Model Overpredicts Research Articles

An Investigation of the SOCOLv4 Model’s Suitability for Predicting the Future Evolution of the Total Column Ozone

The anthropogenic impact on the ozone layer is expressed in anomalies in the total ozone content (TOC) on a global scale, with periodic enhancements observed in high-latitude areas. In addition, there are significant variations in TOC time trends at different latitudes and seasons. The reliability of the TOC future trends projections using climate chemistry models must be constantly monitored and improved, exploiting comparisons against available measurements. In this study, the ability of the Earth’s system model SOCOLv4.0 to predict TOC is evaluated by using more than 40 years of satellite measurements and meteorological reanalysis data. In general, the model overpredicts TOC in the Northern Hemisphere (by up to 16 DU) and significantly underpredicts it in the South Pole region (by up to 28 DU). The worst agreement was found in both polar regions, while the best was in the tropics (the mean difference constitutes 4.2 DU). The correlation between monthly means is in the range of 0.75–0.92. The SOCOLv4 model significantly overestimates air temperature above 1 hPa relative to MERRA2 and ERA5 reanalysis (by 10–20 K), particularly during polar nights, which may be one of the reasons for the inaccuracies in the simulation of polar ozone anomalies by the model. It is proposed that the SOCOLv4 model can be used for future projections of TOC under the changing scenarios of human activities.

Computational multiphase mixture simulations of a two-phase R-744 ejector geometry in transcritical R-744 heat pump applications

Purpose Two-phase R-744 ejectors are critical components enabling energy recovery in R-744 heat pump and refrigeration systems, but despite their simple geometry, the flow physics involve complex multiphase mixing phenomena that need to be well-quantified for component and overall system improvement. This study aims to report on multiphase mixture simulations for a specific two-phase R-744 ejector with supercritical inlet conditions at the motive inlet side. Design/methodology/approach Four different operating conditions, which have motive inlet pressure range of 90.1 bar–101.1 bar, are selected from an existing experimental data set. A two-phase thermodynamic equilibrium (TPTE) model is used, where the fluid properties are described by a thermodynamic look-up table. Findings The results show that the TPTE model overpredicts mass flow rates at the motive inlet, resulting in a relative error ranging from 15.6% to 21.7%. For the mass flow rate at the suction inlet, the relative errors are found less than 1.5% for three cases, while the last case has an error of 12.4%. The maximum deviation of the mass entrainment ratio is found to be 8.0% between the TPTE model and the experimental data. Ejector efficiency ranges from 25.4% to 28.0%. A higher pressure difference between the ejector outlet and the diverging nozzle exit provides greater pressure lift. Research limitations/implications Based on the results, near future efforts will be to optimize estimation errors while enabling more detailed field analysis of pressure, density, temperature and enthalpy in the computational domain. Originality/value The authors have two main original contributions: 1) the presented thermodynamic look-up table is unique and provides unique computation for the real-scale ejector domain. It was created by the authors and has not been applied before as far as we know. 2) To the best of the authors’ knowledge, this study is the first study that applies the STAR-CCM+ multiphase mixture model for R-744 mixture phenomena in heat pumps and refrigeration systems.

Calibration and development of safety performance functions for two-way stop-control intersections on rural two-lane highways in Louisiana

The first edition of the Highway Safety Manual (HSM) contains a simplistic version of the crash prediction model for two-way stop-controlled intersections (TWSC) on rural two-lane highways. This model considers AADT on major and minor roads, with the base conditions defined as no intersection skewness, no turning lanes, and no lighting. A crash modification factor (CMF) will be applied if an intersection has conditions different from the base condition. However, the HSM model does not take account of curvature. It is well known that curved TWSC intersections are less safe than non-curved ones, particularly on rural two-lane roadways. This paper presents the development of crash prediction models incorporating intersection geometrics for TWSC intersections on rural two-lane highways in Louisiana. Then, it compares the results from the developed model with the calibrated HSM model. The negative binomial model was used with 5126 TWSC intersections verified one by one, including both three- and four-leg intersections from all parishes (counties). The estimation results indicate that AADT, curve radius, and intersection skewness angle significantly impact expected crash frequency for both three- and four-leg intersections. This research utilizes cumulative residual plots, mean absolute error, and root mean square error for comparative analysis of HSM models, HSM models with calibration, and Louisiana-specific models. The results show that Louisiana-specific SPFs outperformed the calibrated SPFs with superior reliability. Calibration factors of 0.58 for three-leg intersections and 0.46 for four-leg intersections are estimated, suggesting that the original HSM model overpredicts crashes in Louisiana.

Experimental and numerical investigation of high-pressure methane catalytic synthesis from H2 and CO2

The catalytic synthesis of methane from H2 and CO2, which is an efficient way for the production of synthetic natural gas (SNG) by utilizing the H2 originating from carbon-free renewable power sources and the greenhouse gas CO2, was investigated in a non-isothermal channel-flow reactor coated with Ni/Al2O3 at pressures 1.5–10 bar, H2:CO2 volumetric ratios 4–6, and surface temperatures 450–760 K. In situ, spatially resolved Raman measurements of gas-phase species concentrations across the channel height were complemented by gas chromatography analysis of the exhaust gas. Two-dimensional simulations were carried out, using elementary catalytic and gaseous chemical reaction mechanisms and detailed transport. Comparisons of the Raman data and simulations revealed that the preferential diffusion of H2 drastically altered the local H2:CO2 ratios at the catalytic walls, which attained values up to 8 times higher than the corresponding inlet values. This behavior exemplified the caution needed when performing kinetic studies in the mixed kinetically/transport controlled regime. The production of methane increased with increasing pressure and decreasing H2:CO2 ratio. At the highest ratio H2:CO2 = 6, the simulations underpredicted (overpredicted) the measured reactant (product) mole fractions, however, as H2:CO2 decreased, the trend was reversed. The discrepancies reduced with increasing pressure, resulting in a model overprediction of methane by 7.4% at 10 bar. Methane was produced from C(s) via successive hydrogenation. C(s) was in turn produced from COOH(s), followed by dissociation of COOH(s) to CO(s), and finally H-assisted dissociation of CO(s) to C(s). Gas-phase chemistry was negligible over the investigated conditions. The in-channel Raman measurements in conjunction with the exhaust gas analysis facilitated the construction of a global step for the studied reaction. The methane synthesis was assessed to follow a ∼p0.41 pressure dependence over the temperature range 520–680 K.

Improve the capability of physical model for runoff and sediment yield modelling with a hybrid artificial intelligence-based error updating system

Uncertainty of physical models is caused by the model's complexity as well as the accuracy of input variables. In present study, an error update model was developed based on artificial intelligence, namely Wavelet Multilayer Perceptron (WMLP), Wavelet Support Vector Machine (WSVM), Wavelet Multilayer Perceptron-Genetic Algorithm (WMLP-GA), and Wavelet Support Vector Machine-Genetic Algorithm (WSVM-GA). Sensitivity findings demonstrate that groundwater recession factor (ALPHA_BF), plant uptake compensation factor (EPCO), and curve number were the most sensitive for runoff in the Shivnath River basin, India, as well as four distinctparameters were shown to be highly sensitive to sediment yield at the Sigma station. During the calibration and validation phase, the SWAT model overpredicts runoff at the Simga and Pathardhi stations while underpredicting it at the Kotni station. SWAT model performance for runoff modelling is enhanced via the use of statistical indicators like as Percent of Bias (PBIAS) decreased from −35.355 to −20.503, Root Mean Square Error (RMSE) decreased from 319.905 to 25.191 m3/sec, and coefficient of determination increase from 0.712 to 0.904. At Simga station, the Nash–Sutcliffe efficiency (NSE) increase from 0.315 to 0.745, the PBIAS decreased from −39.502 to –22.946, the RMSE decreased from 186.711 to 24.432 m3/sec, and the coefficient of determination increased from 0.733 to 0.996. Further, NSE increased from 0.305 to 0.985 at Kotni station and PBIAS reduced from −114.026 to −9.214, RMSE reduced from 82.205 to 31.082 m3/sec, coefficient of determination increased from 0.779 to 0.976, NSE increased from 0.523 to 0.927 at Pathardhi station after applying an error update model. For sediment yield prediction, similar outcomes to runoff were obtained. The WSVM-GA model was reported to be the optimum error update model both for runoff and sediment yield modelling. It was recommended that the SWAT model might be applied for runoff and sediment yield simulation in the future.

Optimization of radial extrusion and pellet coating processes using PAT approaches

FDA's initiative Pharmaceutical CGMPs for the 21st century opened the door for introduction of several risk based approaches in pharmaceutical industry. One significant advancement that has emerged is the implementation of process analytical technology (PAT), which has opened doors for understanding and controlling complex technological processes. Two such processes, radial extrusion and pellet coating, offer a solid foundation for the application of PAT tools due to their numerous critical process parameters. The aim of the first part of the study was to optimize the neutral pellet production to produce the pellets with properties desired for successful film coating using design of experiments (DoE). In the second part the optimized pellets underwent film coating and the coating quantity was predicted in real or near real-time using in-line and at-line NIR probes and the performance of both probes was compared.The desired properties of the pellets, narrow particle size distribution, high sphericity and high process yield, were successfully achieved. Models for film coating quantity prediction using in-line and at-line NIR probe were successfully calibrated and tested by coating two additional batches. Despite the limited sample size for model calibration, at-line NIR exhibited excellent prediction performance and enabled accurate determination of process end-point. The coating quantity determined by UV/VIS spectroscopy in both test batches deviated by less than 2.0 % from the target value. However, the in-line NIR probe, primarily due to its inferior spectral resolution, displayed a slightly lower quality of the calibrated model and notable overprediction for the tested batches.

A Retrospective Cohort Study to Evaluate Adding Biomarkers to the Risk Analysis Index of Frailty

IntroductionThe Risk Analysis Index (RAI) is a frailty assessment tool associated with adverse postoperative outcomes including 180 and 365-d mortality. However, the RAI has been criticized for only containing subjective inputs rather than including more objective components such as biomarkers. MethodsWe conducted a retrospective cohort study to assess the benefit of adding common biomarkers to the RAI using the Veterans Affairs Surgical Quality Improvement Program (VASQIP) database. RAI plus body mass index (BMI), creatinine, hematocrit, and albumin were evaluated as individual and composite variables on 180-d postoperative mortality. ResultsAmong 480,731 noncardiac cases in VASQIP from 2010 to 2014, 324,320 (67%) met our inclusion criteria. Frail patients (RAI ≥30) made up to 13.0% of the sample. RAI demonstrated strong discrimination for 180-d mortality (c = 0.839 [0.836-0.843]). Discrimination significantly improved with the addition of Hematocrit (c = 0.862 [0.859-0.865]) and albumin (c = 0.870 [0.866-0.873]), but not for body mass index (BMI) or creatinine. However, calibration plots demonstrate that the improvement was primarily at high RAI values where the model overpredicts observed mortality. ConclusionsWhile RAI's ability to predict the risk of 180-d postoperative mortality improves with the addition of certain biomarkers, this only observed in patients classified as very frail (RAI >49). Because very frail patients have significantly elevated observed and predicted mortality, the improved discrimination is likely of limited clinical utility for a frailty screening tool.

A preference for cold dark matter over Superfluid Dark Matter in local Milky Way data

There are many well-known correlations between dark matter and baryons that exist on galactic scales. These correlations can essentially be encompassed by a simple scaling relation between observed and baryonic accelerations, historically known as the Mass Discrepancy Acceleration Relation (MDAR). The existence of such a relation has prompted many theories that attempt to explain the correlations by invoking additional fundamental forces on baryons. The standard lore has been that a theory that reduces to the MDAR on galaxy scales but behaves like cold dark matter (CDM) on larger scales provides an excellent fit to data, since CDM is desirable on scales of clusters and above. However, this statement should be revised in light of recent results showing that a fundamental force that reproduces the MDAR is challenged by local Milky Way dynamics and rotation curve data between 5-18 kpc. In this study, we test this claim on the example of Superfluid Dark Matter. We find that a standard CDM model is preferred over a static superfluid profile assuming a steady-state Galactic disk and discuss the robustness of this conclusion to disequilibrium effects. This preference is due to the fact that the superfluid model over-predicts vertical accelerations, even while reproducing galactic rotation curves. Our results establish an important criterion that any dark matter model must satisfy within the Milky Way.

A Verification and Validation Study on a Loosely Two-Way Coupled Hydroelastic Model of Wedge Water Entry

_ The interaction between the structural response and hydrodynamic loading (hydroelasticity) must be considered for design and operation purposes of high-speed planing craft made of composites that are prone to frequent water impact (slamming). A computational approach was proposed to study the hydroelastic slamming of a flexible wedge. The computational approach is a loose two-way coupling between a Wagner-based hydrodynamic solution and a linear finite element plate model. Verification and validation (V&V) was performed on this coupled model. It was found that the model overpredicts rigid-body/spray root kinematics by <15% and hydrodynamic loading/ structural response by <26%. Introduction One of the primary constraints on the operational envelope of high-speed craft is slamming (water impact). Slamming occurs between the hull body and the water surface when a portion/whole of the craft exits the water and then reenters at high-enough velocity (Lloyd 1989; Faltinsen 2005). The frequent water impacts, which work like “water hammers,” along with their induced acceleration pose great jeopardy on hull structures as well as crew and instrument on-board (Yamamoto et al. 1985; Ensign et al. 2000; Hirdaris et al. 2014). With the growing use of lightweight materials, the interaction between the structural deformation and the hydrodynamic loading (hydroelasticity) becomes more prevalent. The current design criteria of high-speed craft are based on empirical procedures with no regard to hydroelasticity due to the lack of understanding of this complex phenomenon (DNV 2013; ABS 2016). Therefore, a better comprehension of hydroelastic slamming is the first step to designing more high-performance craft (Fu et al. 2014; Judge et al. 2020).

Influence of a mass transfer stability criterion on double white dwarf populations

Context. Mass transfer stability is a key issue in studies of binary evolution. Critical mass ratios for dynamically stable mass transfer have been analyzed on the basis of an adiabatic mass loss model, finding that the donor stars on the giant branches tend to be more stable than that based on the composite polytropic stellar model. Double white dwarfs (DWDs) are of great importance in many fields and their properties would be significantly affected under the new mass transfer stability criterion. Aims. We seek to investigate the influence of mass transfer stability on the formation and properties of DWD populations and discuss the implications in supernova Type Ia (SN Ia) and gravitational wave (GW) sources. Methods. We performed a series of binary population synthesis, adopting the critical mass ratios from the adiabatic mass loss model (i.e., Ge’s model) and that of the composite polytropic model, respectively. In each simulation, 5 × 106 binaries were included and evolved from zero-age main sequence to the end of their evolution and the DWDs were gradually obtained. Results. For Ge’s model, most of the DWDs are produced from the stable non-conservative Roche lobe (RL) overflow, along with a common-envelope (CE) ejection channel (RL+CE channel), regardless of the CE ejection efficiency, αCE. Conversely, the results of the polytropic model strongly depend on the adopted value of αCE. We find DWDs produced from the RL+CE channel have comparable WD masses and the mass ratio distribution peaks at around 1. Based on the magnitude-limited sample of DWDs, the space densities for the detectable DWDs and those with extremely low-mass WD (ELM WD) companions in Ge’s model is: 1347 kpc−3 and 473 kpc−3, respectively, which is close to what has been shown in observations. On the other hand, the polytropic model overpredicts space density of DWDs by a factor of about 2−3. We also find that the results of DWD merger rate distribution per Galaxy in Ge’s model reproduce the observations better than that of the polytropic model, and the merger rate of DWDs with ELM WD companions in the Galaxy is about 1.8 × 10−3 yr−1 in Ge’s model. This result is comparable to the observation estimation of 2 × 10−3 yr−1. The findings from Ge’s model predict a Galactic SN Ia rate of ∼6 × 10−3 yr−1 from DWDs, supporting observations of (5.4 ± 1.2)×10−3 yr−1. For the fiducial model of αCE = 1, the number of detectable GW sources in the polytropic model is larger than that in Ge’s model by about 35%. Conclusions. We confirm that mass transfer stability plays an important role in the formation and properties of DWD populations as well as in the progenitors of SNe Ia and detectable GW sources. The results of Ge’s model support the observational DWD merger rate distribution per Galaxy and the space density of DWDs in the Galaxy.

Applying machine learning to construct braking emission model for real-world road driving.

Brake emissions from vehicles are increasing as the number of vehicles increases. However, current research on brake emissions, particularly the intensity and characteristics of emissions under real road conditions, is significantly inadequate compared to exhaust emissions. To this end, a dataset of 600 (200 unique real-world braking events simulated using three types of brake pads) real-world braking events (called brake pad segments) was constructed and a mapping function between the average brake emission intensity of PM2.5 from the segments and the segment features was established by five algorithms (multiple linear regression (MLR) and four machine learning algorithms). Based on the five algorithms, the importance of the different features of the fragments was discussed and brake energy intensity (BEI) and metal content (MC) of the brake pad emissions were identified as the most significant factors affecting brake emissions and used as the final modeling features. Among the five algorithms, categorical boosting (CatBoost) had the best prediction performance, with a mean R2 and RMSE of 0.83 and 0.039 respectively for the tenfold cross-validation. In addition, the CatBoost-based model was further compared with the MOVES model to demonstrate its applicability. The CatBoost-based model has better prediction performance than the MOVES model. The MOVES model overpredicts brake fragment emissions for urban roads and underpredicts brake fragment emissions for motorways. Furthermore, the CatBoost-based model was interpreted and visualized by an individual conditional expectation (ICE) plot to break the machine learning "black box", with BEI and MC showing nonlinear monotonic increasing relationships with braking emissions. ICE plot also provides viable technical solutions for controlling brake emissions in the future. Both avoiding aggressive braking driving behavior (e.g., the application of smart transportation technologies) and using brake pads with less metal content (e.g., using ceramic brake pads) can effectively reduce brake emissions. The construction of a machine learning-based brake emission model and the white-boxing of its model provide excellent insights for the future detailed assessment and control of brake emissions.

Effects of coupling of mass transport and blood viscosity models for microchannel flows

In this study, various aspects of a continuum transport model for blood, which is treated as a suspension of red blood cells, are thoroughly investigated. Earlier, treating blood as a suspension of rigid spherical particles in plasma was reasonably successful in mimicking flow dynamics in microchannels of size 40 μ m , comparable to 4 μ m , the dimension of a red blood cell (RBC). However, it is still not entirely clear whether such success owes to the coupling of mass and momentum transport, or to a sophisticated viscosity model for blood. To understand this in detail, we obtain semi-analytical solutions to the continuum model, in the presence and absence of the coupling of mass transport of RBCs, that connects the hematocrit concentration to the local viscosity, for fully developed blood flow in circular and rectangular microchannels. In this study, the transport of RBCs in flowing fluid is modeled by the diffusive flux model (DFM). Thus, overall, we have a hierarchy of continuum models using various viscosity models for blood, with progressive inclusion of physical complexities. Our results clearly highlight the importance of such a coupled approach, without which the results are quantitatively, and sometimes even qualitatively, different. In addition, our study serves as a thorough comparison of different viscosity models for blood, especially the steady state and transient models, recently proposed by Apostolidis and Beris (2014) and Apostolidis et al. (2015), respectively. We observe that, though the transient model reduces exactly to the description by the steady model over a range of low to moderate shear rates, the transient model shows some differences, especially for the RBC concentration in the low shear rate region. In the semi-analytical framework, the Darcy friction factor and cell free layer thickness have also been computed for circular cross-sections and a range of aspect ratios of a rectangular microchannel. Our results also indicate that the cell free layer attains a constant thickness when the aspect ratio of a rectangular microchannel reaches a critical maximum value for a specified average hematocrit. • Rectangular microchannel flow is investigated by coupling with the transport of RBC. • Even a finely tuned viscosity model without RBC transport is shown to be inadequate. • Frictional characteristics are derived in the form of a semi-analytical solution. • The model is found to predict the cell free layer thickness quite well. • The steady viscosity model overpredicts WSS when compared to the transient model.

Evaluation of SO<sub>2</sub>, SO<sub>4</sub><sup>2−</sup> and an updated SO<sub>2</sub> dry deposition parameterization in the United Kingdom Earth System Model

Abstract. In this study we evaluate simulated surface SO2 and sulfate (SO42-) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground-based measurement networks in the USA and Europe for the period 1987–2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO42- in both Europe and the USA over the period 1987–2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3 while under-predicting surface SO42- concentrations by 25 %–35 %. In the cleaner western USA, the model over-predicts both surface SO2 and SO42- concentrations by factors of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO42- concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model's global performance, finding that UKESM1 over-predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA, and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model's SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO42-. In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the model's over-prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations, the simulated surface SO42- concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase.

Modeling of a continuous physical vapor deposition process: Mass transfer limitations by evaporation rate and sonic choking

• Novel evaporation boundary condition is proposed to consider Hertz-Knudsen limitation. • A comprehensive numerical model for continuous PVD processes in OpenFoam is presented. • Non-isentropic losses reduce discharge; this effect decreases with melt temperature. • The isentropic overestimate is 110%–150%, that of the new CFD model only 30%. • Numerical model predictions are relatively insensitive to unknown process parameters. In recent years, Physical Vapor Deposition has been advanced to a continuous process which makes it amenable for in-line, high-quality and energy-efficient galvanization. To achieve the high and uniform mass flow required for in-line production, a Vapor Distribution Box is used, in which the zinc is evaporated. The zinc fills the Vapor Distribution Box at a relatively high pressure and leaves into the coating chamber via nozzles. A reliable modeling approach that can be used in the design and optimization of Vapor Distribution Boxes is as yet not available in the literature. The present paper analyses which phenomena play a major role and therefore have to be included in a simulation model of continuous Physical Vapor Deposition processes, and identifies process parameters which have a significant impact on deposition rate and uniformity. To this end, a model for the flow and heat transfer is developed based on the numerical solution of the compressible Navier–Stokes-Fourier equations in combination with the Launder and Sharma low-Reynolds k- ∊ turbulence model, using the open-source CFD-library OpenFOAM. To account for the vapor mass flow to be limited by both evaporation and sonic choking, a novel inlet boundary condition is proposed based on the Hertz-Knudsen condition. Results from the CFD model are compared to those of analytical models based on isentropic flow, the influence of various modeling parameters is evaluated against experiments, and sensitivity of the process to various process parameters studied. The proposed numerical model predicts mass flow rates with a much better accuracy than analytical models previously proposed in the literature. The latter overpredict the mass flow rate by a factor of 2.1–2.5, whereas the proposed numerical model overpredicts only by a factor of 1.3. Next to the novel Hertz-Knudsen boundary condition, the inclusion of viscous effects is found to be crucial to achieve this improvement, since viscous effects – especially in the boundary layer inside the nozzles – severely reduce the mass flow. The numerical model is shown to be only weakly sensitive to uncertainties in the evaporation coefficients and metal vapor viscosity. For the device studied, the mass flow discharge efficiency was found to be relatively low ( ≈ 40 % ). To increase this efficiency, viscous losses in the nozzle boundary layers have to be reduced, for instance by employing shorter or a bigger radius nozzles (possibly impairing nozzle-to-nozzle uniformity) or by employing a higher melt temperature and vapor pressure.

Pricing commodity futures and determining risk premia in a three factor model with stochastic volatility: the case of Brent crude oil

In this paper we introduce a three factor model to price commodity futures contracts. This model allows both the spot price volatility and convenience yield to be stochastic, nevertheless futures prices can be obtained conveniently in closed form. Further, we use Brent crude oil futures prices to calibrate the model using the extended Kalman filter. In comparison to the benchmark model for commodity futures pricing, the Schwartz two-factor model, our three factor model shows a superior fit for contracts that have longer maturities. We further assess risk premia in Brent crude oil through the two models and observe that the Schwartz two-factor model over-predicts risk premia in comparison to the new model.

Trends in electronic cigarette use and conventional smoking: quantifying a possible 'diversion' effect among US adolescents.

Background and aimsThe impact of electronic cigarettes (ECs) on nicotine use is hotly debated: some fear that ECs are a ‘catalyst’ to conventional smoking, while others argue that they divert adolescents from the more harmful product. This study used simulation modeling to evaluate the plausibility of catalyst and diversion hypotheses against real‐world data.DesignA simulation model represented life‐time exclusive EC use, exclusive conventional smoking and dual use as separate subpopulations. The ‘catalyst’ effect was modeled as EC use increasing dual use initiation (i.e. EC users also start smoking). The ‘diversion’ effect was modeled as EC use decreasing exclusive cigarette initiation. The model was calibrated using data from the US National Youth Tobacco Survey (NYTS). The plausibility of each scenario was evaluated by comparing simulated trends with NYTS data. This is the first study, to our knowledge, to estimate the magnitude of a diversion effect through simulation.SettingUnited States.Participants and measurementsAdolescents aged 12–17 years in NYTS, a cross‐sectional study from 2000 to 2019 (n = 12 500 to 31 000 per wave). Exclusive cigarette use, exclusive EC use and dual use of both products were defined using cumulative life‐time criteria (100+ cigarettes smoked and/or > 100 days vaped).FindingsA null model (no catalyst or diversion) over‐predicts NYTS smoking by up to 87%. Under the conservative assumption that the catalyst effect accounts for all dual use, an exponential decay constant of 19.6% EC users/year initiating smoking is required; however, this further over‐predicts actual smoking by up to 109%. A diversion effect with an exponential decay constant of 55.4%/year or 65.4%/year, with the maximum possible opposing catalyst effect also active, is required optimally to match NYTS smoking trends (root mean square error = 286 632 versus 391 396 in the null model).ConclusionsA simulation model shows that a substantial diversion effect is needed to explain observed nicotine use trends among US adolescents, and it must be larger than any possible opposing catalyst effect, if present.

Overprediction of species distribution models in conservation planning: A still neglected issue with strong effects

Species distribution models (SDM) are increasingly used in conservation planning to identify priority areas for the establishment of protected areas. Nevertheless, the quality of SDM varies widely and may compromise the effectiveness of protected areas. Here we reviewed whether SDM overprediction is considered in spatial conservation prioritization exercises and evaluated how model overprediction influences the effectiveness and the spatial arrangement of priority areas. To do so, we carried out a systematic review to analyze how researchers have handled SDM overprediction when identifying priority areas for conservation. To show how spatial conservation prioritization outcomes are affected by SDM overprediction, we used SDM of native palm at three geographic scales (Neotropics, Amazon ecoregion, and Ecuadorian Amazon). We found that only 10% of the evaluated manuscripts accounted for model overprediction. Our spatial conservation prioritization based on SDM with overprediction conferred high priority rank values in a region where species do not occur, underestimated the efficiency of selected priority areas, and over or underestimated the efficiency of current protected areas. Such effects were lower at smaller geographic extents. Our findings highlight the importance of improving future spatial conservation prioritization studies through the correction of SDM overprediction, resulting in the detection of more adequate areas for species conservation, especially at broader extents.

Effect of vent area, vent location and number of vents on vented hydrogen deflagrations in a 27 m3 chamber

Experiments in a 27 m3 chamber are performed to investigate the effect of vent parameters on the pressure development during homogenous or non-homogenous vented deflagrations, and the performance of Molkov’ models for predicting maximum overpressures is also evaluated. The results show that maximum overpressures exhibit monotonic behaviour as hydrogen concentration increases from 16% to 20.2%. Larger vent area leads to a smaller maximum overpressure, but the rate of maximum pressure decrease with the increase of the vent area is lower as the vent area increases. The vent location and number of vents have a less effect on overpressures during homogenous deflagrations, but the effect becomes more pronounced for non-homogenous deflagrations. Furthermore, two pressure peaks caused by the external explosion appear in the case of two vents. Molkov’ best-fit model over-predicts maximum overpressures for vent area of 1.76 m2, and under-predicts maximum overpressures for vent area of 3.14 m2 located on the roof, but the use of conservative model can give conservative predictions for all tests.

Exploring low temperature oxidation of 1-butene in jet-stirred reactors

1-butene is an important intermediate in combustion of various hydrocarbon fuels and oxygenated biofuels (e.g., butanol). Understanding its oxidation chemistry can help improve ignition and combustion process in advanced engines and provide better emission control. This work addresses a discrepancy between experiments and simulations in 1-butene oxidation at low temperatures, wherein simulations with AramcoMech 3.0 model show greater fuel reactivity than experiments. To further explore 1-butene low temperature reaction pathways from 550 to 910 K, experiments were conducted in three jet-stirred reactors: two coupled to time-of-flight molecular beam mass spectrometers with synchrotron vacuum ultraviolet radiation as the photoionization source, and one coupled to gas chromatography mass spectrometer. Isomeric structure identification, comprehensive species datasets, and reactor cross examinations are provided by the combination of three experiments. The identified isomer-resolved species provide evidence of various 1-butene low temperature reaction pathways. For example, the identification of propanal confirms the Waddington reaction pathway. The kinetic model over-predicts fuel reactivity in the low temperature regime (550–700 K). Updating the rate coefficients of key reactions in the Waddington pathways, e.g., forward and reverse isomerization of hydroxyl-butyl-peroxide to butoxyl-peroxide and Waddington decomposition of butoxyl-peroxide reduces the discrepancies. The role of rate constant updates in each step of the Waddington pathway is evaluated and discussed to provide directions for future model development.

An assessment of the spatial distribution of shallow landslides induced by Hurricane María in Puerto Rico

ABSTRACT Rainfall associated with Hurricane María triggered more than 70,000 shallow landslides over a 6,400 km2 area of Puerto Rico for a mean density of 11.2 slope failures per km2. When compared to other single storm landslide inventories with similarly sized search areas in tropical and sub-tropical regions, HMA’s mean landslide density is eleven times greater than the average. This study relied on frequency and zero-inflated negative binomial regression analyses to explain the spatial variability of landslide densities induced by HMA in relation to soil, geologic terrane, land cover, total storm precipitation, slope, and elevation. Results show that landslide densities tended to be exponentially higher for those areas with the highest rainfall totals and greater on igneous intrusive and other non-limestone sedimentary geologic terranes. Additionally, maximum densities occurred on relatively moderate slopes of 35–50% and not on the steepest hillslopes. Although a resulting regression model over-predicts landslides at lower elevations and underestimates high landslide density areas, it provides an acceptable fit for the landslide densities at a 0.25 km2 scale. The results presented here can guide future landslide susceptibility analyses to adequately contextualize the heterogeneity of landslide activity caused by extreme tropical cyclones in Puerto Rico and elsewhere.