Discrepancies In Predictions Research Articles

Analytical, Numerical, and Experimental Investigations on Transverse Bending Responses of CFRP Square Tube Filled with Aluminum Foam

In order to explore the transverse bending responses of carbon fiber-reinforced polymer (CFRP) square tubes filled with aluminum foam, the three-point bending tests were carried out on an INSTRON machine, the full-field deformation measurement was performed using a 3D-DIC test system, the numerical model was established by ABAQUS/Explicit, and the bending stiffness was calculated by the improved analytical model based on shear-deformable beam theory. The discrepancies of experimental data, numerical results, and analytical predictions were acceptable, which were within 5%. The failure modes and mechanical properties of the filled tubes were experimentally captured and numerically predicted. Due to the filling effect of aluminum foam, the ultimate load, bending stiffness, and energy absorption of the filled CFRP square tubes increased, comparing to those of the hollow CFRP square tubes. With the increase of the aluminum foam density, the ultimate load, bending stiffness, and energy absorption of the filled tubes increased, while the specific ultimate load, specific bending stiffness, and specific energy absorption decreased.

Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is proposed to solve the unmanned aerial vehicle (UAV) positioning problem in the presence of the global positioning system (GPS) spoofing attack, supposing that the GPS spoofing effects are in the form of unknown but bounded errors. To cope with the GPS spoofing attacks as unknown sudden changes of system state variables, the compensation of the GPS spoofing effects is adaptively done in two basic parts of PDIPF algorithm including particle weighting and covariance matrix adaption. In addition, a theorem is developed which verifies that the output estimation error is upper bounded by a given probability with the help of the adapted covariance matrix. Besides, the particle weight calculation in PDIPF is done with respect to the prediction discrepancy of generated particles from the GPS measurements. The proposed PDIPF is used to decrease the effects of any GPS spoofing errors with different probability density functions and estimate true position of UAV in the presence of the GPS spoofing attacks. The algorithm is applied to the inertial navigation system/GPS/Loran-C integration systems. Simulation results demonstrate the effectiveness of the proposed PDIPF algorithm in terms of accuracy and redundancy.

Ethical limitations of algorithmic fairness solutions in health care machine learning

Ethical limitations of algorithmic fairness solutions in health care machine learning

Revisiting Scaling Laws for Robotic Mobility in Granular Media

The development, building, and testing of robotic vehicles for applications in deformable media can be costly. Typical approaches rely on full-sized builds empirically evaluating performance metrics such as drawbar pull and slip. Recently developed granular scaling laws offer a new opportunity for terramechanics as a field. Using non-dimensional analysis on the wheel characteristics and treating the terrain as a deformable continuum, the performance of a larger, more massive wheel may be predicted from a smaller one. This allows for new wheel design approaches. However, robot-soil interaction and specific characteristics of the soil or robot dynamics may create discrepancies in prediction. In particular, we find that for a lightweight rover (2–5 kg), the scaling laws significantly overpredicted mechanical power requirements. To further explore the limitations of the current granular scaling laws, a pair of differently sized grousered wheels were tested at three masses and a pair of differently sized sandpaper wheels were tested at two masses across five speeds. Analysis indicates similar error for both designs, a mass dependency for all five pairs that explains the laws’ overprediction, and a speed dependency for both of the heaviest sets. The findings create insights for using the laws with lightweight robots in granular media and generalizing granular scaling laws.

Development of Hybrid-Vehicle Energy-Consumption Model for Transportation Applications—Part I: Driving-Power Equation Development and Coefficient Calibration

This study is the first of a two-part paper. The overall study presents a new methodology to improve the accuracy of hybrid vehicles’ energy-consumption model over conventional transportation modeling methods. The first paper attempts to improve an equation for vehicles’ driving-power estimation to be more realistic and specific for a particular vehicle model or fleet. The second paper adopts the driving-power equation to estimate the requested driving power. Then, the data are utilized to construct the hybrid-vehicle energy-consumption model, namely, the traction-force–speed-based energy-consumption model (TFS model). The main concept of the first paper is to utilize the power-split hybrid powertrain’s accessible on-board diagnostics (OBD) dataset, and its dynamic model to estimate the total propulsion power. Then, propulsion power was applied as the main parameter for driving-power equation development and vehicle-specific coefficient calibration. For coefficient calibration, this study implemented the stepwise multiple regression method to select and calibrate an optimal set of coefficients. Results showed that conventional driving-power equations Vehicle-Specific Power (VSP) LDV 1999 and VSP Prius3Spec provide low prediction fidelity, especially under high-speed (>80 km/h) and heavy-load driving (≥50 kW). In contrast, D r v P w P r i u s 3 , proposed in this study, effectively improved prediction to become more accurate and reliable through all driving conditions and speed ranges. It dramatically helped to reduce prediction discrepancy over the conventional equations at heavy-load driving, from an R-square of 0.79 and 0.78 to 0.96. D r v P w P r i u s 3 also the prediction error at high-speed driving from the maximal error of approximately −20 to −5 kW. This study also discovered that aerodynamics and rolling resistance were the primary factors that caused the prediction error of conventional VSP equations. In addition, results in this study showed that both of the approaches used to establish the P P T d r v and D r v P w P r i u s 3 equations were valid for a power-split hybrid vehicle’s driving-power estimation. For the coefficient-calibration part, the stepwise and multiple regression method is low-cost and simple, allowing to calibrate an appropriate set of optimal coefficients for a specific vehicle model or fleet.

Peculiar bond characters of fivefold coordinated octet compound crystals.

The present work exemplifies complementary perspectives offered by the band and bond pictures of solids, with an emphasis on the chemical intuition pertaining to the latter, especially in the presence of interfaces. The modern computational method of constructing a unique set of maximally localized Wannier functions from delocalized band states imparts new interpretations to the familiar concept of chemical bonds in the context of crystalline solids. By bridging the band and bond pictures using advanced computational tools, we reveal for the first time the unusual bond characters of a long-predicted fivefold coordinated structure of binary octet compounds ANB8−N consisting of AA′ stacked planar AB honeycombs. While the isolated monolayer retains the familiar pz–π bonding in a honeycomb framework as in graphene and hexagonal boron nitride, the bulk foregoes in-plane π bonding and embraces out-of-plane ⋯A–B–A–B⋯ chain bonding via overlapping pz orbitals. Not only does the chemical intuition gained by invoking the bond picture clarify the chemical nature of the fivefold coordination, but it also facilely explains a salient discrepancy in theoretical predictions in otherwise sound ample experimental evidence in the form of epitaxial thin films, paving the way towards rational synthesis of such thin films for optoelectronic applications. On the other hand, we show that the conduction band minimum, important in determining the electrical and optical properties, is a distinctly extended state that can only be properly described within the band picture.

Bayesian cross-validation of geostatistical models

The problem of validating or criticizing models for georeferenced data is challenging as much as conclusions may be sensitive to the partition of data into training and validation cases. This is an obvious issue related to the basic validation scheme which selects a subset of the data to leave out of estimation and to make predictions with an assumed model. In this setup, only a few out-of-sample locations are usually selected to validate the model. On the other hand, the cross-validation approach, which considers several possible configurations of data divided into training and validation observations, is an appealing alternative, but it could be computationally demanding as the estimation of parameters usually requires computationally intensive methods. The purpose of this work is to use cross-validation techniques to choose between competing models and to assess the goodness of fit of spatial models in different regions of the spatial domain. We consider the sampling design for selecting the training and validation sets by assigning a probability distribution to the possible data partitions. To deal with the computational burden of cross-validation, we estimate discrepancy functions in a computationally efficient manner based on the importance weighting of posterior samples. Furthermore, we propose a stratified cross-validation scheme to take into account spatial heterogeneity, reducing the total variance of estimated predictive discrepancy measures. We also illustrate the advantages of our proposal with simulated examples of homogeneous and inhomogeneous spatial processes and with an application to rainfall dataset in Rio de Janeiro.

Impulsive Loading Noise of a Lift-Offset Coaxial Rotor in High-Speed Forward Flight

This study was conducted to investigate the impulsive acoustic characteristics of a lift-offset coaxial rotor in high-speed forward flight, as well as the impact of wake-grid resolution and turbulence modeling on acoustic prediction. High-fidelity computational fluid dynamics/computational structural dynamics loose-coupling simulations were performed for air-load prediction using the high-fidelity rotorcraft aeromechanics simulation software Helios, and impulsive loading noise was simulated using the acoustic prediction software PSU-WOPWOP at eight microphones positioned below the lower rotor plane. No significant discrepancy in air-load and acoustic predictions was found between the three turbulence modeling cases [Spalart–Allmaras (SA)–Reynolds-averaged Navier–Stokes, SA–detached-eddy simulation, and SA–delayed-detached-eddy simulation] with . It was found that the case showed a good convergence of impulsive loading noise at most microphones in terms of the wake-grid resolution. Isolated rotor simulations were also carried out to quantify the relative importance of the two impulsive loading-noise sources of a lift-offset coaxial rotor: self-blade–vortex interaction (BVI) and blade-crossover events. Contours of induced inflow ratio on the rotor disks revealed higher induced velocity for the lift-offset coaxial rotor than the isolated rotors. The higher induced flow ratio reduced the distribution of sectional normal force, strength of self-BVIs, and BVI noise of the lift-offset coaxial rotor. Even with the reduced BVI noise, the lift-offset coaxial rotor showed a dramatically higher impulsive sound-pressure level than the isolated rotors, especially at high speed, as a consequence of mainly blade-crossover events.

Comparison of experimental and simulated critical heat flux tests with various cladding alloys: Sensitivity of iron-chromium-aluminum (FeCrAl) to heat transfer coefficients and material properties

In this work we analyze the differences between previous transient critical heat flux (CHF) experiments using iron-chromium-aluminum (FeCrAl), Inconel 600, and stainless steel 316 (SS316) alloy test sections and best-estimate modeling results from widely-used nuclear engineering systems and subchannel analysis tools. FeCrAl is an Accident Tolerant Fuel (ATF) candidate cladding material. The thermal hydraulic performance and safety characteristics of FeCrAl are being evaluated to determine viability as a cladding material in Light Water Reactors (LWRs). In this study, the results of the CHF experiments conducted at atmospheric pressure and fixed inlet coolant temperature and mass flux are compared to models built in the fifth version of the Reactor Excursion and Leak Analysis Program (RELAP5-3D) and CTF, the modernized version of COBRA-TF developed by the Consortium for Advanced Simulation of LWRs (CASL). Results from RELAP5-3D and CTF showed differences from the experiments and from each other in predicting CHF. In the Inconel 600 case, both computational tools overpredicted CHF, which led to an underprediction in the tube outer surface temperature. In the SS316 and FeCrAl cases, CHF was underpredicted by the codes, leading to an overprediction of the tube outer surface temperature.To understand the discrepancies in CHF and post-CHF predictions, studies were performed using RELAP5-3D and RAVEN to determine the sensitivity of CHF and peak test section temperature, an analog to peak cladding temperature (PCT), to heat transfer coefficients, a CHF multiplier, and uncertainties in the thermal conductivity and volumetric heat capacity. We found that CHF depends most strongly on the CHF multiplier and thermophysical properties. A combination of these factors that produced the best match to the experiment based on CHF, PCT, and the total energy deposited into the tube was determined. The best match parameters were able to provide best-estimate predictions of the CHF and integral heat flux, but were still conservative when predicting the PCT. The best match set of parameters developed in this paper are intended only as a demonstration of an approach that could be applied in the future with a larger set of experiments to produce more accurate models of CHF and post-CHF behavior.

Spreadsheet-based method for predicting temperature dependence of fracture toughness in ductile-to-brittle temperature region

A spreadsheet-based simplified and direct toughness scaling method to predict the temperature dependence of fracture toughness Jc in the ductile-to-brittle transition temperature region is proposed. This method uses fracture toughness test data and the Ramberg–Osgood exponent and yield stress at the reference temperature, and yield stress at the temperature in interest to predict Jc. The physical basis of the simplified and direct toughness scaling method is the strong correlation between Jc and yield stress. The simplified and direct toughness scaling method was validated for Cr–Mo steel Japan Industrial Standard SCM440 and 0.55% carbon steel Japan Industrial Standard S55C by comparing the simplified and direct toughness scaling prediction results with the median results of an experiment performed at four temperatures ranging from −55°C to 100°C and at three temperatures ranging from −85°C to 20°C, respectively. The simplified and direct toughness scaling method can predict Jc from both low to high temperatures, and vice versa. Thus, 12 and 6 predictions were made for each material. The prediction discrepancy for these 18 cases ranged from −50.4% to +25.8% and the average absolute discrepancy was 22.1%. These results were acceptable considering the large scatter generally observed with Jc. In particular, in case of predicting Jc at temperatures higher than the lowest temperature of −55°C for SCM440, the simplified and direct toughness scaling method predicted Jc more realistically than the American Society for Testing and Materials E1921 master curve approach. Although the simplified and direct toughness scaling method requires additional tensile test data compared with the master curve approach, the acceptable prediction accuracy at high temperatures seems beneficial because the mass and time required for tensile tests are admissible.

Inaccurate secondary structure predictions often indicate protein fold switching.

Although most proteins conform to the classical one‐structure/one‐function paradigm, an increasing number of proteins with dual structures and functions have been discovered. In response to cellular stimuli, such proteins undergo structural changes sufficiently dramatic to remodel even their secondary structures and domain organization. This “fold‐switching” capability fosters protein multi‐functionality, enabling cells to establish tight control over various biochemical processes. Accurate predictions of fold‐switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Recently, we developed a prediction method for fold‐switching proteins using structure‐based thermodynamic calculations and discrepancies between predicted and experimentally determined protein secondary structure (Porter and Looger, Proc Natl Acad Sci U S A 2018; 115:5968–5973). Here we seek to leverage the negative information found in these secondary structure prediction discrepancies. To do this, we quantified secondary structure prediction accuracies of 192 known fold‐switching regions (FSRs) within solved protein structures found in the Protein Data Bank (PDB). We find that the secondary structure prediction accuracies for these FSRs vary widely. Inaccurate secondary structure predictions are strongly associated with fold‐switching proteins compared to equally long segments of non‐fold‐switching proteins selected at random. These inaccurate predictions are enriched in helix‐to‐strand and strand‐to‐coil discrepancies. Finally, we find that most proteins with inaccurate secondary structure predictions are underrepresented in the PDB compared with their alternatively folded cognates, suggesting that unequal representation of fold‐switching conformers within the PDB could be an important cause of inaccurate secondary structure predictions. These results demonstrate that inconsistent secondary structure predictions can serve as a useful preliminary marker of fold switching.

An analytical study of vibration response of a beam stiffened Mindlin plate

This paper presents an analytical solution for the vibration response of a beam-stiffened Mindlin plate using a modal expansion solution approach. The model is utilized to evaluate the prediction discrepancy using a thin (ribbed) plate model. It is found that using a thin plate model would yield a less than 5 percent error when the plate bending wavelength to thickness ratio is greater than 12. The study also investigates the effect of the rib insertion on the plate modal response for different plate thickness. The stiffened beam behaves as an effective boundary for some plate modes when the plate thickness is small but behaves more like a mass attachment for the same modes when the plate thickness becomes large attributing to the increasing shear deformation effect. It is shown that the use of Kirchhoff plate model contributes more to the prediction discrepancy than the use of Euler-Bernoulli beam in a ribbed plate model. It is also found that shear deformation of the plate plays a more dominant role in affecting the prediction accuracy of a beam stiffened plate than the rotatory inertia, which is the main factor causing the difference between results predicted using Mindlin and Kirchhoff plate models.

Unsupervised Blind Image Quality Evaluation via Statistical Measurements of Structure, Naturalness, and Perception

Most existing blind image quality assessment (BIQA) methods belong to supervised methods, which always need a large number of image samples and expensive subjective scores for training a quality prediction model. In this paper, we focus our attention on the unsupervised BIQA methods and put forward a novel unsupervised approach. The main idea of our method is to quantify the image quality degradation through measuring the structure, naturalness, and the perception quality variations of the distorted image from the pristine natural images. In specific, the structure variation is captured by the deviations of the image phase congruency and gradients distributions. The naturalness variation is characterized through the distributions variations of the locally mean subtracted and contrast normalized (MSCN) coefficients and the products of pairs of the adjacent MSCN coefficients. Compared with existing unsupervised methods, we initiatively introduce the perception quality measurement into the construction of unsupervised BIQA method, which is conducted by characterizing the prediction discrepancy between the image and its brain prediction based on the free-energy principle in the newly revealed brain theory. After feature extraction, we learn a pristine multivariate Gaussian (MVG) model with the extracted features from a set of pristine natural images. The quality of a new image is finally defined as the distance between its MVG model and the learned pristine MVG model. The extensive experiments conducted on LIVE, TID2013, CSIQ, Toyama, CID2013, and the Waterloo Exploration databases demonstrate that the proposed method achieves comparative prediction performance with the state-of-the-art BIQA methods.

Multi-Scenario Species Distribution Modeling.

Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty.

Assessment of Turbulence Models in a Hypersonic Cold-Wall Turbulent Boundary Layer

In this study, the ability of standard one- or two-equation turbulence models to predict mean and turbulence profiles, the Reynolds stress, and the turbulent heat flux in hypersonic cold-wall boundary-layer applications is investigated. The turbulence models under investigation include the one-equation model of Spalart–Allmaras, the baseline k - ω model by Menter, as well as the shear-stress transport k - ω model by Menter. Reynolds-Averaged Navier-Stokes (RANS) simulations with the different turbulence models are conducted for a flat-plate, zero-pressure-gradient turbulent boundary layer with a nominal free-stream Mach number of 8 and wall-to-recovery temperature ratio of 0.48 , and the RANS results are compared with those of direct numerical simulations (DNS) under similar conditions. The study shows that the selected eddy-viscosity turbulence models, in combination with a constant Prandtl number model for turbulent heat flux, give good predictions of the skin friction, wall heat flux, and boundary-layer mean profiles. The Boussinesq assumption leads to essentially correct predictions of the Reynolds shear stress, but gives wrong predictions of the Reynolds normal stresses. The constant Prandtl number model gives an adequate prediction of the normal turbulent heat flux, while it fails to predict transverse turbulent heat fluxes. The discrepancy in model predictions among the three eddy-viscosity models under investigation is small.

Turbulence modeling effects on the aerodynamic characterizations of a NASCAR Generation 6 racecar subject to yaw and pitch changes

The characterization of a racecar’s aerodynamic behavior at various yaw and pitch configurations has always been an integral part of its on-track performance evaluation in terms of lap time predictions. Although computational fluid dynamics has emerged as the ubiquitous tool in motorsports industry, a clarity is still lacking about the prediction veracity dependence on the choice of turbulence models, which is central to the prediction variability and unreliability for the Reynolds Averaged Navier–Stokes simulations, which is by far the most widely used computational fluid dynamics methodology in this industry. Subsequently, this paper presents a comprehensive assessment of three commonly used eddy viscosity turbulence models, namely, the realizable [Formula: see text] (RKE), Abe–Kondoh–Nagano [Formula: see text], and shear stress transport [Formula: see text], in predicting the aerodynamic characteristics of a full-scale NASCAR Monster Energy Cup racecar under various yaw and pitch configurations, which was never been explored before. The simulations are conducted using the steady Reynolds Averaged Navier–Stokes approach with unstructured trimmer cells. The tested yaw and pitch configurations were chosen in consultation with the race teams such that they reflect true representations of the racecar orientations during cornering, braking, and accelerating scenarios. The study reiterated that the prediction discrepancies between the turbulence models are mainly due to the differences in the predictions of flow recirculation and separation, caused by the individual model’s effectiveness in capturing the evolution of adverse pressure gradient flows, and predicting the onset of separation and subsequent reattachment (if there be any). This paper showed that the prediction discrepancies are linked to the computation of the turbulent eddy viscosity in the separated flow region, and using flow-visualizations identified the areas on the car body which are critical to this analysis. In terms of racecar aerodynamic performance parameter predictions, it can be reasonably argued that, excluding the prediction of the %Front prediction, shear stress transport is the best choice between the three tested models for stock-car type racecar Reynolds Averaged Navier–Stokes computational fluid dynamics simulations as it is the only model that predicted directionally correct changes of all aerodynamic parameters as the racecar is either yawed from the 0° to 3° or pitched from a high splitter-ground clearance to a low one. Furthermore, the magnitude of the shear stress transport predicted delta force coefficients also agreed reasonably well with test results.

Bayesian estimation of discrepancy in dynamics model prediction

Abstract We present a methodology to predict the discrepancy in modeling dynamic system response for untested system inputs. In particular, the methodology focuses on multi-component systems, where discrepancy in the system response is caused by model form errors in some of the system components. We combine a deterministic time-series artificial neural network (ANN) with Bayesian state estimation to formulate a relationship between inputs to the system and the corresponding discrepancies in the model estimates of system responses. We also identify the system components affected by model form errors, and predict discrepancies even in unmeasured locations of the system. The proposed approach is illustrated on a linear multi-degree of freedom dynamic system, and on an air cycle machine – a multi-component dynamic system.

Shape evolution of Zr nuclei and roles of the tensor force

Shape evolution of Zr nuclei are investigated by the axial Hartree-Fock (HF) calculations using the semi-realistic interaction M3Y-P6, with focusing on roles of the tensor force. Deformation at $N\approx 40$ is reproduced, which has not been easy to describe within the self-consistent mean-field calculations. The spherical shape is obtained in $46\leq N\leq 56$, and the prolate deformation is predicted in $58\leq N\leq 72$, while the shape switches to oblate at $N=74$. The sphericity returns at $N=80$ and $82$. The deformation in $60\lesssim N\lesssim 70$ resolves the discrepancy in the previous magic-number prediction based on the spherical mean-field calculations [Prog. Theor. Exp. Phys. \textbf{2014}, 033D02]. It is found that the deformation at $N\approx 40$ takes place owing to the tensor force with a good balance. The tensor-force effects significantly depend on the configurations, and are pointed out to be conspicuous when the unique-parity orbit (e.g. $n0h_{11/2}$) is present near the Fermi energy, delaying deformation. These effects are crucial for the magicity at $N=56$ and for the predicted shape change at $N=74$ and $80$.

The first cohort of prospective Fontan surgical planning patients with follow-up data: How accurate is surgical planning?

ObjectiveFontan surgical planning is an image-based, collaborative effort, which is hypothesized to result in improved patient outcomes. A common motivation for Fontan surgical planning is the progression (or concern for progression) of pulmonary arteriovenous malformations. The purpose of this study was to evaluate the accuracy of surgical planning predictions, specifically hepatic flow distribution (HFD), a known factor in pulmonary arteriovenous malformation progression, and identify methodological improvements needed to increase prediction accuracy. MethodsTwelve single-ventricle patients who were enrolled in a surgical planning protocol for Fontan surgery with pre- and postoperative cardiac imaging were included in this study. Computational fluid dynamics were used to compare HFD in the surgical planning prediction and actual postoperative conditions. ResultsOverall, HFD prediction error was 17 ± 13%. This error was similar between surgery types (15 ± 18% and 18 ± 10% for revisions vs Fontan completions respectively; P = .73), but was significantly lower (6 ± 7%; P = .05) for hepatic to azygous shunts. Y-grafts and extracardiac conduits showed a strong correlation between prediction error and discrepancies in graft insertion points (r = 0.99; P < .001). Improving postoperative anatomy prediction significantly reduced overall HFD prediction error to 9 ± 6% (P = .03). ConclusionsAlthough Fontan surgical planning can offer accurate HFD predictions for specific graft types, methodological improvements are needed to increase overall accuracy. Specifically, improving postoperative anatomy prediction was shown to be an important target for future work. Future efforts and refinements to the surgical planning process will benefit from an improved understanding of the current state and will rely heavily on increased follow-up data.

Applying generalized allometric regressions to predict live body mass of tropical and temperate arthropods.

The ecological implications of body size extend from the biology of individual organisms to ecosystem‐level processes. Measuring body mass for high numbers of invertebrates can be logistically challenging, making length–mass regressions useful for predicting body mass with minimal effort. However, standardized sets of scaling relationships covering a large range in body length, taxonomic groups, and multiple geographical regions are scarce. We collected 6,212 arthropods from 19 higher‐level taxa in both temperate and tropical locations to compile a comprehensive set of linear models relating live body mass to a range of predictor variables. We measured live weight (hereafter, body mass), body length and width of each individual and conducted linear regressions to predict body mass using body length, body width, taxonomic group, and geographic region. Additionally, we quantified prediction discrepancy when using parameters from arthropods of a different geographic region. Incorporating body width into taxon‐ and region‐specific length–mass regressions yielded the highest prediction accuracy for body mass. Using regression parameters from a different geographic region increased prediction discrepancy, causing over‐ or underestimation of body mass depending on geographical origin and whether body width was included. We present a comprehensive range of parameters for predicting arthropod body mass and provide guidance for selecting optimal scaling relationships. Given the importance of body mass for functional invertebrate ecology and the paucity of adequate regressions to predict arthropod body mass from different geographical regions, our study provides a long‐needed resource for quantifying live body mass in invertebrate ecology research.