Uncertainty In Parameter Values Research Articles

A Mathematical Model of Interstitial Fluid Flow and Retinal Tissue Deformation in Macular Edema.

This study aims to develop a mathematical model to elucidate fluid circulation in the retina, focusing on the movement of interstitial fluid (comprising water and albumin) to understand the mechanisms underlying exudative macular edema (EME). The model integrates physiological factors such as retinal pigment epithelium (RPE) pumping, osmotic pressure gradients, and tissue deformation. It accounts for spatial variability in hydraulic conductivity (HC) across the retina and incorporates the structural role of Müller cells (MCs) in maintaining retinal stability. The model predicts that tissue deformation is maximal at the center of the fovea despite fluid exudation from blood capillaries occurring elsewhere, aligning with clinical observations. Additionally, the model suggests that spatial variability in HC across the thickness of the retina plays a protective role against fluid accumulation in the fovea. Despite inherent simplifications and uncertainties in parameter values, this study represents a step toward understanding the pathophysiology of EME. The findings provide insights into the mechanisms underlying fluid dynamics in the retina and fluid accumulation in the foveal region, showing that the specific conformation of Müller cells is likely to play a key role.

Modeling and Prediction of The Mechanical Properties of Feedstock by Cooling-Slope Casting Process using MOJaya Algorithm

Cooling-slope (CS) casting parameters optimization is very challenging due to the uncertainties of parameter values that are influenced by many factors. Therefore, a combination of estimation and optimization models that can solve multi-objective problems using limited data is proposed to reduce the real casting operational cost and time. In casting optimization, modeling and optimization of CS parameters have been considered to identify optimal CS parameters that would lead to better feedstock performance. Computational techniques have been suggested by previous researchers to solve the optimization in the manufacturing process. The study assessed the CS process to determine the feedstock's quality by evaluating its mechanical qualities, namely Tensile strength and Impact strength. Hence, computational methods namely the MOJaya algorithm are utilized to model and optimize the parameters of CS to address the CS problem and forecast the performance of the feedstock. This work conducted a thorough analysis of the impact of CS process parameters on the tensile strength and impact strength using an experimental design. The experiment was conducted using a three-level full factorial design (FFD) of the experiment (DOE), for the parameters of pouring temperature, slanting angle, and pouring distance. The modeling technique involved the development of multiple polynomial regression (MPR) as an objective function in the MOJaya Algorithm. The outcome of the optimization procedure revealed that the optimal solutions obtained from MOJaya were projected to surpass both the initial experimental data and the MOJaya algorithm in terms of the highest values of tensile strength and impact strength. The difference between the two sets of values is 10.92% and 15.76%, respectively. Subsequently, this technique may be employed by a caster to forecast the performance of the feedstock without using repeated experiments that are costly and time-consuming.

Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions

Aerosol effects on clouds and radiation are a large source of uncertainty in our understanding of human impacts on the climate system. Uncertainty in aerosol effects results from uncertainty in parameter values, known as parametric uncertainty, and from uncertainty from the model’s structure, known as structural uncertainty. While previous studies have assessed the impact of parametric uncertainty on modeled forcing, structural errors from the numerical representation of particle distributions and their dynamics have not been well quantified. Here we present a framework for quantifying error in aerosol size distributions and cloud condensation nuclei activity, which we apply to the widely used 4-mode version of the Modal Aerosol Module (MAM4). Box model predictions from the MAM4 are evaluated against the Particle Monte Carlo Model for Simulating Aerosol Interactions and Chemistry (PartMC-MOSAIC), a benchmark model that tracks the evolution of individual particles. We show that size distributions simulated by MAM4 diverge from those simulated by PartMC-MOSAIC after only a few hours of aging by condensation and coagulation in polluted conditions, which leads to large errors in modeled cloud condensation nuclei concentrations. We find that differences between MAM4 and PartMC-MOSAIC are largest under polluted conditions, where the size distribution evolves rapidly though aging. These findings indicate that structural error in modeled aerosol properties is a key factor contributing to uncertainty in aerosol forcing.

Model refinements with experimental validation in piezoelectric plate actuators

The axisymmetric vibration analysis of circular plates with piezoelectric layers and step changes in plate thickness is conducted. For the chosen configurations, it is necessary to solve a coupled system of differential equations for extensional and flexural vibration to obtain accurate modal results. The adopted computational approach is based upon the classical transfer matrix method. Instead of employing analytical solutions in the form of e.g. Bessel functions, matrix exponentials of the system matrix are directly computed in the transfer matrices for finding highly accurate solutions. The developed approach has enabled the complete quantitative modal analysis in piezoelectric actuators. The procedure is firstly verified against available analytical results for natural frequencies and modes shapes in circular homogeneous plates, and by using finite element simulations for plates with asymmetric step changes in thickness. Ultimately, the developed approach is validated against experimental results of relevance to applications in piezoelectric synthetic jet actuation. The obtained numerical results for modal parameters and frequency response functions are in excellent agreement with the available analytical results, and with previous and newly presented in the paper experimental data, when considering the inherent uncertainty in parameter values. Based on extensive experimental results, realistic damping is introduced in the model of piezoelectric plate actuators. Importantly, it is shown that solutions of high accuracy can be obtained by a more general coordinate system than that traditionally linked to the plate neutral plane(s).

Direct adaptive fractional-order non-singular terminal sliding mode control strategy using extreme learning machine for position control of 5-DOF upper-limb exoskeleton robot systems

This paper presents an innovative adaptive fractional-order (FO) terminal sliding mode (TSM) controller designed for a 5-degree-of-freedom (5-DOF) upper-limb exoskeleton robot. The primary focus is on addressing the challenges posed by nonlinearities and uncertainties in parameter values. The proposed approach integrates several key elements, including a novel fractional-order non-singular terminal sliding mode (FONSTSM) surface, an exponential reaching law, and the utilization of the extreme learning machine (ELM). This comprehensive strategy guarantees not only finite-time convergence but also robust stability, effectively alleviating the well-known chattering phenomenon. Furthermore, it successfully overcomes the singularity issues typically observed in conventional TSM controllers. The incorporation of the ELM with rectified linear unit (ReLU) activation function enhances robustness by facilitating the estimation of parameters related to the exponential control law. Numerical simulations provide compelling evidence of improved tracking, increased robustness against uncertainties, achievement of finite-time convergence, and notable reductions in control signal oscillations and singularity problems.

Quantifying earthworm soil ingestion from changes in vertical bulk density profiles

Soil mixing by earthworms can have a large impact on the fate of nutrients and pollutants and on the soil's ability to sequester carbon. Nevertheless, methods to quantify earthworm ingestion and egestion under field conditions are largely lacking. Soils of the Fennoscandian tundra offer a special possibility for such quantifications, as these soils commonly lack burrowing macrofauna and exhibit a well-defined O horizon with low bulk density on top of a mineral soil with higher density. Since ingestion-egestion mixes the two soil layers, the temporal changes in the bulk density profile of such soils may be useful for estimating field ingestion rates. In this study, we applied a model for earthworm burrowing through soil ingestion to observed changes in soil densities occurring in a mesocosm experiment carried out in the arctic during four summers with intact soil. The earthworms present in the mesocosms were Aporrectodea trapezoides, Aporrectodea tuberculata, Aporrectodea rosea, Lumbricus rubellus and Lumbricus Terrestris (fourth season only). We show that changes in soil density profiles can indeed be used to infer earthworm ingestion rates that are realistic in comparison to literature values. Although uncertainties in parameter values were sometimes large, the results from this study suggest that soil turnover rates and endogeic earthworm soil ingestion rates in tundra heath and meadow soils may be as high as those reported for temperate conditions. Such large ingestion rates can explain observed large morphological changes in arctic soils where dispersing earthworms have resulted in complete inmixing of the organic layer into the mineral soil. Our approach is applicable to soil profiles with marked vertical differences in bulk density such as the soils of the Fennoscandian tundra where earthworms are currently dispersing into new areas and to layered repacked soil samples that are incubated in the field.

Data-Driven Optimal Power Dispatch for Distributed Energy Resources in Radial Feeder using Multi-Stage Regression

This paper develops a multi-stage regression (MSR) based algorithm for dispatching distributed energy resources (DERs) in a power distribution system. Application of the standard regression algorithm to network power flow problems generally suffer from performance limitations due to collinearity in training data, (i.e. voltages, currents and nodal demands), and non-linearity inherent in power flows and DER dispatches. Here, regression collinearity is addressed with intelligent choice of the input data training set which also considerably reduces the requirements on the volume of training data. Next, a logistic regression based labeling scheme is developed and applied to classify the data into disjoint training sets which significantly improves the prediction accuracy. Simulations are provided to demonstrate the performance of the proposed algorithm in a radial distribution system voltage support application under a range of operating conditions and considering uncertainty in parameter values. The proposed approach and learnings can be extended to a range of network power flow problems and DER-based applications.

A systematic review of cost-utility analyses of screening methods in latent tuberculosis infection in high-risk populations

BackgroundThe World Health Organisation (WHO) recommends that testing and treatment for latent tuberculosis infection (LTBI) should be undertaken in high-risk groups using either interferon gamma release assays (IGRAs) or a tuberculin skin test (TST). As IGRAs are more expensive than TST, an assessment of the cost-effectiveness of IGRAs can guide decision makers on the most appropriate choice of test for different high-risk populations. This current review aimed to provide the most up to date evidence on the cost-effectiveness evidence on LTBI testing in high-risk groups—specifically evidence reporting the costs per QALY of different testing strategies.MethodsA comprehensive search of databases including MEDLINE, EMBASE and NHS-EED was undertaken from 2011 up to March 2021. Studies were screened and extracted by two independent reviewers. The study quality was assessed using the Bias in Economic Evaluation Checklist (ECOBIAS). A narrative synthesis of the included studies was undertaken.ResultsThirty-two studies reported in thirty-three documents were included in this review. Quality of included studies was generally high, although there was a weakness across all studies referencing sources correctly and/or justifying choices of parameter values chosen or assumptions where parameter values were not available. Inclusions of IGRAs in testing strategies was consistently found across studies to be cost-effective but this result was sensitive to underlying LTBI prevalence rates.ConclusionWhile some concerns remain about uncertainty in parameter values used across included studies, the evidence base since 2010 has grown with modelling approaches addressing the weakness pointed out in previous reviews but still reaching the same conclusion that IGRAs are likely to be cost-effective in high-income countries for high-risk populations. Evidence is also required on the cost-effectiveness of different strategies in low to middle income countries and countries with high TB burden.

Budget of nitrous acid (HONO) at an urban site in the fall season of Guangzhou, China

Abstract. High concentrations of nitrous acid (HONO) have been observed in the Pearl River Delta (PRD) region of China in recent years, contributing to an elevated atmospheric oxidation capacity due to the production of OH through HONO photolysis. We investigated the budget of HONO at an urban site in Guangzhou from 27 September to 9 November 2018 using data from a comprehensive atmospheric observation campaign. During this period, measured concentrations of HONO were 0.02 to 4.43 ppbv, with an average of 0.74 ± 0.70 ppbv. An emission ratio (HONO/NOx) of 0.9 ± 0.4 % was derived from 11 fresh plumes. The primary emission rate of HONO at night was calculated to be between 0.04 ± 0.02 and 0.30 ± 0.15 ppbv h−1 based on a high-resolution NOx emission inventory. Heterogeneous conversion of NO2 on the ground surface (0.27 ± 0.13 ppbv h−1), primary emissions from vehicle exhaust (between 0.04 ± 0.02 and 0.30 ± 0.15 ppbv h−1, with a middle value of 0.16 ± 0.07 ppbv h−1), and the homogeneous reaction of NO + OH (0.14 ± 0.30 ppbv h−1) were found to be the three largest sources of HONO at night. Heterogeneous NO2 conversion on aerosol surfaces (0.03 ± 0.02 ppbv h−1) and soil emission (0.019 ± 0.009 ppbv h−1) were two other minor sources. Correlation analysis shows that NH3 and the relative humidity (RH) may have participated in the heterogeneous transformation of NO2 to HONO at night. Dry deposition (0.41 ± 0.31 ppbv h−1) was the main removal process of HONO at night, followed by dilution (0.18 ± 0.16 ppbv h−1), while HONO loss at aerosol surfaces was much slower (0.008 ± 0.006 ppbv h−1). In the daytime, the average primary emission Pemis was 0.12 ± 0.02 ppbv h−1, and the homogeneous reaction POH+NO was 0.79 ± 0.61 ppbv h−1, larger than the unknown source PUnknown (0.65 ± 0.46 ppbv h−1). Similar to previous studies, PUnknown appeared to be related to the photo-enhanced conversion of NO2. Our results show that primary emissions and the reaction of NO + OH can significantly affect HONO at a site with intensive emissions during both the daytime and nighttime. Uncertainty in parameter values assumed in the calculation of HONO sources can have a strong impact on the relative importance of HONO sources at night, and could be reduced by improving knowledge of key parameters such as the NO2 uptake coefficient. The uncertainty in the estimated direct emission can be reduced by using emission data with higher resolution and quality. Our study highlights the importance of better constraining both conventional and novel HONO sources by reducing uncertainties in their key parameters for advancing our knowledge of this important source of atmospheric OH.

Safety Warning about Laparoscopic Power Morcellation in Hysterectomy: A Cost-Effectiveness Analysis of National Impact.

Background:Following a 2014 safety warning (that laparoscopic power morcellation may increase tumor dissemination if patients have occult uterine cancer), hysterectomy practice shifted from laparoscopic to abdominal approach. This avoided morcellating occult cancer, but increased perioperative complications. To inform the national impact of this practice change, we examined the cost-effectiveness of hysterectomy practice in the postwarning period, in comparison to counterfactual hysterectomy practice had there been no morcellation warning.Materials and Methods:We constructed a decision tree model to simulate relevant outcomes over the lifetime of patients in the national population undergoing hysterectomy for presumed benign indications. The model accounted for both hysterectomy- and occult cancer-related outcomes. Probability-, cost-, and utility weight-related input parameters were derived from analysis of the State Inpatient Databases, State Ambulatory Surgery and Services Databases, data from the New York Statewide Planning and Research Cooperative System and New York State Cancer Registry, and published literature.Results:With an estimated national sample of 353,567 adult women, base case analysis showed that changes in hysterectomy practice after the morcellation warning led to a net gain of 867.15 quality-adjusted life years (QALYs), but an increase of $19.54 million in costs (incremental cost-effectiveness ratio = $22,537/QALY). In probabilistic sensitivity analysis, the practice changes were cost-effective in 54.0% of the simulations when evaluated at a threshold of $50,000/QALY, which increased to 70.9% when evaluated at a threshold of $200,000/QALY.Conclusion:Hysterectomy practice changes induced by the morcellation warning are expected to be cost-effective, but uncertainty in parameter values may affect the cost-effectiveness results.

The photo-astrometric vertical tracer density of the Milky Way – II. Results from Gaia

ABSTRACT We use Gaia photometry and astrometry to estimate the vertical spatial structure of the Milky Way at the Solar radius, formally accounting for sample incompleteness (the selection function) and parallax measurement uncertainty. Our results show impressive precision demonstrating the power of the Gaia data. However, systematic errors dominate the parameter value uncertainties. We thoroughly test and quantify the impacts of all systematic uncertainties. The vertical tracer density is modelled as a sum of two exponential profiles for the thin and thick discs, together with a spherically symmetric power law for the stellar halo. We constrain the thin disc scale height as ${h_\mathrm{Tn}=260 \pm 3\, (\mathrm{stat}) \pm 26\, \mathrm{pc}\, (\mathrm{sys})}$ and thick disc ${h_\mathrm{Tk}=693 \pm 7 \, (\mathrm{stat}) \pm 121\, \mathrm{pc}\, (\mathrm{sys})}$. For the halo, we obtain a power-law profile with $n_\mathrm{H}=3.543\pm 0.023 \, (\mathrm{stat}) \pm 0.259\, (\mathrm{sys})$. We infer a local stellar mass density for non-compact object stars of ${\rho _\mathrm{local}^{*} = 3.66\pm 0.03\, (\mathrm{stat})\pm 0.52 \times 10^{-2}\, \mathrm{M}_\odot \, \mathrm{pc}^{-3}\, (\mathrm{sys})}$ and surface density of ${\Sigma _\mathrm{local}^{*} = 23.17\pm 0.08\, (\mathrm{stat})\pm 2.43\, \mathrm{M}_\odot \, \mathrm{pc}^{-2}\, (\mathrm{sys})}$. We find asymmetries above and below the disc with longer disc scale heights in the north but a flatter halo in the south at the ≲ 10 per cent level.

Improved Model for Predicting Total Dissolved Gas Generation With the Residence Time of the Water in the Stilling Phase

Quantitative predictions of total dissolved gas (TDG) super-saturation are essential for developing operation schemes for high dams. Most TDG generation prediction models have various shortcomings that affect the accuracy of TDG super-saturation estimation, such as oversimplification of influencing factors and uncertainty in parameter values. In this study, the TDG generation process was divided into three parts, gas-liquid mass transfer process in the stilling phase, dilution resulting from the water jet plunging into the stilling phase, and outflow of TDG–super-saturated water from the stilling phase, while considering the water body and bubbles in the stilling phase as a whole. The residence time of the water in the stilling phase (Tr) was introduced to estimate mass transfer time, along with dimensional analysis methods. The properties of TDG generation were evaluated experimentally under varying Tr values. Based on the theoretical analysis and experimental results, a basic water renewal model was proposed and was validated using experimental data. Furthermore, prediction results of this model were compared with those of a classical empirical model and mechanical model based on observed data from a field survey at Xiluodu Dam. The results show that the relative errors between the predicted and experimental measurements were all less than 5%, indicating that the developed prediction model has a good performance. Compared with the mechanism model, the developed model could reduce the standard error (SE), normalized mean error (NME), and error of maximum (REMAX) by 60, 96, and 15%, respectively. Meanwhile, the developed model could reduce the SE, NME, REMAX by 17.4, 36, and 23%, respectively, compared with the empirical model. Considering all the error indexes, it can be concluded that the prediction performance of the water renewal model is the best among the three models. The proposed model was also more generically versatile than the existing models. Prediction results of water regeneration model for TDG could aid the drafting of governing strategies to minimize the risk of super-saturated TDG.

Assessing the Viability of the Sarasota Bay Community of Bottlenose Dolphins

Population models, such as those used for Population Viability Analysis (PVA), are valuable for projecting trends, assessing threats, guiding environmental resource management, and planning species conservation measures. However, rarely are the needed data on all aspects of the life history available for cetacean species, because they are long-lived and difficult to study in their aquatic habitats. We present a detailed assessment of population dynamics for the long-term resident Sarasota Bay common bottlenose dolphin (Tursiops truncatus) community. Model parameters were estimated from 27 years of nearly complete monitoring, allowing calculation of age-specific and sex-specific mortality and reproductive rates, uncertainty in parameter values, fluctuation in demographic rates over time, and intrinsic uncertainty in the population trajectory resulting from stochastic processes. Using the Vortex PVA model, we projected mean population growth and quantified causes of variation and uncertainty in growth. The ability of the model to simulate the dynamics of the population was confirmed by comparing model projections to observed census trends from 1993 to 2020. When the simulation treated all losses as deaths and included observed immigration, the model projects a long-term mean annual population growth of 2.1%. Variance in annual growth across years of the simulation (SD= 3.1%) was due more to environmental variation and intrinsic demographic stochasticity than to uncertainty in estimates of mean demographic rates. Population growth was most sensitive to uncertainty and annual variation in reproduction of peak breeding age females and in calf and juvenile mortality, while adult survival varied little over time. We examined potential threats to the population, including increased anthropogenic mortality and impacts of red tides, and tested resilience to catastrophic events. Due to its life history characteristics, the population was projected to be demographically stable at smaller sizes than commonly assumed for Minimum Viable Population of mammals, but it is expected to recover only slowly from any catastrophic events, such as disease outbreaks and spills of oil or other toxins. The analyses indicate that well-studied populations of small cetaceans might typically experience slower growth rates (about 2%) than has been assumed in calculations of Potential Biological Removal used by management agencies to determine limits to incidental take of marine mammals. The loss of an additional one dolphin per year was found to cause significant harm to this population of about 150 to 175 animals. Beyond the significance for the specific population, demographic analyses of the Sarasota Bay dolphins provide a template for examining viability of other populations of small cetaceans.

A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions

AbstractUse of carefully designed computer supported parameterisation methods in voltammetric studies can provide highly robust and accurate methods for simultaneously quantifying the large number of parameters present in complex electrochemical reactions. In this study, a computer program has been developed to parameterise large amplitude AC voltammetric data using mathematical optimisation in combination with Bayesian inference algorithms for calculating posterior distributions of parameters and hence uncertainties in parameter values. The computer program has been applied to objective functions, relevant to total AC current, frequency domain data in the form of the power spectrum derived from Fourier transformation and multivariate based methods using the resolved harmonic data. The robustness of the objective functions have been confirmed and Bayesian inference methods have been validated using “noisy” synthetic and experimental data for the [Fe(CN)6]3−/4− reduction process in aqueous 3.0 M KCl electrolyte at a gold electrode. It was found that the harmonic based Bayesian inference methods outperformed other methods in parameterisation of the thermodynamics and electrode kinetics of the close to reversible [Fe(CN)6]3−/4− process due to their ability to compensate for non‐ideality in the modelling and the superior parameter sensitivities available in the higher harmonics. The computer supported and heuristic methods were compared. Their advantages and limitations are discussed.

Effects of Waning Maternal Immunity on Infection Dynamics in Seasonally Breeding Wildlife.

Increasing outbreaks of emerging infectious diseases originating from wildlife have intensified interests in understanding their dynamics in reservoir hosts. The effect of waning maternally derived antibodies on epidemics in a seasonally breeding wild mammal population is unclear. We examined how the population structure, influenced by seasonal breeding and maternally derived immunity, affects viral invasion and persistence using a hypothetical system based on Hendra virus infection in black flying foxes (Pteropus alecto). A deterministic Hendra virus epidemic model with uncertainty in parameter values was used to simulate transient epidemics following viral introduction into an infection-free population, including various timings within a year and differences in pre-existing seroprevalence. Additionally, we applied different modelling methods of waning maternal immunity to examine whether different models notably affected modelling outputs. The waning of maternally derived immunity temporally dispersed the supply of susceptible individuals in seasonally breeding populations, diminishing the effect of birth pulses generating the temporally synchronised supply of susceptible newborns. Thus, even in a population with seasonal births, a considerable level of probabilities of viral invasion and persistence could occur no matter when infectious individuals were introduced into the population. Viral invasion and persistence were substantially influenced by the modelling method of maternally derived immunity, emphasising the need to select an appropriate method and further investigate the waning pattern of maternally derived antibodies.

Reducing Parameter Value Uncertainty in Discrete Bayesian Network Learning: A Semantic Fuzzy Bayesian Approach

Bayesian network has gained increasing popularity among the data scientists and research communities, because of its inherent capability of capturing probabilistic information and reasoning with uncertain knowledge. However, the discrete Bayesian learning, with continuous and categorical variables, often shows poor performance because of parameter value uncertainty, arising due to strict boundary value of the discretized data and lack of knowledge on domain semantics. In this work, we propose semFBnet, a variant of Bayesian network with incorporated fuzziness and semantic knowledge, to reduce the uncertainty during parameter learning. The performance of semFBnet has been validated with prediction of daily meteorological conditions in two states of India, namely West Bengal and Delhi, for the years 2015 and 2016, respectively. The study of Dawid-Sebastiani score and the confidence interval analysis, in comparison with the state-of-the-art and benchmark prediction techniques, demonstrate the effectiveness of the proposed semFBnet in reducing parameter value uncertainty.

The Case for Campaign Analysis: A Method for Studying Military Operations

Abstract Military operations lie at the center of international relations theory and practice. Although security studies scholars have used campaign analysis to study military operations for decades, the method has not been formally defined or standardized, and there is little methodological guidance available for scholars interested in conducting or evaluating it. Campaign analysis is a method involving the use of a model and techniques for managing uncertainty to answer questions about military operations. The method comprises six steps: (1) question selection, (2) scenario development, (3) model construction, (4) value assignment, (5) sensitivity analysis, and (6) interpretation and presentation of results. The models that scholars develop to direct analysis are significant intellectual contributions in their own right, and can be adapted by other scholars and practitioners to guide additional analyses. Careful model construction can clarify, but does not obviate, the uncertainty of conflict. To manage uncertainty in parameter values, scholars can use the “input distribution approach” to propagate uncertainty in inputs through to a model's output. Replications and extensions of Wu Riqiang's 2020 analysis of Chinese nuclear survivability and Barry Posen's 1991 analysis of the North Atlantic Treaty Organization's prospects against the Warsaw Pact illustrate the six steps of campaign analysis, the value of transparent models and the input distribution approach, and the potential of campaign analysis to contribute to policy and theory.

Active galactic nucleus feedback in an elliptical galaxy with the most updated AGN physics: Parameter explorations

ABSTRACT In a previous work, we have proposed a sub-grid model of active galactic nucleus (AGN) feedback by taking into account the state-of-the-art AGN physics, and used that model to study the effect of AGN feedback on the evolution of an isolated elliptical galaxy by performing 2D high-resolution (i.e. the Bondi radius is well resolved) simulations. In that work, typical values of model parameters were adopted. In this work, we extend that study by exploring the effects of uncertainties of parameter values. Such a study is also useful for us to understand the respective roles of various components of the model. These parameters include the mass flux and velocity of AGN wind and radiative efficiency in both the hot and cold feedback modes, and the initial black hole (BH) mass. We find that the velocity of AGN wind in the hot mode is the most important quantity to control the typical accretion rate and luminosity of AGN, and the mass growth of the BH. The effect of the wind on star formation is less sensitive. Within the limited parameter range explored in this work, a stronger AGN wind suppresses star formation within ∼100 pc but enhances star formation beyond this radius, while the star formation integrated over the evolution time and the whole galaxy roughly remain unchanged. AGN radiation suppresses the BH accretion in a mild way, but dust is not considered here. Finally, a smaller initial BH mass results in a more violent evolution of the BH accretion rate. The corresponding AGN spends more time in the high-luminosity state and the percentage of BH mass growth is higher. Our results indicate the robustness of AGN feedback in keeping the galaxy quenched.

Bayesian calibration of firn densification models

Abstract. Firn densification modelling is key to understanding ice sheet mass balance, ice sheet surface elevation change, and the age difference between ice and the air in enclosed air bubbles. This has resulted in the development of many firn models, all relying to a certain degree on parameter calibration against observed data. We present a novel Bayesian calibration method for these parameters and apply it to three existing firn models. Using an extensive dataset of firn cores from Greenland and Antarctica, we reach optimal parameter estimates applicable to both ice sheets. We then use these to simulate firn density and evaluate against independent observations. Our simulations show a significant decrease (24 % and 56 %) in observation–model discrepancy for two models and a smaller increase (15 %) for the third. As opposed to current methods, the Bayesian framework allows for robust uncertainty analysis related to parameter values. Based on our results, we review some inherent model assumptions and demonstrate how firn model choice and uncertainties in parameter values cause spread in key model outputs.

Design and analysis of a computer experiment for the study of the distortion of an advanced gas-cooled reactor moderator brick

Engineering models, resolved using the finite element method, are used to model through-life changes in material properties and the resulting internal brick stresses in graphite moderator bricks within an advanced gas-cooled reactor (AGR). These models require inputs that describe the loading conditions of the moderator brick and coded relationships describing the behaviour of material properties as the brick ages. A highly multivariate spacio-temporal dataset of displacements, stresses and strains is returned as model output. In this work we describe a computer experiment conducted to study the variability in brick distortion resulting from parameter value uncertainty in the inputs to the model. We identify summary measures of brick shape and conduct global sensitivity analyses to identify the key uncertain parameters driving the variability in brick shape. Our results indicate that different measures of brick shape show sensitivities to different groups of model inputs and suggest that data obtained from core monitoring campaigns can be used to calibrate uncertain parameters in the model. Model calibration and the impact of calibration on predictions of internal brick stresses, which cannot be directly validated, are areas of current research.