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One key source of uncertainty for weather and climate models is structural uncertainty arising from the fact that these models must simplify or approximate complex physical, chemical, and biological processes that occur in the real world. However, structural uncertainty is rarely examined in the context of simulated effects of anthropogenic heat flux in cities. Using the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model, it is found that the sensitivity of urban canopy air temperature to anthropogenic heat flux can differ by an order of magnitude depending on how anthropogenic heat flux is released to the urban environment. Moreover, varying model structures through changing the treatment of roof-air interaction and the parameterization of convective heat transfer between the canopy air and the atmosphere can affect the sensitivity of urban canopy air temperature by a factor of 4. Urban surface temperature and 2-m air temperature are less sensitive to the methods of anthropogenic heat flux release and the examined model structural variants than urban canopy air temperature, but their sensitivities to anthropogenic heat flux can still vary by as much as a factor of 4 for surface temperature and 2 for 2-m air temperature. Our study recommends using temperature sensitivity instead of temperature response to understand how various physical processes (and their representations in numerical models) modulate the simulated effects of anthropogenic heat flux.

Inverse problems involving high-dimensional uncertain variables may encounter the challenge of the ‘curse of dimensionality’ during the solution process. In addition, the current uncertainty inverse algorithms may encounter insufficient prior information on the parameters to be inversely solved, leading to difficulty in determining the search interval for inverse problem modeling and optimization. In light of this, this study introduces an inverse solution algorithm that considers model uncertainty based on a high-dimensional model representation (HDMR) approach. The algorithm utilizes the HDMR method to construct models of system input parameters and uncertain model parameters. Subsequently, by enhancing the traditional CV-Voronoi sequential sampling method, the algorithm ensures that the sequential sampling points in the modeling process satisfy both model prediction accuracy and the distribution forms of uncertain variables. Finally, through a stepwise modeling process based on the HDMR, efficient modeling and solution are achieved in scenarios where prior information on the parameters to be determined inversely is insufficient. The effectiveness of the proposed method is validated through several numerical and engineering examples. The method presented in this study provides an effective tool for solving inverse problems with high-dimensional model uncertainty in the field of structural design. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) has been used to evaluate wastewater treatment technologies, processes, and scenarios. The outcomes of LCA are affected by various uncertainties, including those from input data and those from the LCA model set-up (i.e., structural uncertainty). The impacts of structural uncertainty have not received adequate attention in research. The objective of this study was to quantify how the subjective choices embedded in the LCA model set-up affect the LCA outcomes of three wastewater sludge treatment processes. The subjective choices were based on LCA's cultural theory that considers the three different human perspectives: egalitarian, hierarchist, and individualist. The three processes are multiple hearth incineration (MHI), fluid bed incineration (FBI), and anaerobic digestion (AD) for wastewater sludge treatment. This study revealed that for MHI, lower impacts are in the categories of human toxicity and marine ecotoxicity under the individualist perspective when compared to the other two perspectives, but higher impacts are in the categories of terrestrial acidification, terrestrial ecotoxicity, fresh water ecotoxicity, and marine ecotoxicity under the egalitarian perspective. Among the three subjective perspectives and the three studied wastewater sludge treatment processes, AD has the lowest and MHI has the highest environmental impacts. The results from this study revealed that the differences from subjective choices created large differences in LCA outcomes in climate change, human toxicity, ionizing radiation, terrestrial acidification, terrestrial ecotoxicity, and marine ecotoxicity. Findings from this study can benefit stake holders to understand the impacts of subjective choices and the limitation of LCA outcomes for making informed and sound decisions. [ABSTRACT FROM AUTHOR]

The Mount Hood Diabetes Challenge Network aimed to examine the impact of model structural uncertainty on the estimated cost-effectiveness of interventions for type 2 diabetes. Ten independent modeling groups completed a blinded simulation exercise to estimate the cost-effectiveness of 3 interventions in 2 type 2 diabetes populations. Modeling groups were provided with a common baseline population, cost and utility values associated with different model health states, and instructions regarding time horizon and discounting. We collated the results to identify variation in predictions of net monetary benefit (NMB) and the drivers of those differences. Overall, modeling groups agreed which interventions had a positive NMB (ie, were cost-effective), Although estimates of NMB varied substantially—by up to £23 696 for 1 intervention. Variation was mainly driven through differences in risk equations for complications of diabetes and their implementation between models. The number of modeled health states was also a significant predictor of NMB. This exercise demonstrates that structural uncertainty between different health economic models affects cost-effectiveness estimates. Although it is reassuring that a decision maker would likely reach similar conclusions on which interventions were cost-effective using most models, the range in numerical estimates generated across different models would nevertheless be important for price-setting negotiations with intervention developers. Minimizing the impact of structural uncertainty on healthcare decision making therefore remains an important priority. Model registries, which record and compare the impact of structural assumptions, offer one potential avenue to improve confidence in the robustness of health economic modeling. • Structural uncertainty is known to be an important, but often unexamined, contributor to healthcare decision uncertainty. • Conducting a common simulation exercise across 10 independent modeling groups using uniform parameterization has allowed us to investigate the structural drivers of differences in cost-effectiveness estimates between diabetes models. We show how blinded simulation exercises can be used to quantitatively examine both the drivers and impact of structural uncertainty. Similar exercises could be replicated to examine the effect of structural uncertainty in healthcare decision problems in other disease areas. • This article demonstrates the value of incentivizing collaboration and comparative assessments among modeling groups to critically examine their modeling practice. This generates more robust evidence for decision making. Healthcare decision makers may benefit from directly facilitating collaboration between modeling groups, such as aiding funding and maintenance of model registries, which can demonstrate the relative suitability of different decision models for specific decision problems. [ABSTRACT FROM AUTHOR]

In this paper, an efficient and reliable method is developed for assessing the seismic performance of reinforced concrete (RC) frame structures by using the dynamic concentrated plastic beam–column element. Firstly, the beam–column element considering the dynamic effect caused by strain rate sensitivity of RC materials and the epistemic uncertainties in structural parameters is proposed, in which the mechanical behavior of plastic hinge is described by the damage index-based hysteretic model. Moreover, a simplified approach is employed to consider the strain rate-sensitivity of RC materials. Then the computation procedure for probabilistic seismic analysis of RC frame structures is illustrated based on the proposed element. The change in strain rate at each time step is considered by modifying the hysteretic model, which is further used in updating the matrices in dynamic equations. Finally, the probabilistic seismic response and damage analyses of a shaking table test RC frame structure are performed and the proposed method is validated with the experimental data. Furthermore, the influences of structural uncertainties on the analytical results of maximum drift ratio and collapse probability are discussed. It is indicated that both the dynamic effect and structural uncertainties need to be seriously taken into account for obtaining a more reliable seismic response and collapse assessment of RC frame structures. [ABSTRACT FROM AUTHOR]

Even though it has been established that a hyetograph's shape affects the results of hydrological simulations, common engineering practice does not always account for this fact. Instead, a single design storm is often considered sufficient for designing a urban drainage system. This study examines the impact that this design paradigm, combined with the uncertainty introduced by subjective choices made during the design process, has on the robustness of a designed system. To do so, we evaluated a set of individual designs created by engineering students using the same Chicago hyetograph as a design storm. We then created ensembles of hyetographs with the same precipitation volume and duration as the Chicago hyetograph and evaluated the designs' hydrological responses. The results showed that designs, which performed equally well for the initial design storm, triggered varying responses for the storms in the ensembles and, consequently, showed different levels of robustness, hinting at a need to adapt the current design approach. HIGHLIGHTS Designing an urban drainage system (UDS) using a single design storm does not produce a robust result.; Storms with the same total precipitation volume but different temporal patterns can expose different vulnerabilities in an UDS.; Designer subjectivity introduces uncertainty to the performance of a UDS.;

Abstract Background Evaluating sugar-sweetened beverage (SSB) taxation often relies on simulation models. We assess how assumptions about the response to SSB taxation affect the projected body weight change and subsequent health and economic impacts related to type 2 diabetes mellitus (T2DM) using Germany as an example. Methods In the main analysis, we estimated changes in energy intake by age and sex under a 20% value-added tax on SSBs in Germany using marginal price elasticities (PE) and applied an energy equilibrium model to predict body weight changes. We then quantified the impact of several assumption modifications: SSB own-PE adjusted for consumption (M1)/based on alternative meta-analysis (M2); SSB consumption adjusted for underreporting (M3); substitution via marginal (M4a) or adjusted (M4b) cross-PE/as % of calorie change (M4c). We also assessed scenarios with alternative tax rates of 10% (S1) or 30% (S2) and including fruit juice (S3). We calculated overweight and obesity rates per modification and scenario. We simulated the impact on T2DM, associated healthcare costs, and disability-adjusted life years (DALYs) over the lifetime of the 2011 German adult population with a Markov model. Data included official demographics, national surveys, and meta-analyses. Results A 20% value-added tax in Germany could reduce the number of men and women with obesity by 210,800 [138,800; 294,100] and 80,800 [45,100; 123,300], respectively. Over the population’s lifetime, this would lead to modest T2DM-related health and economic impacts (76,700 DALYs [42,500; 120,600] averted; €2.37 billion [1.33; 3.71] costs saved). Policy impacts varied highly across modifications (all in DALYs averted): (M1) 94,800 [51,500; 150,700]; (M2) 164,200 [99,500; 243,500]; (M3) 52,600 [22,500; 91,100]; (M4a) -18,100 [-111,500; 68,300]; (M4b) 25,800 [-31,400; 81,500]; (M4c) 46,700 [25,300; 77,200]. The variability in policy impact related to modifications was similar to the variability between alternative policy scenarios (all in DALYs averted): (S1) 26,400 [9,300; 47,600]; (S2) 126,200 [73,600; 194,500]; (S3) 342,200 [234,200; 430,400]. Conclusions Predicted body weight reductions under SSB taxation are sensitive to assumptions by researchers often needed due to data limitations. Because this variability propagates to estimates of health and economic impacts, the resulting structural uncertainty should be considered when using results in decision-making.

In this paper, a novel covariance tracking receding horizon (CTRH) process is proposed to design a robust control algorithm. Some drawbacks of the equivalent robust algorithms, including infeasibility, computational complexity, and non-optimality, are alleviated in this process. Hence, first, covariance analysis is applied to rephrase the dynamic equation of the system to model the structural uncertainties. Afterward, this approach is extended to the future time horizons as the discrete-time formulations are to be embedded in the receding horizon controller framework. Then, a new constrained quadratic programming cost function is proposed considering the covariance matrix to mitigate the trajectory dispersion along with the control action. The final control rule is estimated by solving the new cost function using the Hildreth method. The efficiency of the developed robust algorithm is demonstrated by numerical simulation of two benchmark buildings equipped with active tendon systems subjected to earthquake excitations. The competency of the proposed method (CTRH) is then proven using nominal and various perturbed scenarios and outputs compared to the linear–quadratic–Gaussian (LQG) controller, sliding mode control (SMC), H∞, and conventional receding horizon (CRH) controllers, and comparative results are presented. The outcomes indicate that the proposed method not only well reduces the controlled responses compared to uncontrolled one but also demonstrate a high level of robustness against various other control approaches. Less computational complexity due to not adding any linear matrix inequalities and constraints is also one of the prominent features of the proposed approach. [ABSTRACT FROM AUTHOR]

Emerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly‐warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16‐year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic‐specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1‐2.6 and SSP5‐8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra‐model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific "answers," but as bounding for plausible, policy‐relevant questions to assess trade‐offs and mitigate risks. Plain Language Summary: As the Arctic Ocean gets warmer, melting ice is opening up new opportunities for fishing. However, we don't know where fish will go and how they can be managed sustainably. An important first step is to figure out which unknowns we can solve quickly with more research, and what is so uncertain that we will have to make decisions without ideal information. In this paper, we looked at uncertainty in a set of global models that predict how fish populations might shift in the next 10–25 years. Overall, these models show that fish populations might increase in the northern parts of the Arctic while decreasing in the south. But the models make very different predictions, and some disagree on whether fish populations will increase or decrease in certain areas. A major source of uncertainty is how sea ice will change, and how ocean life will respond. Therefore, this is a priority area to invest in long‐term research and better models. Overall, these models are too uncertain to rely on for specific management decisions about Arctic fishing. Instead, scientists and decision makers can use them to shape more informed discussions about potential trade‐offs and risks of future fishing in the Arctic. Key Points: Variation and disagreement in marine ecosystem model projections are too high to be informative for near‐term Arctic fisheries managementInsufficient inclusion and knowledge of sea ice cover and sea ice productivity dynamics are major drivers of uncertaintyResearchers should be transparent about uncertainty and risk; present model projections as the basis for hypotheses and scenario planning [ABSTRACT FROM AUTHOR]

Background: Evaluating sugar-sweetened beverage (SSB) taxation often relies on simulation models. We assess how assumptions about the response to SSB taxation affect the projected body weight change and subsequent health and economic impacts related to type 2 diabetes mellitus (T2DM) using Germany as an example. Methods: In the main analysis, we estimated changes in energy intake by age and sex under a 20% value-added tax on SSBs in Germany using marginal price elasticities (PE) and applied an energy equilibrium model to predict body weight changes. We then quantified the impact of several assumption modifications: SSB own-PE adjusted for consumption (M1)/based on alternative meta-analysis (M2); SSB consumption adjusted for underreporting (M3); substitution via marginal (M4a) or adjusted (M4b) cross-PE/as % of calorie change (M4c). We also assessed scenarios with alternative tax rates of 10% (S1) or 30% (S2) and including fruit juice (S3). We calculated overweight and obesity rates per modification and scenario. We simulated the impact on T2DM, associated healthcare costs, and disability-adjusted life years (DALYs) over the lifetime of the 2011 German adult population with a Markov model. Data included official demographics, national surveys, and meta-analyses. Results: A 20% value-added tax in Germany could reduce the number of men and women with obesity by 210,800 [138,800; 294,100] and 80,800 [45,100; 123,300], respectively. Over the population's lifetime, this would lead to modest T2DM-related health and economic impacts (76,700 DALYs [42,500; 120,600] averted; €2.37 billion [1.33; 3.71] costs saved). Policy impacts varied highly across modifications (all in DALYs averted): (M1) 94,800 [51,500; 150,700]; (M2) 164,200 [99,500; 243,500]; (M3) 52,600 [22,500; 91,100]; (M4a) -18,100 [-111,500; 68,300]; (M4b) 25,800 [-31,400; 81,500]; (M4c) 46,700 [25,300; 77,200]. The variability in policy impact related to modifications was similar to the variability between alternative policy scenarios (all in DALYs averted): (S1) 26,400 [9,300; 47,600]; (S2) 126,200 [73,600; 194,500]; (S3) 342,200 [234,200; 430,400]. Conclusions: Predicted body weight reductions under SSB taxation are sensitive to assumptions by researchers often needed due to data limitations. Because this variability propagates to estimates of health and economic impacts, the resulting structural uncertainty should be considered when using results in decision-making. [ABSTRACT FROM AUTHOR]

Parametric and structural uncertainties complicate the project management processes. The critical path is one of the pivotal parameters, which helps to control the project schedule and is used to determine the criticality of the tasks and activities that are the most decisive and should be treated during a project expediting or controlling. There may be a set of the critical paths in uncertain environment. Therefore, the main question is which of the critical paths to select. The aim of this paper is to answer to this question. We used Monte Carlo simulation to investigate the multiple critical paths. We revealed and explained several paradoxes that emerged as results of the multiple critical paths occurrence. They are inevitable late bias of the project duration under uncertainty, the tasks probability and their correlation effects, the impact of concurrent chains of the tasks on their criticality, multiplicity of the critical paths and especially multi-chain critical paths. We demonstrated that multiple critical paths are not negative effect. On the contrary, they play extraordinary useful role and are the reliable criterion of the project robustness and stability.

In recent years considerable interest has been generated in a new approach known as resist–accept–direct, or RAD, for managing ecosystems in the face of climate change. Under RAD, strategic responses to climate change are described in terms of three broad categories: resisting climate transformation, accepting the transformation and continuing to manage as best one can, and directing the transformed system toward novel ecological conditions. In particular, the potential for integrating RAD and adaptive management has been broadly considered, though absent a decision‐making framework needed for implementation.We propose a hierarchical decision scheme for RAD that accounts for strategy selection among the three RAD options, as well as adaptive decision making within each option. We use stochastic models and uncertainties about ecosystem processes to account for the dynamics of climate‐transformed ecosystems, and show how these features can be used to inform RAD strategies. Operationally, the approach involves decisions at two levels: one level involves choosing a policy for each strategy, and the second level involves deciding which strategy has the greatest policy value.The structure described here extends recent work in climate change adaptation, by including Markovian decisions under climate change, strategy‐specific policies, and value functions for assessing and selecting RAD strategies. We provide a hierarchical accounting of decisions and responses, and develop rules for the timing of those decisions.Combining RAD and adaptive management can help to organize thinking about ecological conservation under climate change, and focus attention on mechanisms for making decisions. We believe the structure presented here can facilitate conservation efforts under the non‐stationary climate conditions we are sure to face for the foreseeable future. [ABSTRACT FROM AUTHOR]

One of the most remarkable features of the central Northern Calcareous Alps (Eastern Alps, Austria) is the widespread presence of Upper Triassic deep-water carbonates (the Hallstatt facies) and Permo-Triassic evaporites resting on deep-water Middle Jurassic strata and their underlying Upper Triassic shallow-water carbonate platform successions. The Hallstatt facies and accompanying evaporites have been classically interpreted to originate either from a location south of the time-equivalent carbonate platforms, or to have been deposited in deeper water seaways within the broad platform domain. To date, this dispute has been addressed mostly through the analysis of Triassic and Jurassic facies distribution in map view, which, however, is subject to some degree of ambiguity and subjectivity. In this contribution we present, for the first time, sequentially restored regional cross-sections through the central Northern Calcareous Alps to understand the implications of the contrasting paleogeographic models. We present (a) an interpretation based on a highly allochthonous origin of the Triassic deep-water units and (b) an interpretation based on their relative autochthony in which we incorporate the potential influence of salt tectonics in the central NCA. The restored cross-sections provide a framework within which the alternative scenarios and their paleogeographic implications can be better understood. Through this analysis we propose that salt tectonics in the central NCA can provide a valid explanation for apparent inconsistencies in the relative autochthony scenario and thus constitutes a reasonable alternative to the currently accepted allochthony scenario. [ABSTRACT FROM AUTHOR]

Abstract Emerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly‐warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16‐year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic‐specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1‐2.6 and SSP5‐8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra‐model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific “answers,” but as bounding for plausible, policy‐relevant questions to assess trade‐offs and mitigate risks.

Floods are frequent natural disasters, and the management of floods involves predicting flood hydrographs and inundation maps. However, the uncertainty associated with these predictions is a significant challenge in flood management. This study focuses on the uncertainty associated with the selection of mathematical models to estimate flood hydrographs and their impact on inundation map predictions. The study involved setting up 27 hydrological models using different mathematical models for the loss, transform, and routing methods within the Hydrologic Engineering center – Hydrologic Modelling system (HEC-HMS) model. The results showed that the peak flow estimation depends on the models adopted for predicting the flood hydrograph. This uncertainty was translated to the flood inundation maps, where the inundated area varied across different flood events. The study also found that the hydrological conditions prior to the start of the event impact the hydrological model predictive uncertainty, and ensemble predictions by Bayesian model averaging can reduce the uncertainty. [ABSTRACT FROM AUTHOR]

Truss string structures (TSSs) are often prone to the risk of progressive collapse owing to critical member failures in accidental events. Moreover, the randomness of material properties, cross-sectional dimensions, and construction errors can inevitably lead to fluctuations in the collapse resistance of structures. Thus, to avoid catastrophic collapse of TSSs, it is essential to investigate the resistance to progressive collapse of TSSs considering structural uncertainties. In this study, three limit states of TSSs under key member failures are defined. In addition, a probabilistic evaluation methodology for the progressive collapse resistance of single TSSs considering structural uncertainties is proposed and extended to spatial TSSs. The analysis reveals that the external load of single or spatial TSSs reaching different limit states due to cable failure is less than that of the failure of the bottom chord of the support. Furthermore, the fragility analysis shows that the progressive collapse resistance of TSSs exposed to the failure of the bottom chord of the support is greater than that of cable failure, the cable failure should be given more attention than the failure of the bottom chord of the support in the structural design and post-maintenance of TSSs. The sensitivity analysis indicates that the performance of single TSSs against progressive collapse is more sensitive to the variability of random parameters. In addition, the spatial effect can considerably enhance the performance of TSSs against progressive collapse when the bottom chord of the support fails, whereas the improvement is marginal when the cable fails. [ABSTRACT FROM AUTHOR]

Featured Application: This study is aimed at sensitivity of seismic fragility demand parameters caused by structural uncertainty of high-speed railway extradosed cable-stayed bridge considering the Yuanjiang extra-large bridge on Huaihua–Shangyang–Hengyang Railway in China. Based on the probability distribution and correlation of random parameters, a sampling analysis method is proposed herein. Furthermore, a dynamic 3D finite element model of the employed bridge is established by using OpenSEES nonlinear software with full consideration of the randomness of structural parameters using sampling analysis. Based on these findings, some important conclusions were drawn. We believe that our study makes a significant contribution to the literature because although existing studies have focused on static parameter sensitivity analyses and have provided evidence for the design and construction control of cable-stayed bridges, the dynamic sensitivity studies especially for the structural parameter uncertainty of high-speed railway extradosed cable-stayed bridges have not been extensively studied. Further, we believe that this paper will be of interest to the readership of your journal because our analysis employs innovative research techniques, and our findings have the potential to provide guidance for the seismic fragility analysis of high-speed railway extradosed cable-stayed bridges. This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal's policies, and we believe that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare. It is known that the extradosed cable-stayed bridge, a hybrid bridge, possesses the virtues of both classic cable-stayed bridges and girder bridges in mechanical behaviors. In this paper, the sensitivity of seismic fragility demand parameters (SFDP) of a high-speed railway extradosed cable-stayed bridge is studied systematically along with the consideration of structural parameter uncertainty. Based on the probability distribution and correlation of random parameters, the Latin hypercube sampling method is adopted herein. The dynamic 3D finite element model of the employed bridge is established by using powerful and attractive OpenSEES nonlinear software. A nonlinear incremental dynamic analysis is performed to consider the randomness of structural parameters using sampling analysis. Some important conclusions are drawn indicating that the structural design parameter uncertainty predominantly has influence on the SFDP for fragility analysis of bridge structures. The design parameters of extradosed cable-stayed bridges are categorized and identified as primary, secondary and insensitive parameters. The high sensitivity parameters of extradosed cable-stayed bridges for fragility analysis include friction coefficient of bearing, concrete bulk density, damping ratio, peak compressive strength of confined concrete, component size and peak strain of confined concrete. Additionally, the strength and strain of unconfined concrete cannot be ignored. Furthermore, the uncertainty of structural design parameters fails to be responsible for the cable force responses due to larger girder stiffness. The structural design parameter uncertainty has a significant influence on the responses of extradosed cable-stayed bridges for seismic fragility analysis. [ABSTRACT FROM AUTHOR]

Moving to a low carbon society requires pro-active decisions to transform social and physical systems and their supporting infrastructure. However, the inherent complexity of these systems leads to uncertainty in their responses to interventions, and their critical societal role means that stakes are high. Techniques for decision making under deep uncertainty (DMDU) have recently begun to be applied in the context of transformation to a low carbon society. Applying DMDU to support transformation necessitates careful attention to uncertainty in system relationships (structural uncertainty), and to actions targeting deep leverage points to transform system relationships. This paper presents outcomes of a structured literature review of 44 case studies in which DMDU is applied to infrastructure decisions. Around half of these studies are found to neglect structural uncertainty entirely, and no study explicitly considers alternative system conceptions. Three quarters of studies consider actions targeting only parameters, a shallow leverage point for system transformation. Where actions targeting deeper leverage points are included, models of system relationships are unable to represent the transformative change these interventions could effect. The lack of attention to structural uncertainty in these studies could lead to misleading results in complex and poorly understood systems. The lack of interventions targeting deep leverage points could lead to neglect of some of the most effective routes to achieving transformative change. This review recommends greater attention to deeper leverage points and structural uncertainty in applications of DMDU targeting transformative change.

Conservation and management of biological systems involves decision‐making over time, with a generic goal of sustaining systems and their capacity to function in the future. We address four persistent and difficult conservation challenges: (1) prediction of future consequences of management, (2) uncertainty about the system's structure, (3) inability to observe ecological systems fully, and (4) nonstationary system dynamics. We describe these challenges in terms of dynamic systems subject to different sources of uncertainty, and we present a basic Markovian framework that can encompass approaches to all four challenges. Finding optimal conservation strategies for each challenge requires issue‐specific structural features, including adaptations of state transition models, uncertainty metrics, valuation of accumulated returns, and solution methods. Strategy valuation exhibits not only some remarkable similarities among approaches but also some important operational differences. Technical linkages among the models highlight synergies in solution approaches, as well as possibilities for combining them in particular conservation problems. As methodology and computing software advance, such an integrated conservation framework offers the potential to improve conservation outcomes with strategies to allocate management resources efficiently and avoid negative consequences. [ABSTRACT FROM AUTHOR]

A new hybrid deterministic–statistical energy analysis (SEA) formulation is presented by introducing a meshless method for modeling the deterministic components. Moving least square Ritz (MLSR) meshless method is applied, in which MLS is used to build the discrete model and the Ritz method allows to obtain variational formulation of the deterministic components of the governing equations. Such governing equations can be formulated via boundary conditions by penalty method and Lagrange multipliers. The hybrid model by penalty method keeps a similar formulation with the framework of the finite element SEA, while the model by the latter increases the size of the dynamic stiffness matrix and the expanded components are determined by the constraints. For validation purpose, three case studies are provided, including beam–coupled plates and plate–coupled plates built-up structure. The results by the hybrid MLSR-SEA model are compared with those by FE-SEA and Monte Carlo simulation. Good agreements of responses between the methods demonstrate the reliability of the MLSR-SEA formulation. [ABSTRACT FROM AUTHOR]

• A structural identification method is developed that can determine the interval of identification results. • A distribution model for the structural random receptance matrix is developed, taking into account uncertain model errors. • It is found that increasing the number of measurement points does not improve identification performance due to model errors. Structural identification based on substructure decoupling technique (SDT) is an efficient and widely applied method. The input for SDT consists of the frequency response functions (FRFs) obtained from testing and dynamic model solutions of the assembly and substructure. Consequently, one of the significant factors impacting identification accuracy is the error in the dynamic model. A structural identification method considering uncertain dynamic model is proposed in this work, taking into account the inherent uncertainty of these errors. The structural receptance is treated as a random matrix, and a distribution model is formulated to quantify its stochastic characteristics. The distribution model comprehensively accounts for the randomness of structural modal parameters. Through this method, random receptances are sampled directly in the matrix, instead of in the numerous modal parameters in traditional method, which effectively reduces the dimensionality of randomness. The identified results are obtained through a Monte Carlo simulation (MCS) process, whose results constitute an interval. The application of the proposed method is not limited by specific structural forms or dynamic modeling approaches. Compared with traditional identification method that does not consider uncertainty, only three additional coefficients need to be introduced, showing its nice practicality. A simulation and an experimental study are conducted for validation. The results indicate that random model errors in the dynamic model have a substantial impact on identification accuracy, while the proposed method can significantly improve the identification performance compared to traditional method. Additionally, the influencing factors affecting the method's effectiveness are studied, and suggestions to achieve better identifying are provided regarding the selection of measured points. [ABSTRACT FROM AUTHOR]

We examine sources of uncertainty in projections of Arctic amplification (AA) using the CMIP6 multi-model (MM) ensemble and single model initial-condition large ensembles of historical and future scenario simulations. In the CMIP6 MM mean, the annual mean AA ratio is steady at approximately 2.5, both in time and across scenarios, resulting in negligibly small scenario uncertainty in the magnitude of AA. Deviations from the steady value can be found at the low and high emission scenarios due to different root causes, with the latter being mostly evident in the summer and autumn seasons. Best estimates of model uncertainty are at least an order of magnitude larger than scenario uncertainty in CMIP6. The large ensembles reveal that irreducible internal variability has a similar magnitude to model uncertainty for most of the 21st century, except in the lowest emission scenario at the end of the 21st century when it could be twice as large.

Runtime and memory requirements for typical formulations of energy system models increase non-linearly with resolution, computationally constraining large-scale models despite state-of-the-art solvers and hardware. This scaling paradigm requires omission of detail which can affect key outputs to an unknown degree. Recent algorithmic innovations employing decomposition have enabled linear increases in runtime and memory use as temporal resolution increases. Newly tractable, higher resolution systems can be compared with lower resolution configurations commonly employed today in academic research and industry practice, providing a better understanding of the potential biases or inaccuracies introduced by these abstractions. We employ a state-of-the art electricity system planning model and new high-resolution systems to quantify the impact of varying degrees of spatial, temporal, and operational resolution on results salient to policymakers and planners. We find models with high spatial and temporal resolution result in more realistic siting decisions and improved emissions, reliability, and price outcomes. Errors are generally larger in systems with low spatial resolution, which omit key transmission constraints. We demonstrate that high temporal resolution cannot overcome biases introduced by low spatial resolution, and vice versa. While we see asymptotic improvements to total system cost and reliability with increased resolution, other salient outcomes such as siting accuracy and emissions exhibit continued improvement across the range of model resolutions considered. We conclude that modelers should carefully balance resolution on spatial, temporal, and operational dimensions and that novel computational methods enabling higher resolution modeling are valuable and can further improve the decision support provided by this class of models.

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0-100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20th century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

This paper investigates a novel mid-rise large-span cassette structure. The unique limit state divisions of the cassette structure are studied by following the probabilistic seismic capacity analysis (PSCA) method. A numerical model is established and verified through the use of shake table test results. Multidirectional pushover analyses is performed, and the directional effect of the 3D cassette structure is considered. The uncertainty present in fourteen major structural parameters is also considered in the modelling by using Latin hypercube sampling (LHS). The probability density function (PDF), cumulative distribution function (CDF), coefficient of variability (COV) and PSCA curves are obtained. The probabilistic distributions of the PSCA curves suggest that the threshold values of the maximum storey drift ratios of the large-span cassette are 1/550, 1/200, 1/60, and 1/25 for the slight damage (SD), moderate damage (MD), extensive damage (ED) and complete damage (CD) limit states, respectively. Furthermore, the average median values and logarithmic standard deviations are also given for the further probabilistic seismic fragility analysis and structural assessment performed. Finally, sensitivity analyses are conducted to examine the effects of critical structural parameters on structural damage measures. The results show that the structural performance is more sensitive to material parameters than load parameters. [ABSTRACT FROM AUTHOR]

We propose to evaluate the impact of rainfall–runoff model (RRM) structural uncertainty on climate model evaluation, performed within a process-oriented framework using the RRM. Structural uncertainty is assessed with an ensemble approach using three conceptual RRMs (HBV, IHACRES and GR4J). We evaluate daily precipitation and temperature from 11 regional climate models forced by five general circulation models (GCM–RCMs), issued from EURO-CORDEX. The assessment was performed over the reference period (1970–2000) for five catchments situated in northern Tunisia. Seventeen discharge performance indexes were used to explore the representation of hydrological processes. The three RRMs performed well over the reference period, with Nash–Sutcliffe efficiency values ranging from 0.70 to 0.90 and bias close to 0%. The ranking of GCM–RCMs according to hydrological performance indexes is more meaningful before the bias correction, which considerably reduces the differences between GCM- and RCM-driven hydrological simulations. Our results illustrate a strong similarity between the different RRMs in terms of raw GCM–RCM performances over the reference period for the majority of performance indexes, in spite of their different model structures. This proves that the structural uncertainty induced by RRMs does not affect GCM–RCM evaluation and ranking, which contributes to consolidate the RRM as a standard tool for climate model evaluation. HIGHLIGHTS Structural uncertainty induced using rainfall–runoff models does not affect climate model evaluation and ranking performed using hydrological modelling.; Importance of considering a wide set of hydrological performance indexes to evaluate climate model performance.; Climate models that rank well on high-flow performance indexes rank poorly on low-flow performance indexes and vice versa.;

Background and purpose : Recent methodological reviews of evaluations of behaviour change interventions in public health have highlighted that the decay in effectiveness over time has been mostly overlooked, potentially leading to suboptimal decision-making. While, in principle, discrete-time Markov chains—the most commonly used modelling approach—can be adapted to account for decay in effectiveness, this framework inherently lends itself to strong model simplifications. The application of formal and more appropriate modelling approaches has been supported, but limited progress has been made to date. The purpose of this paper is to encourage this shift by offering a practical guide on how to model decay in effectiveness using a continuous-time Markov chain (CTMC)-based approach. Methods: A CTMC approach is demonstrated, with a contextualized tutorial being presented to facilitate learning and uptake. A worked example based on the stylized case study in physical activity promotion is illustrated with accompanying R code. Discussion: The proposed framework presents a relatively small incremental change from the current modelling practice. CTMC represents a technical solution which, in absence of relevant data, allows for formally testing the sensitivity of results to assumptions regarding the long-term sustainability of intervention effects and improving model transparency. Conclusions : The use of CTMC should be considered in evaluations where decay in effectiveness is likely to be a key factor to consider. This would enable more robust model-based evaluations of population-level programmes to promote behaviour change and reduce the uncertainty surrounding the decision to invest in these public health interventions. [ABSTRACT FROM AUTHOR]

The classical PID controller is the most basic feedback control algorithm in engineering systems, while its limitation in stabilizing nonlinear control systems has been rarely explored in the literature. As a starting point, we will first establish some impossibility theorems to determine when a second-order nonlinear system cannot be stabilized by the classical PID controller. This will illustrate the limitations of PID control in addressing strongly nonlinear dynamics. Next, to overcome such limitations, we will propose several type of nonlinear PID controllers for dynamical systems subject to model uncertainties, and provide mathematical proofs of the global stability of the resulting closed-loop systems. It is important to note that the proposed nonlinear PID controllers possess considerable advantages over classical PID control when serving as global regulators. Finally, we will consider a class of controlled Liénard's system in simulations, highlighting that while the classical PID controller fails to stabilize it, the proposed nonlinear PID control can still achieve global stabilization and asymptotic regulation. [ABSTRACT FROM AUTHOR]

It is known that the extradosed cable-stayed bridge, a hybrid bridge, possesses the virtues of both classic cable-stayed bridges and girder bridges in mechanical behaviors. In this paper, the sensitivity of seismic fragility demand parameters (SFDP) of a high-speed railway extradosed cable-stayed bridge is studied systematically along with the consideration of structural parameter uncertainty. Based on the probability distribution and correlation of random parameters, the Latin hypercube sampling method is adopted herein. The dynamic 3D finite element model of the employed bridge is established by using powerful and attractive OpenSEES nonlinear software. A nonlinear incremental dynamic analysis is performed to consider the randomness of structural parameters using sampling analysis. Some important conclusions are drawn indicating that the structural design parameter uncertainty predominantly has influence on the SFDP for fragility analysis of bridge structures. The design parameters of extradosed cable-stayed bridges are categorized and identified as primary, secondary and insensitive parameters. The high sensitivity parameters of extradosed cable-stayed bridges for fragility analysis include friction coefficient of bearing, concrete bulk density, damping ratio, peak compressive strength of confined concrete, component size and peak strain of confined concrete. Additionally, the strength and strain of unconfined concrete cannot be ignored. Furthermore, the uncertainty of structural design parameters fails to be responsible for the cable force responses due to larger girder stiffness. The structural design parameter uncertainty has a significant influence on the responses of extradosed cable-stayed bridges for seismic fragility analysis.

Illicit supply chains for products like counterfeit Personal Protective Equipment (PPE) are characterized by sparse data and great uncertainty about the operational and logistical structure, making criminal activities largely invisible to law enforcement and challenging to intervene in. Simulation is a way to get insight into the behavior of complex systems, using calibration to tune model parameters to match its real-world counterpart. Calibration methods for simulation models of illicit supply chains should work with sparse data, while also tuning the structure of the simulation model. Thus, this study addresses the question: "To what extent can various model calibration techniques reconstruct the underlying structure of an illicit supply chain when varying the degree of data sparseness?" We evaluate the quality-of-fit of a reference technique, Powell's Method, and three model calibration techniques that have shown promise for sparse data: Approximate Bayesian Computing, Bayesian Optimization, and Genetic Algorithms. For this, we use a simulation model of a stylized counterfeit PPE supply chain as ground truth. We extract data from this ground truth and systematically vary its sparseness. We parameterize structural uncertainty using System Entity Structure. The results demonstrate that Bayesian Optimization and Genetic Algorithms are suitable for reconstructing the underlying structure of an illicit supply chain for a varying degree of data sparseness. Both techniques identify a diverse set of optimal solutions that fit with the sparse data. For a comprehensive understanding of illicit supply chain structures, we propose to combine the results of the two techniques. Future research should focus on developing a combined algorithm and incorporating solution diversity. [ABSTRACT FROM AUTHOR]

• A fiber random distribution plug-in is developed to realize rapid generation of uncertain UCF/PEEK models. The script file written in Python is used to achieve rapid modeling and representation. • A mathematical expression adopted the trust region algorithm is proposed to accurately describe the detailed cross-sectional shapes and fluctuation amplitudes characteristics of BCF/PEEK. Based on the Python script, a complex meso-scale RVE model considers the random structure size of fiber bundle is established. • The influence of yarn direction uncertainty, fiber volume fraction uncertainty and braided angle between fiber bundles on mechanical properties of BCF/PEEK are investigated furtherly. The correctness of the model is verified by the standard tensile experiment. The focus of this study is exploring a scale-span characterization method to predict the mechanical properties of Braided Carbon Fiber Reinforced Poly Ether Ether Ketone (BCF/PEEK) considering structural uncertainty. Firstly, a complicated micro-scale Representative Volume Element (RVE) model that considers the random position and size of fiber was established via the developed Python script. Analogously, a mathematical expression adopted by the trust region algorithm was proposed to accurately describe the detailed cross-sectional shapes and fluctuation amplitude characteristics of BCF/PEEK for the sake of establishing the precision meso-scale RVE model. Then, the corresponding elastic properties that consider fiber volume fraction and fiber distribution location were characterized via the span-scale characterization method. Meanwhile, the influence of fiber bundle fluctuation amplitude and fiber volume fraction on the mechanical properties was investigated as well. In addition, the mechanical properties of BCF/PEEK with the change of the braid angle between warp and weft yarn were predicted via the established meso-scale RVE model. Finally, a series of experiments have been carried out. The maximum and minimum absolute prediction deviation of all elastic property parameters were only 4.07 % and 1.41 %, respectively, which verified the proposed scale-span characterization method can predict the mechanical properties of composites well. [ABSTRACT FROM AUTHOR]

The characterization of the karst conduit network is an essential task to understand the complex flow system within karst aquifers. However, this task is challenging and often associated with uncertainty. Equivalent porous media approaches for modeling flow in karst aquifers fall short of capturing the hydraulic effect of individual karst features, while process‐oriented karst evolution models imply major computational efforts. In this study, we apply the Stochastic Karst Simulator (SKS) developed by Borghi et al. (2012) to generate karst conduit networks at a regional scale of a highly karstified carbonate aquifer located in the Eastern Mediterranean region and extensively used for water supply. The SKS generates conduit network geometries reasonably quick, using a mathematical proxy that mimics conduit evolution. The conduit simulation is based on a conceptual model of the genesis of the aquifer, consisting of different karstification phases. The stochastic approach of the algorithm enables us to generate an ensemble of conduit network realizations and to represent the uncertainties of these simulations in a Karst Probability Map. With only soft input information to constrain conduit evolution, multiple equivalent realizations yield similar resulting network geometries, indicating a robust approach. The presented methodology is numerically efficient, and its input can be easily adjusted. Subsequently, the resulting stochastic spatial distribution of conductivities can be employed for the parametrization of regional karst groundwater models. Key Points: We statistically generate multiple sets of karst conduit network geometries using input data based on soft informationThe resulting Karst Probability Map accounts for uncertainty in the spatial distribution of the karst conduit networkOur approach can assist in the integration of soft information into the parametrization of karst groundwater models [ABSTRACT FROM AUTHOR]

Background: Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models. Methods: Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences. Results: Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (−0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models (P = 0.049). Conclusions: Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions. Highlights: The findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs). There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values. [ABSTRACT FROM AUTHOR]

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Purpose: We aimed to describe the types of uncertainties examined in the economic evaluations submitted for reimbursement in Korea and their impact on the incremental cost-effectiveness ratio (ICER). Method: Fifty dossiers were submitted by pharmaceutical companies to the economic subcommittee of the Pharmaceutical Benefit Coverage Advisory Committee (PBCAC) from January 2014 to December 2018. The types of uncertainties were categorized as structural and parametric, and the frequencies of the sensitivity analysis per variables were analyzed. The impact of uncertainties was measured by the percent variance of the ICER relative to that of the base case analysis. Results: Of the 50 submissions, varying discount rate (44 submissions), followed by time horizon (38 submissions) and model assumptions (29 submissions), were most frequently used to examine structural uncertainty, while utility (42 submissions), resource use (41 submissions), and relative effectiveness (26 submissions) were used to examine parametric uncertainty. A total of 1,236 scenarios (a scenario corresponds to a case where a single variable is varied by a single range) were presented in the one-way sensitivity analyses, where parametric and structural sensitivity analyses comprised 679 and 557 scenarios, respectively. Varying drug prices had the highest impact on ICER (median variance 19.9%), followed by discount rate (12.2%), model assumptions (11.9%), extrapolation (11.8%), and time horizon (10.0%). Conclusions: Variables related to long-term assumptions, such as model assumptions, time horizon, extrapolation, and discounting rate, were related to a high level of uncertainty. Caution should be exercised when using immature data. [ABSTRACT FROM AUTHOR]

Sustainable human exploitation of living marine resources stems from a delicate balance between yield stability and population persistence to achieve socioeconomic and conservation goals. But our imperfect knowledge of how oceanic oscillations regulate temporal variation in an exploited species can obscure the risk of missing management targets. We illustrate how applying a management policy to suppress fluctuations in fishery yield in variable environments (prey density and regional climate) can present unintended outcomes in harvested predators and the sustainability of harvesting. Using Atlantic cod (Gadus morhua, an apex predatory fish) in the Barents Sea as a case study we simulate age‐structured population and harvest dynamics through time‐varying, density‐dependent and density‐independent processes with a stochastic, process‐based model informed by 27‐year monitoring data. In this model, capelin (Mallotus villosus, a pelagic forage fish), a primary prey of cod, fluctuations modulate the strength of density‐dependent regulation primarily through cannibalistic pressure on juvenile cod survival; sea temperature fluctuations modulate thermal regulation of cod feeding, growth, maturation, and reproduction. We first explore how capelin and temperature fluctuations filtered through cod intrinsic dynamics modify catch stability and then evaluate how management to suppress short‐term variability in catch targets alters overharvest risk. Analyses revealed that suppressing year‐to‐year catch variability impedes management responses to adjust fishing pressure, which becomes progressively out of sync with variations in cod abundance. This asynchrony becomes amplified in fluctuating environments, magnifying the amplitudes of both fishing pressure and cod abundance and then intensifying the density‐dependent regulation of juvenile survival through cannibalism. Although these transient dynamics theoretically give higher average catches, emergent, quasicyclic behaviors of the population would increase long‐term yield variability and elevate overharvest risk. Management strategies that overlook the interplay of extrinsic (fishing and environment) and intrinsic (life history and demography) fluctuations thus can inadvertently destabilize fish stocks, thereby jeopardizing the sustainability of harvesting. These policy implications underscore the value of ecosystem approaches to designing management measures to sustainably harvest ecologically connected resources while achieving socioeconomic security. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to describe the types of uncertainties examined in the economic evaluations submitted for reimbursement in Korea and their impact on the incremental cost-effectiveness ratio (ICER).Method: Fifty dossiers were submitted by pharmaceutical companies to the economic subcommittee of the Pharmaceutical Benefit Coverage Advisory Committee (PBCAC) from January 2014 to December 2018. The types of uncertainties were categorized as structural and parametric, and the frequencies of the sensitivity analysis per variables were analyzed. The impact of uncertainties was measured by the percent variance of the ICER relative to that of the base case analysis.Results: Of the 50 submissions, varying discount rate (44 submissions), followed by time horizon (38 submissions) and model assumptions (29 submissions), were most frequently used to examine structural uncertainty, while utility (42 submissions), resource use (41 submissions), and relative effectiveness (26 submissions) were used to examine parametric uncertainty. A total of 1,236 scenarios (a scenario corresponds to a case where a single variable is varied by a single range) were presented in the one-way sensitivity analyses, where parametric and structural sensitivity analyses comprised 679 and 557 scenarios, respectively. Varying drug prices had the highest impact on ICER (median variance 19.9%), followed by discount rate (12.2%), model assumptions (11.9%), extrapolation (11.8%), and time horizon (10.0%).Conclusions: Variables related to long-term assumptions, such as model assumptions, time horizon, extrapolation, and discounting rate, were related to a high level of uncertainty. Caution should be exercised when using immature data.

Model uncertainty and limited data are fundamental challenges to robust management of human intervention in a natural system. These challenges are acutely highlighted by concerns that many ecological systems may contain tipping points, such as Allee population sizes. Before a collapse, we do not know where the tipping points lie, if they exist at all. Hence, we know neither a complete model of the system dynamics nor do we have access to data in some large region of state space where such a tipping point might exist. We illustrate how a Bayesian non-parametric approach using a Gaussian process (GP) prior provides a flexible representation of this inherent uncertainty. We embed GPs in a stochastic dynamic programming framework in order to make robust management predictions with both model uncertainty and limited data. We use simulations to evaluate this approach as compared with the standard approach of using model selection to choose from a set of candidate models. We find that model selection erroneously favours models without tipping points, leading to harvest policies that guarantee extinction. The Gaussian process dynamic programming (GPDP) performs nearly as well as the true model and significantly outperforms standard approaches. We illustrate this using examples of simulated single-species dynamics, where the standard model selection approach should be most effective and find that it still fails to account for uncertainty appropriately and leads to population crashes, while management based on the GPDP does not, as it does not underestimate the uncertainty outside of the observed data.

• Quantifying structural uncertainties requires a vast variety of time-consuming numerical simulations. • Machine learning methods can save the computation time up to several orders of magnitude. • A machine learning algorithm can be robust enough to predict time series based on the structural information of the geological model. Including uncertainty is essential for accurate decision-making in underground applications. We propose a novel approach to consider structural uncertainty in two enhanced geothermal systems (EGSs) using machine learning (ML) models. The results of numerical simulations show that a small change in the structural model can cause a significant variation in the tracer breakthrough curves (BTCs). To develop a more robust method for including structural uncertainty, we train three different ML models: decision tree regression (DTR), random forest regression (RFR), and gradient boosting regression (GBR). DTR and RFR predict the entire BTC at once, but they are susceptible to overfitting and underfitting. In contrast, GBR predicts each time step of the BTC as a separate target variable, considering the possible correlation between consecutive time steps. This approach is implemented using a chain of regression models. The chain model achieves an acceptable increase in RMSE from train to test data, confirming its ability to capture both the general trend and small-scale heterogeneities of the BTCs. Additionally, using the ML model instead of the numerical solver reduces the computational time by six orders of magnitude. This time efficiency allows us to calculate BTCs for 2′000 different reservoir models, enabling a more comprehensive structural uncertainty quantification for EGS cases. The chain model is particularly promising, as it is robust to overfitting and underfitting and can generate BTCs for a large number of structural models efficiently. [ABSTRACT FROM AUTHOR]

According to the IEC Standard 61400-1, designers of wind turbines are required to apply statistical extrapolation techniques to estimate the extreme (ultimate) load. In the present article, the certification procedure is assessed under the uncertainty of the material properties using a simulated load time series of the National Renewable Energy Laboratory 5MW reference wind turbine rotor. The uncertainty of the material properties is introduced in the elastic properties of the composite materials based on the OptiDAT composite material database. The assessment relies on the comparison of the estimated blade extreme loads and deflections, obtained for the reference and the stochastically varied material properties. It is found that the variability of the material properties does not affect the estimated ultimate moments (differences < 1.5%) but affects the maximum flapwise deflection (differences ~8%). It is concluded that the peak over threshold peak extraction technique and the three-parameter Weibull fitting functions outperform among those considered in the article. [ABSTRACT FROM AUTHOR]

The ocean's "biological pump" significantly modulates atmospheric carbon dioxide levels. However, the complexity and variability of processes involved introduces uncertainty in interpretation of transient observations and future climate projections. Much research has focused on "parametric uncertainty," particularly determining the exponent(s) of a power‐law relationship of sinking particle flux with depth. Varying this relationship's functional form introduces additional "structural uncertainty." We use an ocean biogeochemistry model substituting six alternative remineralization profiles fit to a reference power‐law curve, to systematically characterize structural uncertainty, which, in atmospheric pCO2 terms, is roughly 50% of parametric uncertainty associated with varying the power‐law exponent within its plausible global range, and similar to uncertainty associated with regional variation in power‐law exponents. The substantial contribution of structural uncertainty to total uncertainty highlights the need to improve characterization of biological pump processes, and compare the performance of different profiles within Earth System Models to obtain better constrained climate projections. Plain Language Summary: The ocean's "biological pump" regulates atmospheric carbon dioxide levels and climate by transferring organic carbon produced at the surface by phytoplankton to the ocean interior via "marine snow," where the organic carbon is consumed and respired by microbes. This surface to deep transport is usually described by a power‐law relationship of sinking particle concentration with depth. Uncertainty in biological pump strength can be related to different variable values ("parametric" uncertainty) or the underlying equations ("structural" uncertainty) that describe organic matter export. We evaluate structural uncertainty using an ocean biogeochemistry model by systematically substituting six alternative remineralization profiles fit to a reference power‐law curve. Structural uncertainty makes a substantial contribution, about one‐third in atmospheric pCO2 terms, to total uncertainty of the biological pump, highlighting the importance of improving biological pump characterization from observations and its mechanistic inclusion in climate models. Key Points: Six alternative flux profiles fit to a Martin Curve yield large differences in atmospheric carbonStructural uncertainty comprises one‐third of total uncertainty in the ocean's biological pumpVarying particle attenuation with depth may account for half of the biological pump's overall carbon drawdown [ABSTRACT FROM AUTHOR]

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel "structural" uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or "deep" uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research. [ABSTRACT FROM AUTHOR]

Sources of uncertainty in a marine biogeochemical model include input from physical processes and the choice of functional forms representing the strength and dependencies of biogeochemical processes. This study explores characteristic signatures from these uncertainties by generating ensembles from perturbing the biogeochemistry equations and perturbing physical input using a 1-D intermediately-complex model run at five oceanographic stations. Perturbed biogeochemistry ensemble (PBE) produces larger spreads than perturbed physics ensemble (PPE), and distinctly different ensemble variations. Fractions of nitrogen in phytoplankton pool from observations show a larger variability than in any single model-ensemble member, but the PBE spread generally captures this variability, whereas the PPE spread does not. The results show that the PBE method gives a more realistic representation of uncertainty than PPE in our 1D-model setup. Our method needs to be tested in more complex models in order to understand its significance on larger scales.

Karst aquifers are characterized by high-conductivity conduits embedded in a low-conductivity fractured matrix, resulting in extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters, and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior. This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate between networks. [ABSTRACT FROM AUTHOR]

The problem of distillation columns control systems synthesis is an actual topic for many years. But the research aim of most publications is not to develop an optimal control system, but to develop decentralized control systems with autonomous continuous-time controllers. Although such control systems almost a priori do not allow to reach the theoretical optimum in the sense of classical integral performance index in the problem of control of complex technological plants, such as distillation columns, they have practical advantages in their digital implementation. One of the reasons for less attention of researchers to the problem of optimal multivariable control of distillation columns is the greater complexity of the problem of commercially suitable control systems development. The problem of digital implementation of such systems, their stability under different types of uncertainties, such as their suitability for use in conditions of structural and parametric uncertainties of the plant model, is often not studied in the works that consider this problem. The problem of synthesis of the optimal digital linear-quadratic multivariable controller for a distillation column with the use of an observer and a disturbance model is considered in the article. The problem of the optimal plant model structure for simple choice of weight matrices in solving the Riccati equation is investigated. A clear relationship between the sample time and the control quality is found. The problem of synthesis of the control system with one and with two observers (the second is for observing the integrated states) is formulated. The simple approach of weight matrices selection is suggested, which needs to specify only weight coefficients and can take into account a parametric and structural uncertainty of the plant. The mash distillation column control problem is researched. To improve the quality of control processes for all major types of disturbances, we use multivariable controllers which take into account the influence of cross-links between channels. The application of both formulated synthesis problems allowed obtaining digital control systems that meet the requirements of production and have the properties of parametric and structural robustness. [ABSTRACT FROM AUTHOR]

Soil food web models are a powerful tool for calculating the carbon and nitrogen mineralized by different soil organisms. Two limitations of the current models are that they use (a) fixed parameters for determining the relative efficiency of carbon and nitrogen conversion and (b) a generic set of trophic species (i.e. nodes).We propose a new method for analysing soil food webs that varies production efficiency and diet mechanistically with resource stoichiometry. Then, we calculated how the lumping or splitting of trophic species affects carbon and nitrogen mineralization for both net mineralization rates as well as the distribution of mineralization (i.e. sources of mineralization). Our models with additional stoichiometric details better represent organisms that consume basal resources with high C:N ratios.This is important because we show that lumping together trophic species at low trophic levels, which often differ in C:N ratios (i.e. microbial taxa), causes the largest deviations in estimates of carbon and nitrogen mineralization. One reason for the large effect of C:N ratio is that it impacts diet and production efficiency in our stoichiometric model. Conversely, lumping together species at higher trophic levels causes the largest errors when those species have different production and assimilation efficiencies. Finally, we demonstrate that differences in death rate and biomass are mostly important when lumping species that have different diets.We suggest that C:N ratios are especially important when grouping microbial taxa because microbial C:N ratios vary widely and microbes feed at low trophic levels where differences in C:N ratio mattered the most. Conversely lumping higher consumers will be most problematic when they have differences in the conversion efficiencies. We provide an approach to calculate structural error and report it with parameter uncertainty. Then, we demonstrate, using oribatid mites as an example, that considering the life history of different taxa can help us group organisms more efficiently into trophic species. In doing so, we provide a way to reduce and report the uncertainty in soil food web models and to stimulate future empirical work measuring key life history parameters. A free Plain language summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]