insufficient sampling of the configurational space. To improve the quality of the force field, we developed a Python-based computational workflow. The workflow described here uses the minimal basis iterative stockholder (MBIS) method to determine atomic charges and Lennard-Jones parameters from the polarized molecular density. This is done by performing electronic structure calculations on various configurations of the ligand when it is both bound and unbound. In addition, we validated a simulation procedure that accounts for the protein and ligand degrees of freedom to precisely calculate binding free energies. This was achieved by comparing the self-adjusted mixture sampling and nonequilibrium thermodynamic integration methods using various protein and ligand conformations. The accuracy of predicting binding affinity is improved by using MBIS-derived force field parameters and a validated simulation procedure. This improvement surpasses the chemical precision for the eight aromatic ligands, reaching a root-mean-square error of 0.7 kcal/mol.

Optimal 1D Ly α forest power spectrum estimation – III. DESI early data

ABSTRACT The 1D power spectrum P1D of the Ly α forest provides important information about cosmological and astrophysical parameters, including constraints on warm dark matter models, the sum of the masses of the three neutrino species, and the thermal state of the intergalactic medium. We present the first measurement of P1D with the quadratic maximum likelihood estimator (QMLE) from the Dark Energy Spectroscopic Instrument (DESI) survey early data sample. This early sample of 54 600 quasars is already comparable in size to the largest previous studies, and we conduct a thorough investigation of numerous instrumental and analysis systematic errors to evaluate their impact on DESI data with QMLE. We demonstrate the excellent performance of the spectroscopic pipeline noise estimation and the impressive accuracy of the spectrograph resolution matrix with 2D image simulations of raw DESI images that we processed with the DESI spectroscopic pipeline. We also study metal line contamination and noise calibration systematics with quasar spectra on the red side of the Ly α emission line. In a companion paper, we present a similar analysis based on the Fast Fourier Transform estimate of the power spectrum. We conclude with a comparison of these two approaches and discuss the key sources of systematic error that we need to address with the upcoming DESI Year 1 analysis.

An efficient method for measuring and identifying geometric and dynamic errors in dual five-axis machine tools

The dual five-axis machining machine is state-of-the-art equipment designed for producing thin-walled aerospace parts like skins and panels. It comprises two five-axis machine tools with mirrored synchronous motions: one bi-rotary machine for milling and the other machine for follow-up support. The high absolute accuracy of the five-axis machine tools themselves and the high synchronous motion accuracy between the two machines are both crucial to ensure machining accuracy. This paper proposes an efficient method to measure and identify geometric and dynamic errors of dual five-axis machine tools. Using the R-test combined with three measurement trajectories to measure the absolute motion error of two five-axis machine tools and the synchronous motion error between the two machines. The designed trajectories include the main rotary axis linkage conditions and can fully reflect the primary motion forms of the dual five-axis machine tool. The main position-independent geometric errors and rotational axis linkage dynamic errors are identified by converting quasi-static and dynamic measurement data from three different trajectories into a unified reference coordinate system. The entire process requires only one instrument installation, effectively avoiding time-consuming repetitive installations and calibration processes while reducing the impact of installation errors on results. The effectiveness of the proposed method for measuring and identifying the geometric and dynamic errors is analyzed combined with Monte Carlo simulation. Experiments were carried out on a dual five-axis mirror milling system (MMS) with a 5000 mm X-stroke. Both simulations and experiments confirmed that the proposed method is efficient in quickly identifying key error sources of the MMS.

Validation of the single-event method for low-energy electron transport via stopping power calculations with MCNP

Monte Carlo simulations of low-energy (<50 keV) electron transport in matter are essential for a broad range of application fields. Several Monte Carlo codes have developed specialized treatments for this case, but a comprehensive validation of low-energy electron transport for general-purpose simulations remains lacking in the literature. One approach to accomplish this validation is calculation of stopping power using low-energy electron transport physics, as stopping power is a fundamental radiation transport quantity which must be simulated accurately for nearly any application. In this work, we use the Monte Carlo N-Particle (MCNP) radiation transport code with the single-event method for electron transport to calculate stopping powers of low-energy electrons (50 eV to 30 keV) in 41 elemental solids, 14 compound solids, and five rare gas solids, comparing simulation results to published semi-empirical stopping power calculations from optical measurements. In general, the simulations give good agreement (typically within ±10%) with semi-empirical stopping power values at higher energies: 300 eV and above for most elemental solids, 1 keV and above for compound solids, and 400 eV and above for rare gas solids. Agreement between MCNP and semi-empirical values is worse below these energies. The most significant source of error is the EPRDATA14 cross section data, which does not account for changes in electronic structure due to solid-state bonding, particularly in compound materials. The simplistic model of atomic excitation used to generate the EPRDATA14 cross sections is another key source of error. Additionally, the breakdown of the continuous slowing-down approximation introduces significant uncertainty at low energies, although this is a limitation of the calculation method and not of the simulation procedure. Accounting for these and other uncertainty sources, the single event method in MCNP is robust and able to give good accuracy for a variety of low-energy electron transport problems through diverse kinds of materials.

Position-independent geometric error compensation of a non-orthogonal five-axis machine tool using a simplified algebraic algorithm

Geometric errors are the key error sources that affect the machining quality of a five-axis machine tool. In the case of a non-orthogonal five-axis machine tool (NOFAMT), the modelling and compensation of geometric errors can be quite complex. This paper proposes a simplified modelling algorithm to derive the analytical expressions for the position-independent geometric error (PIGE) compensation of a NOFAMT without generating higher-order error terms. First, the geometric error modelling was conducted based on the homogeneous transformation matrix (HTM). Then, algebraic expressions for PIGE compensation were obtained according to the actual inverse kinematics and a simplified algorithm. By using the simplified algorithm, the efficiency of deriving algebraic expressions for geometric error compensation can be greatly enhanced. The PIGE compensation’s numerical control (NC) codes can be directly computed using these algebraic expressions. Finally, the effectiveness and efficiency of the new algorithm were verified by numerical analysis and virtual machining. The detailed results were compared with those obtained using the differential method, and both sets of results showed the same trend.

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier–Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods.

Assessors influence results: Evidence on enumerator effects and educational impact evaluations

A significant share of education research uses data collected by “enumerators.” It is well-documented that “enumerator effects”—or inconsistent practices among the people who administer measurement tools—can be a key source of error in survey data collection. However, it is less understood whether this is a problem for academic assessments. We leverage a remote phone-based mathematics assessment of primary school students and survey of their parents in Kenya. Enumerators, who were teachers in our partner's network, were randomized to students to study the presence of enumerator effects. We find that both the academic assessment and survey were prone to enumerator effects and use simulation to show that these effects were large enough to lead to spurious results at a troubling rate in impact evaluations. We therefore recommend assessment administrators randomize enumerators at the student level, orthogonal to categories being compared (e.g., treatment/control groups), and train enumerators to minimize bias.

Expanding bifactor models of psychological traits to account for multiple sources of measurement error.

Over the last decade, applications of bifactor modeling within clinical settings have increased markedly but typically rely on data collected on single occasions. A shortcoming of such research is that reliability coefficients are likely inflated because key sources of measurement error are inadequately modeled and/or confounded with construct variance. We address these problems using three variations of multi-occasion bifactor models with Bayesian-derived parameter estimates to separate systematic variance into general and group factor effects and measurement error into three subcomponents (transient, specific-factor, and random-response). Collectively, these models produce indices of reliability and validity aligned with both standard confirmatory factor models and generalizability designs that extend interpretations of results to the broader domains from which items and occasions are sampled. We demonstrate how these techniques can provide new insights into psychometric properties of scores using Negative Emotionality domain and facet scales from the newly updated Big Five Inventory (BFI-2; Soto & John, 2017). Overall, the two-occasion congeneric bifactor model provided the best fit to the data and most informative indices for revising measures, examining dimensionality of composite and subscale scores, and evaluating the viability of those scores. We include code in R for analyzing all models in our extended online Supplemental Material. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

High-resolution streamflow and weather data (2013–2019) for seven small coastal watersheds in the northeast Pacific coastal temperate rainforest, Canada

Abstract. Hydrometeorological observations of small watersheds of the northeast Pacific coastal temperate rainforest (NPCTR) of North America are important to understand land to ocean ecological connections and to provide the scientific basis for regional environmental management decisions. The Hakai Institute operates a densely networked and long-term hydrometeorological monitoring observatory that fills a spatial data gap in the remote and sparsely gauged outer coast of the NPCTR. Here we present the first 5 water years (October 2013–October 2019) of high-resolution streamflow and weather data from seven small (&lt; 13 km2) coastal watersheds. Measuring rainfall and streamflow in remote and topographically complex rainforest environments is challenging; hence, advanced and novel automated measurement methods were used. These methods, specifically for streamflow measurement, allowed us to quantify uncertainty and identify key sources of error, which varied by gauging location. Average yearly rainfall was 3267 mm, resulting in 2317 mm of runoff and 0.1087 km3 of freshwater exports from all seven watersheds per year. However, rainfall and runoff were highly variable, depending on the location and elevation. The seven watersheds have rainfall-dominated (pluvial) streamflow regimes, streamflow responses are rapid, and most water exports are driven by high-intensity fall and winter storm events. The complete hourly and 5 min interval datasets can be accessed at https://doi.org/10.21966/J99C-9C14 (Korver et al., 2021), and accompanying watershed delineations with metrics can be found at https://doi.org/10.21966/1.15311 (Gonzalez Arriola et al., 2015).

“Looking outside the forensic toxicology box – An approach to link results from the lab to public health”

Exposure to environmental contaminants from air pollution is being increasingly identified as a significant cause for a large portion of the global death burden and associated with severe adverse health outcomes, including pulmonary, cardiovascular or immunologic and neurologic diseases. Among these air pollutants, carbon monoxide (CO) finds its place as one of the main “suspects”. Especially in enclosed spaces CO poses a great health risk – whether its low level chronic exposures leading to permanent neurologic disorders or acute exposures that lead to death. In both cases, the job of a toxicologist is to accurately detect CO by using the appropriate analytical method. The job of an epidemiologist is to associate the CO levels to a health outcome. Errors in the exposure measurement can lead to real associations not being detected. Therefore, we reviewed measurement methods used for CO measurement and identified key sources of error, which could contribute to exposure misclassification. A novel approach to more accurately determine the total amount of CO in blood (TBCO) via gas-chromatography mass spectrometry (GC-MS) was developed and validated. The results obtained from this method were then used to “feed” CO exposure assessments. Exposure assessments in public health represent the tool that from exposure estimates lead to determination of risk estimates. Using the previously identified key sources of error together with the results obtained from the improved measurement method, we developed a simulation model to quantify the impact that changes in the error due to measurement improvement could have on the relative risk (RR) of CO exposure estimates in a study using exhaled breath CO (exCO) and personal CO monitors (pCO) to measure CO. Choice of biomarker, analytical method, number of samples/measurements, time of sampling, inter-individual variabilities and use of averages for population data were some of the identified potential sources of error and can contribute from 5% up to 80% to the total measurement error. The novel CO measurement method leads to improvements in CO concentrations ranging from 20%–80% (compared to currently used methods). When applying this to the simulation model, the RR was increased up to 18.3% for exCO and up to 30% for pCO. An improved measurement method for TBCO was validated and has shown promising results in improving accuracy of CO determinations in blood as well as showing better stability to a variety of storage conditions in clinical and forensic settings. Application of this method to exposure assessment data lead to significant reduction of measurement error, resulting in CO exposure estimates closer to the true CO burden on the population. Improvement of the CO measurement method not only directly affects the results obtained in clinical and forensic cases by allowing otherwise undetected cases to be known, but it can also indirectly influence the data that is used by public health authorities to create guidelines, to generate prevention, treatment and training plans, ultimately improving global health. The model used here for CO exposure can easily be expanded and applied to other types of exposures.

Validation of enabling technologies for deorbiting devices based on electrodynamic tethers

The increasing number of man-made objects in near-earth space is becoming a serious problem for future space missions around the Earth. Among the proposed strategies to face this issue, and due to the passive and propellant-less character, electrodynamic tethers appear to be a promising option for spacecraft in low Earth orbits thanks to the limited storage mass and the minimum interface requirements to the host spacecraft.This work presents the roadmap that the Electrodynamic Tether Technology for Passive Consumable-less Deorbit Kit (E.T.PACK) is following to develop a prototype of a deorbit device based on electrodynamic tether technology with Technology Readiness Level 4 by the end of 2022. The paper illustrates the roadmap of the activities carried out at the University of Padova, where software and hardware have been prepared to validate some of the critical elements of the deorbit device. Specifically, the software tools include: (a) the software called “DEPLOY” that allows the computation of a reference trajectory for the deployment of the tether and the completion of sensitivity analysis of the deployment trajectory to key error sources; (b) the software called “FLEXSIM” that predicts the performances of electrodynamic tethers as a function of the system configuration employed; and (c) the software called “FLEX” that includes the dynamical effects of tether flexibility and provides important information on the dynamic stability of the system during deployment and deorbiting phase. The paper describes in detail the three software tools and provides results of a simulation showing how it is possible to deorbit a 24-kg satellite from an initial orbital altitude of 600 km in less than 100 days using a 500-m long tape-like bare tether.The team has also developed laboratory mock-ups and performed experimental activities to: (a) determine the tether mechanical properties; (b) test the functionality of mechanisms used to deploy the tether; (c) test the functionality of the attitude control assembly used during the deployment phase; and (d) validate a passive damper designed for dissipating the longitudinal oscillations of the tether and thus guarantee the stability of the system during both deployment and deorbiting phase. The paper provides a description of both the laboratory setup and the experimental activities performed to validate EDT technologies, including the damping capability of a compact passive-damping mechanism, showing how it can reduce consistently the peak forces up to about 80%.

Sex and Prediction Error, Part 3: Provoking Prediction Error.

In parts 1 and 2 of this Lacanian neuropsychoanalytic series, surplus prediction error was presented as a neural correlate of the Lacanian concept of jouissance. Affective consciousness (a key source of prediction error in the brain) impels the work of cognition, the predictive work of explaining what is foreign and surprising. Yet this arousal is the necessary bedrock of all consciousness. Although the brain's predictive model strives for homeostatic explanation of prediction error, jouissance "drives a hole" in the work of homeostasis. Some residual prediction error always remains. Lacanian clinical technique attends to this surplus and the failed predictions to which this jouissance "sticks." Rather than striving to eliminate prediction error, clinical practice seeks its metabolization. Analysis targets one's mode of jouissance to create a space for the subject to enjoy in some other way. This entails working with prediction error, not removing or tolerating it. Analysis aims to shake the very core of the subject by provoking prediction error-this drives clinical change. Brief clinical examples illustrate this view.

Numeric Optimization for Configurable, Parallel, Error‐Robust Entangling Gates in Large Ion Registers

AbstractA class of entangling gates for trapped atomic ions is studied and the use of numeric optimization techniques to create a wide range of fast, error‐robust gate constructions is demonstrated. A numeric optimization framework is introduced targeting maximally‐ and partially‐entangling operations on ion pairs, multi‐ion registers, multi‐ion subsets of large registers, and parallel operations within a single register. Ions are assumed to be individually addressed, permitting optimization over amplitude‐ and phase‐modulated controls. Calculations and simulations demonstrate that the inclusion of modulation of the difference phase for the bichromatic drive used in the Mølmer–Sørensen gate permits approximately time‐optimal control across a range of gate configurations, and when suitably combined with analytic constraints can also provide robustness against key experimental sources of error. The impact of experimental constraints such as bounds on coupling rates or modulation band‐limits on achievable performance is further demonstrated. Using a custom optimization engine based on TensorFlow, for optimizations on ion registers up to 20 ions, time‐to‐solution of order tens of minutes using a local‐instance laptop is also demonstrated, allowing computational access to system‐scales relevant to near‐term trapped‐ion devices.

Analysis of monitoring data where butterflies fly year-round.

Butterfly Monitoring Schemes (BMSs) engage the public in conservation and provide data sets that cover broad geographical areas over long timescales. Most existing BMSs are in temperate climates; however, the Israeli Butterfly Monitoring Scheme (BMS-IL), established in 2009, is a notable exception as it encompasses a large climatic gradient from Euro-Siberian through Mediterranean to hyper-arid regions. Israel's climate poses challenges in analyzing data from year-round butterfly activity, as in other tropical or arid countries. The Regional Generalized Additive Model (Regional GAM) is a butterfly phenology and abundance model based on repeat visits throughout species' flight season. We tested the applicability of Regional GAM for species with complex flight seasonality (e.g., multivoltine) by comparing estimated abundance and seasonal indices for the full data set and rarefied subsets. We assessed the reliability of modeled flight seasons and compared abundance estimates per site resulting from biologically plausible and unreliable seasonal models. The reliability of Regional GAM rises with the number of observations, and the model tends to produce more biologically plausible models for species with simple phenologies (e.g., univoltine with a single peak in activity). Abundance estimates based on unreliable models produce values with inter-quartile ranges of 90%-153% compared with biologically plausible models, while peak time changes with an interquartile range of 0-22.5d when comparing all rarefied models with the full data set. Regional GAM should be applied with great caution for rare species and those with a complex flight season, and the date of year start needs to be carefully chosen for species that are active year-round. We identified the key sources of error and propose an operational workflow to address them. With few adaptations, Regional GAM can support new BMSs in analyzing data where butterflies are active year-round, including tropical climates. We propose guidelines for analyzing BMS data for species or regions with long activity periods and complex phenologies.

Measurements error propagation and its sensitivity analysis in the aero-engine multistage rotor assembling process.

The purpose of this paper is to research the measurements error propagation principle about the measurement-assembly equipment of aero engine multistage rotor, besides analyze the effect of guide linear motion and turntable rotational motion on the final alignment error. Furthermore, we can improve the measurement and assembly accuracy of the aero engine multistage rotor. In this paper, a novel error analysis method related to measurement-assembly equipment was proposed. First, the topology of the measuring equipment was established based on the multibody system theory, the error propagation path was constructed by using the low order body sorting method. Second, the error transfer model of the linear motion and the rotational motion were established by using the homogeneous coordinate transformation matrix, and the total measurement terminal errors introduced by the equipment were calculated. Finally, through numerical simulation, the magnitude of the measurement terminal offset error was obtained, sensitivity analysis was performed to calculate key error sources. Measurement accuracy not only depended on the accuracy of the sensor but also closely related to the accuracy of the measurement-assembly equipment. Through the simulation verification, when the linearity error is 0.1 μm and the angular error is 0.1″, the final cumulative offset displacement error is about -0.1002 μm to -0.0998 μm with a probability of 97%. Different type errors have different effects on the results. We can give a conclusion that linear guide alignment error and verticality error are the primary error sources, and the angle error of the turntable need to be improved.

Binding Thermodynamics of Host-Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative.

Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small-molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a druglike guest molecule have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations. The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root-mean-square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.

A Comparative Analysis of Equations to Estimate Patient Energy Requirements Following Cardiopulmonary Bypass for Correction of Congenital Heart Disease.

BackgroundNo consensus exists on the optimal method to estimate resting energy expenditure (REE) in critically ill children following cardiopulmonary bypass (CPB). This study assesses the accuracy of REE estimation equations in children with congenital heart disease following CPB and tests the feasibility of using allometric scaling as an alternative energy prediction equation.MethodsA retrospective analysis of a pediatric cohort following CPB (n = 107; median age 5.2 months, median weight 5.65 kg) who underwent serial measures (median 5 measurements) of REE using indirect calorimetry for 72 hours following CPB. We estimated REE using common estimation methods (Dietary Reference Intake, Harris Benedict, Schofield, World Health Organization [WHO]) as well as novel allometric equations. We compared estimated with measured REE to determine accuracy of each equation using overall discrepancy, calculated as a time‐weighted average of the absolute deviation.ResultsAll equations incorrectly estimated REE at all time points following CPB, with overestimation error predominating. WHO had the lowest discrepancy at 10.7 ± 8.4 kcal/kg/d. The allometric equation was inferior, with an overall discrepancy of 16.9 ± 10.4. There is a strong nonlinear relationship between body surface area and measured REE in this cohort, which is a key source of estimation error using linear equations.ConclusionIn a cohort of pediatric patients with congenital heart disease following CPB, no currently utilized clinical estimation equation reliably estimated REE. Allometric scaling proved inferior in estimating REE in children following CPB. Indirect calorimetry remains the ideal method of determining REE after CPB until nonlinear methods can be derived due to overestimation using linear equations.