Extrapolation Model Research Articles

Covid-19 mortality: the Proportionality Hypothesis

AbstractWe introduce and provide evidence to support the Proportionality Hypothesis which states that Covid-19 infection fatality rates are approximately proportional to all-cause death rates by age and subgroup (e.g., socio-economic class). We also show that vaccination played a very significant role in preventing people infected with Covid-19 from needing to be hospitalised, since it reduced the average severity of an infection. Death rates involving Covid-19 were very significantly lower for people in the fully vaccinated group compared to the unvaccinated group. During the pandemic, death rates from other causes were in some cases reduced (e.g., flu and pneumonia), in some cases unchanged (e.g., lung cancer) and in some cases elevated (e.g., heart disease). We discuss the implications of our findings both for potential adjustments to extrapolative mortality models which allow for future pandemics in a way that is consistent with the Proportionality Hypothesis and for insurance companies in terms of both modelling extreme scenarios and the design of mortality catastrophe bonds.

Radar Echo Extrapolation Based on Translator Coding and Decoding Conditional Generation Adversarial Network

In response to the shortcomings of current spatiotemporal prediction models, which frequently encounter difficulties in temporal feature extraction and the forecasting of medium to high echo intensity regions over extended sequences, this study presents a novel model for radar echo extrapolation that combines a translator encoder-decoder architecture with a spatiotemporal dual-discriminator conditional generative adversarial network (STD-TranslatorNet). Initially, an image reconstruction network is established as the generator, employing a combination of a temporal attention unit (TAU) and an encoder–decoder framework. Within this architecture, both intra-frame static attention and inter-frame dynamic attention mechanisms are utilized to derive attention weights across image channels, thereby effectively capturing the temporal evolution of time series images. This approach enhances the network’s capacity to comprehend local spatial features alongside global temporal dynamics. The encoder–decoder configuration further bolsters the network’s proficiency in feature extraction through image reconstruction. Subsequently, the spatiotemporal dual discriminator is crafted to encapsulate both temporal correlations and spatial attributes within the generated image sequences. This design serves to effectively steer the generator’s output, thereby augmenting the realism of the produced images. Lastly, a composite multi-loss function is proposed to enhance the network’s capability to model intricate spatiotemporal evolving radar echo data, facilitating a more comprehensive assessment of the quality of the generated images, which in turn fortifies the network’s robustness. Experimental findings derived from the standard radar echo dataset (SRAD) reveal that the proposed radar echo extrapolation technique exhibits superior performance, with average critical success index (CSI) and probability of detection (POD) metrics per frame increasing by 6.9% and 7.6%, respectively, in comparison to prior methodologies.

Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm

A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selection methods were applied: genetic algorithm combined with Ordinary Least Squares (GA-OLS), Partial Least Squares (GA-PLS), and Support Vector Machines (GA-SVM), each identifying ten pertinent AlvaDesc descriptors. The models were constructed using the Dragonfly Algorithm combined with the Support Vector Regressor (DA-SVR), with the GA-SVM-based model emerging as the top performer. Rigorous statistical metrics validate its superior predictive accuracy (R2 = 0.98142, Q2LOO = 0.98142, RMSE = 1.12836, AARD = 0.78746). Furthermore, an external test set of ten compounds was employed for model validation and extrapolation, along with assessing the applicability domain, further underscoring the model’s reliability. The selected descriptors (X0Av, VE1sign_B(e), ATSC1e, MATS6v, P_VSA_ppp_A, TDB01u, E1s, R2m+, N-067, SssO) collectively elucidate the key structural factors influencing surface tension in the studied drugs. The model provides excellent predictions and can be used to determine the surface tension of new psychoanaleptic drugs. Its outcomes will guide the design of novel medications with targeted surface tension properties.

Water density–pressure–temperature modeling for drilling fluids in real-time HPHT applications

Accurate equations for extrapolating the density of water to varying pressures and temperatures are important for the petroleum industry. Water is a key component in drilling fluids, and extrapolation models of water are essential for calculating the density of the fluid as a whole for different depths in the well. This is further of paramount importance to be able to keep the pressure in the well within safety limits. This paper shows and explains how density–pressure–temperature (DPT) models referred to in the field of Petroleum are not well-suited for high-pressure, high-temperature (HPHT) wells which are becoming more and more common. It also discusses DPT- and pressure–volume–temperature (PVT) models from other disciplines and demonstrates that models with the accuracy needed for HPHT wells, are excessively complex and have unnecessary long execution times. For many challenging wells, hydraulic models run today in parallel with the operation in real-time and may constitute one of several components in a digital twin. In such applications it is important that each component runs as efficiently as possible, such that the entire system is able to deliver its results in time. In this work, new polynomial DPT models are constructed, that fill this gap in water DPT modeling. I.e., they are designed for being used for calculating the water density in real-time, and cover HPHT wells with pressure up to 200MPa and temperature up to 260°C. It is demonstrated that it is possible to construct polynomial models that are 1−2 decades faster than the industry standard used for comparison, while also being more accurate over the pressure and temperature range relevant to HPHT wells. If the accuracy can be slightly relaxed, a new model that is 2−3 orders of magnitude faster than the industry standard is proposed.

DiffREE: feature-conditioned diffusion model for radar echo extrapolation

DiffREE: feature-conditioned diffusion model for radar echo extrapolation

Assessing the Costs of Intravenous Push Waste in Intraoperative Areas Through Observation: A Multi-site Study.

The costs associated with proper disposal, management, and regulatory compliance of controlled substances in healthcare systems are substantial. In the context of the current opioid crisis, and given the high abuse potential of controlled substances, it is imperative that waste is minimized and waste procedures are followed to ensure safe disposal of controlled substances. This study aims to quantify the costs associated with fentanyl, hydromorphone, morphine, midazolam, and ketamine waste in intraoperative areas through a multi-site observational analysis. The study used an observational design across various hospital procedural and post-procedural units in the Southwest Florida region of the United States. Automated and non-automated workflows for wasting controlled substances were compared. As with a previous study conducted by Hertig et al., waste was evaluated as (1) the quantity (mg/μg) of medication disposed defined as 'pharmaceutical waste' or 'product waste' (PW); and (2) workforce time associated with the waste disposal process defined as 'workforce time waste' (WTW). Secondary measures include workforce costs associated with the waste disposal process. The product waste analysis was conducted between October and December 2023. The workforce time waste analysis was examined over a 10-day period in January and February 2024. A yearly extrapolation model was applied to cost data. The findings revealed substantial costs linked to both PW and WTW, emphasizing the financial burden of controlled substance waste. Study data validated previous literature describing the extent of fentanyl, hydromorphone, and morphine waste while documenting significant amounts of midazolam and ketamine waste. The combined annual waste cost for the two study hospitals was estimated at US$56,557, with workforce time accounting for 36%-50% of this total cost. This study provides vital insights into the financial and operational impact of medication waste in procedural and post-procedural areas, supporting ongoing efforts to minimize waste, ensuring the safe and effective use of controlled substances. Future research should explore the impact of medication waste across diverse healthcare settings and the cost implications associated with pharmacy professionals in the waste compliance process.

Detailed multivariate comparisons of environments with mobility oriented parity

Multivariate model projections onto conditions that differ from those under which the models were calibrated can result in uncertainty owing to response extrapolation. Therefore, interpretations of models transferred to conditions not analogous to those of model calibration must be done carefully to avoid misleading conclusions. A good practice when producing model transfers is to assess the degree of dissimilarity between calibration and transfer conditions. The mobility oriented parity (MOP) metric is a tool commonly used to quantify such dissimilarities. Our study elucidated the details of MOP, and expanded the interpretability of results when transfer conditions are not analogous to those of model calibration. We implemented a more detailed characterization of non-analogous conditions previously only identified as outside calibration ranges. In addition, we quantified the sensitivity of MOP to the density and position of reference conditions in environmental space, and explored the effect of sample size on MOP results. We show that detailed characterizations of non-analogous conditions can help to determine which variables are different between the two areas and how. This information can be used to understand causes of environmental dissimilarity and to identify variables that could be excluded from ecological niche modeling owing to extrapolation risks associated with them. Our results also show that dissimilarity values calculated in comparisons offer good indicators of how reliable interpretations can be in areas with conditions not analogous to those of reference. The tools developed and implemented in this study provide a useful, quantitatively efficient means of characterizing extrapolation uncertainty in model transfer studies, a best practice when using multivariate ecological niche models to make predictions about species’ distributions. An extended version of the mobility oriented parity metric helps identify variables for which risks of model extrapolations can be expected. Distinct MOP metrics used to characterize dissimilarity show comparable general patterns in transfer areas, despite differences in values. As MOP results are influenced by background sampling size, the definition of this sample should be aimed to appropriately characterize calibration areas.

Multi-population mortality modeling with economic, environmental and lifestyle variables

AbstractThe premise of multipopulation mortality models is that pooling multiple populations can help identify more stable trends and diminish statistical noise. However, many existing models fail to contextualize mortality trends, treating them as isolated phenomena. This article introduces a comprehensive multipopulation mortality model that incorporates a broad spectrum of economic, environmental, and lifestyle factors to predict mortality trends. The factors are obtained with principal components analysis, extending current models which employ external variables beyond GDP. The model is applied to 33 countries present in the Human Mortality Database, divided into 9 clusters. Expanding the scope of covariates improves model fit for 29 countries out of 33 compared to GDP alone, and consistently outperforms the Li-Lee model. Furthermore, forecasting accuracy surpasses that of the Li-Lee model across various jump-off years and matches or exceeds models limited to GDP as a covariate. This study advances the field by demonstrating that a multipopulation approach, enriched with a wide array of covariates, significantly refines mortality forecasts, challenging the reliance on extrapolative or GDP-only models. It offers actuarial practitioners and policymakers a more nuanced tool for scenario planning, emphasizing the interconnectedness of mortality rates with broader socio-economic and environmental factors.

In Vitro Hepatic Clearance Evaluations of Per- and Polyfluoroalkyl Substances (PFAS) across Multiple Structural Categories.

Toxicokinetic (TK) assays and in vitro-in vivo extrapolation (IVIVE) models are New Approach Methods (NAMs) used to translate in vitro points of departure to exposure estimates required to reach equivalent blood concentrations. Per- and polyfluoroalkyl substances (PFAS) are a large chemical class with wide-ranging industrial applications for which only limited toxicity data are available for human health evaluation. To address the lack of TK data, a pooled primary human hepatocyte suspension model was used with targeted liquid chromatography-mass spectrometry to investigate substrate depletion for 54 PFAS. A median value of 4.52 μL/(min x million cells) was observed across those that showed significant clearance, with 35 displaying no substrate depletion. Bayesian modeling propagated uncertainty around clearance values for use in IVIVE models. Structural evaluations showed the fluorotelomer carboxylic acids were the only PFAS carboxylates showing appreciable clearance, and per- and polyfluorosulfonamides were more readily metabolized than other PFAS sulfonates. Biotransformation product prediction, using the chemical transformation simulator, suggested hydrolysis of PFAS sulfonamides to more stable sulfonic acids, which is an important consideration for exposure modeling. This effort greatly expands the PFAS in vitro toxicokinetic dataset, enabling refined TK modeling, in silico tool development, and NAM-based human health evaluations across this important set of emerging contaminants.

Hindcasting and updating Landsat-based forest structure mapping across years to support forest management and planning

Forest vegetation mapping that integrates forest inventory data with multispectral remote sensing data provides valuable geospatial products for public land management agencies, but resource managers may require rapid updating of maps as new imagery becomes available (updating) or retrospective mapping for times prior to forest inventory plot measurement (hindcasting). While forest attribute mapping using Landsat multispectral imagery is common, the accuracy of applying models outside of reference epoch to support long-term forest monitoring is not normally quantified. We examine whether a Landsat-based mapping approach can support robust, temporally consistent multivariate mapping of forest structure and composition data in support of forest management planning and landscape analysis. Specifically, we ask: how accurate forest attribute mapping was when hindcasting or updating outside of a period of time when forest inventory plot data were available (reference epoch)? In the western Cascade Mountains of Oregon and California, USA, we used the gradient nearest neighbor approach to annually impute USDA Forest Inventory and Analysis (FIA) plot data (2001–2016) to all 30-m forested pixels based on temporally smoothed Landsat multispectral imagery (1986–2021), including basal area, canopy cover, quadratic mean diameter of dominant trees, stand height, and the density of large diameter trees. We made extrapolations from models fit to a 10-year reference epoch to both earlier periods (2001–2006 hindcast) and to later period (2011–2016 update) and quantified prediction accuracies relative to models based on the full data (2001–2016). To evaluate the influence of spatial scale on hindcasting and updating, we compared full and extrapolation model predictions at pixel-level (0.09 ha) and hexagon-level (780 ha).At the plot-level, we found no strong differences between the full and extrapolation model predictions for R2 and mean error nor among predicted vs. observed regression coefficients. At the pixel-level, average differences due to hindcasting and updating were near zero, though differences varied up to 20 % across pixels. At the hexagon-level, the range in map differences was small (+/- 5 %), but hindcasting resulted in lesser forest attribute predictions. We observed greater variability in pixel-level and hexagon-level prediction differences when hindcasting or updating was temporally further away from the reference period. Using 2001 hindcast and 2016 updated maps as a case study, we found that with hindcasting and updating map differences were spatially aggregated across the study region. Our results support Landsat-based hindcasting and updating of forest attribute mapping beyond the time period covered by forest plot data. Our results suggest aggregating data to coarse spatial resolutions may minimize differences due to hindcasting and updating. Further research is needed to identify the key drivers for prediction differences to improve the accuracy of both hindcasting and updating as a basis for forest monitoring.

A novel approach to triazole fungicides risk characterization: Bridging human biomonitoring and computational toxicology

Brazil stands as the world's leading coffee producer, where the extensive use of pesticides is economically critical yet poses health and environmental risks due to their non-selective mechanisms of action. Specifically, triazole fungicides are widely used in agriculture to manage fungal diseases and are known to disrupt mammalian CYP450 and liver microsomal enzymes. This research establishes a framework for risk characterization of human exposure to triazole fungicides by internal-dose biomonitoring, biochemical marker measurements, and integration of high-throughput screening (HTS) data via computational toxicology workflows from the Integrated Chemical Environment (ICE). Volunteers from the southern region of Minas Gerais, Brazil, were divided into two groups: farmworkers and spouses occupationally and environmentally exposed to pesticides from rural areas (n = 140) and individuals from the urban area to serve as a comparison group (n = 50). Three triazole fungicides, cyproconazole, epoxiconazole, and triadimenol, were detected in the urine samples of both men and women in the rural group. Androstenedione and testosterone hormones were significantly reduced in the farmworker group (Mann-Whitney test, p < 0.0001). The data show a significant inverse association of testosterone with cholesterol, LDL, VLDL, triglycerides, and glucose and a direct association with HDL (Spearman's correlation, p < 0.05). In the ICE workflow, active in vitro HTS assays were identified for the three measured triazoles and three other active ingredients from the pesticide formulations. The curated HTS data confirm bioactivities predominantly related to steroid hormone metabolism, cellular stress processes, and CYP450 enzymes impacted by fungicide exposure at occupationally and environmentally relevant concentrations based on the in vitro to in vivo extrapolation models. These results characterize the potentially significant human health risk, particularly from the high frequency and intensity of exposure to epoxiconazole. This study showcases the critical role of biomonitoring and utility of computational tools in evaluating pesticide exposure and minimizing the risk.

Predicting the solid solution structure preference of multi-component alloys

High-entropy alloys (HEAs) provide limitless opportunities to enhance material performance while predicting the thermodynamic properties and the structures rapidly within HEAs remain challenging. Herein, a new method (M2FEn) for rapidly predicting the formation enthalpies is provided. In contrast to Miedema's semi-empirical methodology, which solely calculates the mixing enthalpy, the M2FEn is capable of calculating the formation enthalpies of various solid solution structures, and by ordering these enthalpies, one can determine the preferred solid-solution structure for the single-phase alloy. The predicted structures using M2FEn are in line with first-principles calculations. The effectiveness of different extrapolation models for formation enthalpy has been assessed. The M2FEn with the Ouyang extrapolation model and the regular solution model have achieved a prediction accuracy of 95.8% and 95.4%, respectively for single-phase HEAs that were experimentally prepared. 28 common HEA elements were selected as the chemical space, and the formation enthalpies of all binary, ternary, quaternary, quinary and senary alloys, a total of 499149 various alloys were calculated by M2FEn. It is found that the BCC structure ratio increases with the increase of the principal element of the alloy. The M2FEn should potentially accelerate the development of novel, high-performance HEAs, enabling more efficient and cost-effective materials design processes.

Magnetic Eruption from a Three-ribbon Flare

We present observations and analysis of an eruptive M1.5 flare (SOL2014-08-01T18:13) in NOAA active region (AR) 12127, characterized by three flare ribbons, a confined filament between ribbons, and rotating sunspot motions as observed by the Solar Dynamics Observatory. The potential field extrapolation model shows a magnetic topology involving two intersecting quasi-separatrix layers (QSLs) forming a hyperbolic flux tube (HFT), which constitutes the fishbone structure for the three-ribbon flare. Two of the three ribbons show separation from each other, and the third ribbon is rather stationary at the QSL footpoints. The nonlinear force-free field extrapolation model implies the presence of a magnetic flux rope (MFR) structure between the two separating ribbons, which was unclear in the observation. This suggests that the standard reconnection scenario for eruptive flares applies to the two ribbons, and the QSL reconnection for the third ribbon. We find rotational flows around the sunspot, which may have caused the eruption by weakening the downward magnetic tension of the MFR. The confined filament is located in the region of relatively strong strapping field. The HFT topology and the accumulation of reconnected magnetic flux in the HFT may play a role in holding it from eruption. This eruption scenario differs from the one typically known for circular ribbon flares, which is mainly driven by a successful inside-out eruption of filaments. Our results demonstrate the diversity of solar magnetic eruption paths that arises from the complexity of the magnetic configuration.

A CFD Model for Spatial Extrapolation of Wind Field over Complex Terrain—Wi.Sp.Ex

A CFD Model for Spatial Extrapolation of Wind Field over Complex Terrain—Wi.Sp.Ex

Reliability Assessment Method Based on Small Sample Accelerated Life Test Data

Aiming at the problems of small sample size, few test failure data and low reliability evaluation efficiency in accelerated life test of high-reliability and long-life products, an improved virtual augmentation Bootstrap-Bayes reliability evaluation method based on small sample accelerated life test data was proposed. Firstly, the test data under various stress conditions are augmented by virtual fusion. Secondly, the empirical distribution function and bootstrap sampling method are improved, and the kernel density estimation method is used to fit the density distribution of test data as prior information. Then, the parameter estimates of the test data are obtained by Gibbs sampling combined with Bayes formula. Finally, the reliability index under normal stress level is obtained by accelerating model extrapolation. The feasibility of the proposed method is verified by the accelerated life test data of a type of Ship communication equipment.

Human next-generation risk assessment of trichothecene toxicity

Trichothecenes are naturally occurring chemicals, produced by fungi, that can be found in contaminated crops. Trichothecenes have the potential to indirectly damage DNA and exacerbate genotoxic effects of genotoxicants. However, genotoxicity data for most trichothecenes are limited and data gaps remain. Here we use the γH2AX/pH3 assay to evaluate DNA damage in vitro of 13 trichothecenes. Three human cell lines (SH-SY5Y, ACHN, and HepG2) were exposed to each trichothecene (0.001–100 μM) to assess toxicity as models for the brain, kidney, and liver, respectively. Concentration-dependent induction of DNA damage, illustrated by γH2AX induction, was observed for all trichothecenes. In vitro-in vivo extrapolation (IVIVE) modeling was employed to support in vivo equivalent potency ranking and screen for risk potential. Diacetoxyscirpenol, T-2, and HT-2 had the highest genotoxic potency, notably in SH-SY5Y cells. Administered equivalent doses (AEDs) derived from IVIVE were compared against exposure data from French total diet studies to assess risk potential. AEDs derived for T-2 and HT-2 from the SH-SY5Y model were within 100-fold of exposure levels for infants aged one year or less. Overall, the potential for trichothecenes to damage DNA and higher exposures in infants highlights the need to investigate the cumulative effects across the broader trichothecene family.

Study on the modified three-temperature model for spatial extrapolation of evapotranspiration based on individual urban vegetation evapotranspiration data

Study on the modified three-temperature model for spatial extrapolation of evapotranspiration based on individual urban vegetation evapotranspiration data

Solar Wind Interactions with Comet C/2021 A1 Using STEREO HI and a Data-assimilative Solar Wind Model

Cometary tails display dynamic behavior attributed to interactions with solar wind structures. Consequently, comet-tail observations can serve as in situ solar wind monitors. During 2021 December, Comet Leonard (C/2021 A1) was observed by the STEREO-A heliospheric imager. The comet tail exhibited various signatures of interactions with the solar wind including bending, kink formation, and finally complete disconnection. In this study, we compare the timing of these events with solar wind structures predicted by the Heliospheric Upwind eXtrapolation model with a time-dependency (or HUXt) solar wind model using new solar wind data assimilation (DA) techniques. This serves both to provide the most accurate solar wind context to interpret the cometary processes, but also as a test of the DA and an example of how comet observations can be used in model validation studies. Coronal mass ejections, stream interaction regions (SIRs), and heliospheric current sheet (HCS) crossings were all considered as potential causes of the tail disconnection. The results suggest the tail disconnection at Comet Leonard was the result of it crossing the HCS and into an SIR. The timing of these features agree better with the DA model results than the non-DA model, showing the value of this approach. Case studies such as this expand our understanding of comet–solar wind interactions, and in demonstrating the utility of DA for solar wind modeling. We note that this could lead to comets acting as additional in situ measures for solar wind conditions for regions where no in situ spacecraft are available, potentially improving solar wind DA in the future.

Evaluating different CFD surrogate modelling approaches for fast and accurate indoor environment simulation

Indoor Environment Quality (IEQ) holds significant importance in building design and operation, and the simulation of indoor environments playing a crucial role in enhancing IEQ. Although Computational Fluid Dynamics (CFD) has been widely employed for simulating building environments, it is computationally demanding, particularly for large spaces. To tackle this challenge, we conducted a systematic evaluation of three surrogate models for accelerating CFD: Proper Orthogonal Decomposition (POD), Artificial Neural Networks (ANN), and a combined POD-ANN approach. Our evaluation criteria focused on assessing the model accuracy, the model size, computational time and extrapolation ability. A validated CFD case model and a real campus building are employed for model evaluation. The findings demonstrate that the top five modes can reconstruct the original data matrix accurately, and the POD-ANN significantly reduces model complexity and computation time by reducing the number of parameters in the neural network, the POD-ANN parameters is only 0.14 % of the ANN, and computation time is reduced by 63 %. In addition, the combination with ANN helps increase the extrapolation ability of POD significantly. In conclusion, this research proves that the POD-ANN can enhance the efficiency of CFD calculations with the advantages of both ANN and POD. By applying the POD-ANN to predict indoor temperature, we achieve faster predictions without compromising model accuracy, and an excellent extrapolation ability is achieved. This approach also reduces model complexity, highlighting its practical value for indoor environment prediction, particularly for large and complicated spaces.