Variability Model Research Articles

Variation in the cross-sectional geometry of hot-rolled I-sections: Measurements, statistics, probabilistic modelling

This paper presents the evaluation of an extensive measurement campaign on the cross-sectional dimensions of hot-rolled I-sections. As a preliminary step, the reliability of the measuring process was quantified as well as checked to what extent the cross-sectional dimensions remain constant along a beam. In the main part of the investigation, a total of 561 statistically independent I-sections with a wide range of different sizes were surveyed and statistically analysed. Overall, the observed variation of the dimensions revealed a good agreement with the characterisation in the new Annex E of EN 1993-1-1. However, a more detailed evaluation showed that there is a certain dependence of these statistical parameters on the nominal size of the dimensions. Furthermore, several other aspects were checked, such as the correlation between the individual dimensions, the compliance with manufacturing tolerances according to EN 10034 and the uniformity of the web and flange thickness at various positions within the cross-section. Subsequently, the measured profiles were also analysed based on the resulting cross-sectional properties. In addition to a statistical evaluation in respect to the corresponding nominal values, the influence of the common shape idealisation to a perfectly double-symmetrical cross-section with constant flange thicknesses was addressed. Finally, a comparison of several options for the probabilistic modelling of the geometric variability was carried out, based on the lower 5%-quantiles of the resulting cross-sectional properties.

Modeling and numerical simulation of variability in traffic queues at traffic signals using a queueing model approach

Modeling and numerical simulation of variability in traffic queues at traffic signals using a queueing model approach

Surface texture evolution of weathering steel in corrosive marine atmospheres: Geospatial variability and explainable machine learning models

The surface texture of 144 weathering steel samples were analyzed under marine atmospheres to assess the effects of the exposure conditions and structural factors. Comprehensive methods were used to characterize the corrosion evolution and pitting performance. Geospatial variability was introduced as a new method for quantifying non-uniform and irregular surface substrates. The results showed significant variations in corrosion severity under identical environmental conditions. Closely spaced pits formed clustered patterns with an increased range, whereas isolated sparse pits demonstrated the converse. Sill effectively captured serrated surface variations. Explainable machine learning models were developed to regenerate and predict the steel surface texture.

How to use the impossible map – Considerations for a rigorous exploration of Digital Twins of the Earth

The term “Digital Twins of the Earth” has rocketed into scientific use and policymaker discourse by promising a virtual replica of our planet. While the potential of a digital representation of reality is captivating for environmental monitoring, decision-making, and scientific inquiry, the term lacks a clear and shared definition and may be misleading. It conceals that all digital representations are models and, as such, will always be detached from reality. Detailed simulation models are excellent digital laboratories that allow us to interrogate our theories about the world in ways otherwise not possible, given the limited scales at which we can run real-world experiments, yet a perfect representation of reality is impossible as it would be exactly as complex. As we embark on the journey of building such detailed models, one question we must ask is, "How can we ensure that they can be explored with scientific rigor?" Here, we discuss possible ways to utilize a model's internal variability to understand its dominant controls to increase our understanding of both the models we build and the world that they represent.

Image-based random fields in numerical modeling of heterogeneous materials

Modeling of spatial variability using random fields is nowadays standard in computational modeling of heterogeneous materials. The only difficulty remains in determining the input parameters of the random field in order to represent the modeled material morphology as accurately as possible. The most frequent method of constructing a random field relies on various covariance kernels that primarily use as input the values of the correlation lengths, which are typically estimated ad-hoc. However, another possibility is to extract input parameters from the modeled material morphology. Moreover, this approach enables to calculate the covariance kernel itself. The performance of the image-based procedure is compared with standard methods of random field construction on the response of the two-phase elastic material.

Distinguishing West Florida gag (Mycteroperca microlepis) nurseries with otolith stable isotopes

Numerous reef fishes utilize estuarine nurseries following spawning in marine habitats. Nursery origin affects their life cycle space use, and otolith chemistry can be an effective tool for tracking movement over this lifetime scale. Gag Mycteroperca microlepis occupy West Florida (WF), USA, estuaries as juveniles and have a spatially complex life cycle with ontogenetic habitat shifts and offshore spawning. We tested whether age-0 gag otolith stable isotope values distinguished WF nurseries and assessed isotopic variability at multiple temporal and spatial scales. Northern and southern juveniles had distinct otolith carbon (δ13C) and oxygen (δ18O) stable isotope values despite evidence of interannual variability, and both year-specific and combined year models had high (>80%) classification accuracies. We investigated long-term stability of otolith δ13C and δ18O by comparing age-0 gag collected during 2009-2012 and 2020-2021. The δ18O values of southern age-0 otoliths decreased over time, thus decreasing inter-regional differences and the classification accuracy of the combined year model (79%). Concomitantly, age-0 otoliths became depleted in 13C across regions, which may result from increased limestone dissolution (i.e. dekarstification) in the Floridan aquifer. We identified greater decadal variability in region-specific stable isotope values than has been documented in previous studies, indicating that a cohort-specific approach will likely be required to estimate gag lifetime movement.

Influence of Model Resolution on Wind Energy Simulations over Tibetan Plateau Using CMIP6 HighResMIP

The assessment of wind energy resources is critical for the transition from fossil fuel to renewable energy sources. Using the outputs from high-resolution global climate models (GCMs), such as the High Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), has become one of the most important tools in wind energy research. This study evaluated the reliability of the 22 GCMs available in the HighResMIP-PRIMAVERA project by simulating the wind energy climatology and variability over the Tibetan Plateau (TP) with reference to observations and investigated the differences in performance of the GCMs between high-resolution (HR) and low-resolution (LR) simulations. The results show that most models performed relatively well in simulating the probability distribution of the observed wind speed over the TP, but nearly half of the models generally underestimated the wind speed, whereas the others tended to overestimated the wind speed. Compared with the wind speed, the GCMs showed larger biases in reproducing the wind power density (WPD) and other wind energy resources, whereas the biases in multi-model ensembles were relatively smaller than those in most individual models. With respect to interannual variability, both the HR and LR models failed to capture interannual variations in WPD over the TP. Furthermore, more than half of the HR GCMs had a reduced bias relative to the corresponding LR GCMs, indicating the good performance of most HR models in simulating wind energy resources over the TP in terms of spatial pattern and temporal variability. However, the overall performance of HR GCMs varied among models, which suggests that solely improving the horizontal resolution is not sufficient to completely solve the uncertainties and deficiencies in the simulation of wind energy over complex terrain.

Morphine and hydromorphone pharmacokinetics in human volunteers: population-based modelling of interindividual and opioid-related variability

BackgroundMorphine and hydromorphone have differing onsets, magnitudes, and durations of effects and side-effects. Differences between opioids in their interindividual variabilities in pharmacokinetics and pharmacodynamics might influence rational drug selection. Crossover drug studies can provide more informative interindividual variability data than parallel group studies. Using data from a crossover study of i.v. morphine and hydromorphone in healthy volunteers, we tested the hypothesis that morphine and hydromorphone differ in their interindividual pharmacokinetic variability. MethodsArterial opioid and metabolite concentrations from a randomised crossover study in 51 volunteers receiving a 2-h infusion of hydromorphone (0.05 or 0.1 mg kg−1 i.v.) or morphine (total 0.1 or 0.2 mg kg−1 i.v.) 1–2 weeks apart were evaluated with a three-compartmental model for parent opioid and incorporating glucuronides using population modelling (NONMEM). The primary outcome was interindividual variability in pharmacokinetics, based on the coefficient of variation (%CV) of individual model parameters, calculated as √[exp(ω2)–1]×100 where ω2 is the interindividual variability. ResultsData were analysed per drug and in a combined morphine–hydromorphone model. Both analyses indicate that interindividual variabilities for hydromorphone and morphine were comparable with %CV ranging from 9% to 31% for structural model parameters (combined analysis). Similarly, additive and relative residual errors had comparable variabilities, 20–40% and 72–87%, respectively, for morphine and hydromorphone (combined analysis). ConclusionsMorphine and hydromorphone did not differ in a statistically significant or clinically meaningful manner in their interindividual pharmacokinetic variability. Interindividual pharmacokinetic variability does not appear a meaningful consideration in the choice between these two opioids.

Looking for Typical Traits in Monovarietal VOOs According to Their Phenolic Composition.

Due to its high sensitivity to numerous variability sources, it is hard to define the typicity of a monovarietal virgin olive oil (VOO) according to its phenolic profile. In this study, we aimed to identify the features of phenolic composition that are persistent and minimally affected by variability sources, making them potential varietal markers. We separately analyzed three databases of monovarietal VOO phenolic compositions, determined by liquid chromatography, from three different cultivars. The first database was produced from the original data of the Bosana cultivar. The other two were obtained through a systematic analysis of scientific literature on Coratina and Frantoio cultivars. Several statistical tools, including coefficient of variability, correlations, and linear regression models, were used to find recurring proportions or ratios unaffected by variability sources suitable to define typical varietal traits. Some proportions between molecules, mostly within the same phenolic class, remain constant. Strong correlations between (i) flavonoids were observed in Bosana and Frantoio VOOs (R2 = 0.87 and 0.77, respectively), (ii) oleacein-oleocanthal (Bosana, R2 = 0.81) (iii) oleuropein aglycon-ligstroside aglycon (Frantoio, R2 = 0.88), and (iv) lignans (Coratina, R2 = 0.84). These traits could be useful tools for defining the typicity of monovarietal VOOs.

Detecting and sizing the Earth with PLATO: A feasibility study based on solar data

The PLAnetary Transits and Oscillations of stars (PLATO) mission will observe the same area of the sky continuously for at least two years in an effort to detect transit signals of an Earth-like planet orbiting a solar-like star. We aim to study how short-term solar-like variability caused by oscillations and granulation would affect PLATO's ability to detect and size Earth if PLATO were to observe the Solar System itself. We also compare different approaches to mitigate noise caused by short-term solar-like variability and perform realistic transit fitting of transit signals in PLATO-like light curves. We injected Earth-like transit signals onto real solar data taken by the Helioseismic and Magnetic Imager (HMI) instrument on board the Solar Dynamics Observatory (SDO). We isolated short-term stellar variability in the HMI observations by removing any variability with characteristic timescales longer than five hours using a smooth Savitzky-Golay filter. We then added a noise model for a variety of different stellar magnitudes computed by assuming an observation by all 24 normal cameras. We first compared four different commonly used treatments of correlated noise in the time domain by employing them in a transit fitting scheme. We then tried to recover pairs of transit signals using an algorithm similar to the transit least squares algorithm. Finally, we performed transit fits using realistic priors on planetary and stellar parameters and assessed how accurately the pair of two injected transits was recovered. We find that short-term solar-like variability affects the correct retrieval of Earth-like transit signals in PLATO data. Variability models accounting for variations with typical timescales at the order of one hour are sufficient to mitigate these effects. We find that when the limb-darkening coefficients of the host star are properly constrained, the impact parameter does not negatively affect the detectability of a transit signal or the retrieved transit parameters, except for high values ($b > 0.8$). For bright targets (8.5 - 10.5 mag), the transit signal of an Earth analogue can reliably be detected in PLATO data. For faint targets a detection is still likely, though the results of transit search algorithms have to be verified by transit-fitting algorithms to avoid false positive detections being flagged. For bright targets (V-mag leq 9.5), the radius of an Earth-like planet orbiting a solar-like star can be correctly determined at a precision of 3<!PCT!> or less, assuming that at least two transit events are observed and the characteristics of the host star are well understood.

Population variability in X-chromosome inactivation across 10 mammalian species

One of the two X-chromosomes in female mammals is epigenetically silenced in embryonic stem cells by X-chromosome inactivation. This creates a mosaic of cells expressing either the maternal or the paternal X allele. The X-chromosome inactivation ratio, the proportion of inactivated parental alleles, varies widely among individuals, representing the largest instance of epigenetic variability within mammalian populations. While various contributing factors to X-chromosome inactivation variability are recognized, namely stochastic and/or genetic effects, their relative contributions are poorly understood. This is due in part to limited cross-species analysis, making it difficult to distinguish between generalizable or species-specific mechanisms for X-chromosome inactivation ratio variability. To address this gap, we measure X-chromosome inactivation ratios in ten mammalian species (9531 individual samples), ranging from rodents to primates, and compare the strength of stochastic models or genetic factors for explaining X-chromosome inactivation variability. Our results demonstrate the embryonic stochasticity of X-chromosome inactivation is a general explanatory model for population X-chromosome inactivation variability in mammals, while genetic factors play a minor role.

Towards the construction of regional marine radiocarbon calibration curves: an unsupervised machine learning approach

Abstract. Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. “reservoir age” offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age and delta-R estimates therefore have limited applicability. While forward modelling of past R-age variability has been proposed as a means of resolving this problem, this requires accurate a priori knowledge of past global radiocarbon budget closure (i.e. production, and cycling), which we currently lack. In this context, the construction of empirical regional marine calibration curves could provide a way forward. However, the spatial reach of such calibrations and their robustness subject to (uncertain) temporal changes in climate and ocean circulation would need to be tested. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R ages), regardless of the causes of R-age variability. We apply multiple algorithms (k-means, k-medoids, and hierarchical clustering) to outputs from two different numerical models spanning a range of climate states, forcings, and timescales of adjustment. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely stem from constraints imposed by large-scale ocean and atmospheric physics. At the coarsest scale, regions of coherent R-age variability correspond to the major ocean basins. By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations, such as increased high-latitude R ages, or the propagation of R-age anomalies from regions of deep mixing in the Southern Ocean to upwelling sites in the eastern equatorial Pacific, which cohere with known modern oceanographic processes. We show that the medoids (i.e. the most representative locations) for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. This remains true when cluster assignments obtained from one model simulation are applied to simulated R-age time series from another. Further, model-based clusters are found to be broadly consistent with existing reservoir age reconstructions that span the last ∼30 kyr. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which empirical marine radiocarbon calibration curves may eventually be generated.

Models of Variability in Probabilistic Causal Judgments

AbstractMost theories of causal reasoning aim to explain the central tendency of causal judgments. However, experimental studies show that causal judgments are quite variable. In this article, we report the results of an experiment using a novel repeated measures design that demonstrate the existence of meaningful (i.e., not noise-related) within-participant variability. Next, we introduce and assess multiple computational cognitive models that serve as potential accounts of the sources of variability and fit those models to the new empirical data. We find that the Bayesian Mutation Sampler has the best fit to the data and is able to account for a number of unusual features of the response distributions (e.g., bi-modality), supporting the view that the stochastic sampling mechanism it posits reflects the cognitive processes via which people draw causal inferences. Additionally, our findings suggest that incorporating ‘non-reasoning’ processes, such as rounding and guessing, can improve the ability of models of causal reasoning to account for the observed response distributions. Overall, the study highlights the potential of computational modeling of full response distributions to shed light on the underlying mechanisms of human causal reasoning and identifies promising directions for future research.

Comparison of Physically Based and Empirical Modeling of Nighttime Spatial Temperature Variability during a Heatwave in and around a City

Abstract The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model. Significance Statement As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed.

Daily sibling interactions among latino children in middle childhood: An examination of familism values and sibling roles

Siblings are children’s most frequent daily companions in middle childhood and these relationships are theorized to be dynamic in nature but are rarely studied in a way that captures their day-to-day variation. Guided by cultural-ecological and relationship perspectives, this study examined the role of older siblings’ familism values and caring roles as predictors of average levels of and day-to-day variation in siblings’ positive and negative exchanges across seven days and tested moderation by child sex. Participants were older ( M = 10.6 years; SD = 0.39; 46.6% female) and younger siblings ( M = 8.2 years; SD = 1.07; 47.2% female) in 307 Latino sibling dyads recruited from 11 elementary schools in the southwestern U.S. Data were collected via home visits with parents, and in afterschool sessions and seven nightly phone calls with siblings. Analyses were conducted using a random variability model in a multilevel structural equation modeling approach. Findings revealed consistent associations between older siblings’ familism values and sibling roles and both average levels of and intraindividual variation in older siblings’ positivity and negativity. For younger siblings, some findings were moderated by child sex with more consistent links to younger sisters’ positivity and negativity than younger brothers’. Discussion highlights how older siblings’ cultural values and roles shape both overall levels of and day-to-day fluctuations in siblings’ exchanges and offers future directions of research.

Middle Stone Age technological organisation from MIS 5 at Mertenhof Rockshelter, South Africa

Lithic technological organisation within the southern Africa Early Middle Stone Age (∼315,000–80,000 years ago) has seen relatively little investigation owing to the subtly of technological change, frequent use of locally derived raw materials, and the archaeological spatio-temporal discontinuity. This has resulted in relatively limited use of explanatory models for technological variability, including mobility, provisioning, tool production, and core reduction strategies. This paper uses 2952 artefacts to test the lithic technological organisation across Marine Isotope Stage 5 units of Mertenhof Rockshelter. Here we argue that the scales and concepts currently used to approach Early Middle Stone Age technology requires reconsideration. The Mertenhof sequence exhibits high proportions of non-local raw materials with their transport reflective of tactical adjustments within relatively stable mobility, provisioning, and reduction strategies. We demonstrate that Early Middle Stone Age populations maintained a diverse array of tactical solutions across these strategic domains, offering a durable and flexible strategy that would be adapted to changing contexts.

Haemolysis overestimates plasma oxidative stress biomarkers in free-ranging roe deer

Quantifying oxidative stress has garnered extensive interest in evolutionary ecology and physiology since proposed as a mediator of life histories. However, while the theoretical framework of oxidative stress ecology is well-supported by laboratory-based studies, results obtained in wild populations on oxidative damage and antioxidant biomarkers have shown inconsistent trends. We propose that red blood cell lysis could be a source of bias affecting measurements of oxidative stress biomarkers, distorting the conclusions drawn from them. Using an experimental approach consisting of enriching plasma from roe deer with lysed red blood cells, we show that the values of commonly used oxidative stress biomarkers linearly increase with the degree of haemolysis – assayed by haemoglobin concentration. This result concerns oxidized proteins (carbonyls) and lipids (TBARS), as well as enzymatic (superoxide dismutase) and non-enzymatic (trolox assay, OXY assay) antioxidant markers. Based on 707 roe deer blood samples collected in the field, we next show that the occurrence of haemolysis in plasma samples is negatively related to age. Finally, we illustrate that considering the variance explained by age-related haemolysis improves explanatory models for inter-individual variability in plasma oxidative stress biomarkers, without substantially altering the estimates of the parameters studied here. Our results raise the question of the veracity of the conclusions if the degree of haemolysis in plasma is not considered in animal models such as roe deer, for which the occurrence and severity of haemolysis vary according to individual characteristics. We recommend measuring and controlling for the degree of haemolysis be considered in future studies that investigate the causes and consequences of oxidative stress in ecophysiological studies.

Evaluating solar ramp rate correlations by simple radiation and wind measurements

The stability of photovoltaic (PV) energy output is critical to power supply and is a crucial factor for the reliability of power supply systems but is significantly influenced by the unpredictable shading caused by clouds. Assessing the variability of solar radiation across a spatially dispersed PV fleet can be challenging, as it is usually lower than that taken by routine measurements at a singular, localized point. This study refines the previously developed spatial radiation variability model by depicting the attenuation mode of solar irradiance correlation across spatial distances and considering the differences in the along-wind and cross-wind directions under various weather and solar radiation conditions. These modes are further connected to the variables that properly depict the weather conditions and are straightforward to measure. The data being adopted in developing and validating the model are collected from 17 pyranometers over a 0.56 km2 area for about half a year with a 1-s resolution. The model's performance, with a percentage root-mean-square error that surpasses isotropic models by 2.42 %–10.6 % for solar ramp intervals ranging from 5 to 240 s, underscores its accuracy and potential for optimizing the integration of PV fleets into power grids.

Modeling of Chloride Spatial Variability in a Reinforced Concrete Wharf from Onsite Measurements

Modeling of Chloride Spatial Variability in a Reinforced Concrete Wharf from Onsite Measurements

Kidney function assessment for eligibility in clinical cancer trials – Data from the European Organisation for Research and Treatment of Cancer

PurposeThere is no consensus on how to estimate kidney function for the assessment of eligibility in clinical cancer trials. Patients and methodsWe recalculated the creatinine clearance (CrCl)/glomerular filtration rate (GFR) at baseline in a total of 1768 patients enrolled in twelve clinical trials using the Cockcroft-Gault (CG), Modification of Diet in Renal Disease (MDRD), 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI 2021) and European Kidney Function Consortium (EKFC) formulas. Patients were classified as having renal impairment (RI; CrCl/GFR <60 mL/min) or no renal impairment (NRI; CrCl/GFR ≥60 mL/min) with each of the four formulas, respectively. Furthermore, we analyzed the number of adverse events (AE) per month under study treatment using measures of central tendency, variability and regression models. ResultsUsing CG, EKFC, MDRD and CKD-EPI 2021, 152 (8 %), 140 (8 %), 110 (6 %), and 61 (4 %) patients had RI respectively. Indeed, 47 (3 %) patients had RI using all 4 formulas, while 158 (9 %) had RI by at least one but not all four methods. CG showed the broadest variability and inconsistencies with other methods. All calculation methods performed similarly for excluding patients at risk of severe AE. EKFC demonstrated superior predictive ability for excluding patients at risk of renal and urinary tract AE. ConclusionThis post hoc analysis highlights the importance of choosing accurate and representative methods for kidney function estimation in clinical cancer trials. CG should be replaced by newer methods. While CKD-EPI 2021 may maximize trial accrual, EKFC should be considered for treatment affecting kidney function.