Various Sources Of Uncertainty Research Articles

Diffuse and Localized Functional Dysconnectivity in Schizophrenia: a Bootstrapped Top-Down Approach

Schizophrenia is a brain disorder leading to detached mind’s normally integrated processes. Hence, the exploration of the symptoms in relation to functional connectivity (FC) had great relevance in the field. Connectivity can be investigated on different levels, going from global features to single edges between pairs of regions, revealing diffuse and localized dysconnection patterns. In this context, schizophrenia is characterized by a different global integration with reduced connectivity in specific areas of the brain, part of the Default Mode Network (DMN). However, the assessment of FC presents various sources of uncertainty. This study proposes a multi-level approach for more robust group-comparison. FC data between 74 AAL brain areas of 15 healthy controls (HC) and 12 subjects with chronic schizophrenia (SZ) were used. Multi-level analyses were carried out by the previously published SPIDER-NET tool. Graph topological indexes were evaluated to assess global abnormalities. Robustness was augmented by bootstrapped (BOOT) data and the stability was evaluated by removing one (RST1) or two subjects (RST2). The DMN subgraph was extracted and specifically evaluated. Changes relevant to the overall local indexes were also analyzed. Finally, the connection weights were explored to enhance common strongest activations/deactivations. At a global level, expected trends of the indexes were found and the significance of modularity (p = 0.043) was not confirmed by BOOT (p = 0.133). The robustness assessment tests (both RST1 and RST2) highlighted more stable results for BOOT compared to the direct data testing. Conversely, significant results were found in the analysis at lower levels. The DMN highlighted reduced connectivity and strength as well as increased deactivation in the SZ group. At local level, 13 areas were found to be significantly different (p < 0.05) in the groups, highlighting a greater divergence in the frontal lobe. These results were confirmed analyzing the single negative edges, suggesting inverted connectivity between prefronto-temporal areas. In conclusion, multi-level analysis supported by BOOT is highly recommended when analyzing FC, especially when diffuse and localized dysconnections must be investigated in limited samples.

Probabilistic neural data fusion for learning from an arbitrary number of multi-fidelity data sets

In many applications in engineering and sciences analysts have simultaneous access to multiple data sources. In such cases, the overall cost of acquiring information can be reduced via data fusion or multi-fidelity (MF) modeling where one leverages inexpensive low-fidelity (LF) sources to reduce the reliance on expensive high-fidelity (HF) data. In this paper, we employ neural networks (NNs) for data fusion in scenarios where data is very scarce and obtained from an arbitrary number of sources with varying levels of fidelity and cost. We introduce a unique NN architecture that converts MF modeling into a nonlinear manifold learning problem. Our NN architecture inversely learns non-trivial (e.g., non-additive and non-hierarchical) biases of the LF sources in an interpretable and visualizable manifold where each data source is encoded via a low-dimensional distribution. This probabilistic manifold quantifies model form uncertainties such that LF sources with small bias are encoded close to the HF source. Additionally, we endow the output of our NN with a parametric distribution not only to quantify aleatoric uncertainties, but also to reformulate the network’s loss function based on strictly proper scoring rules which improve robustness and accuracy on unseen HF data. Through a set of analytic and engineering examples, we demonstrate that our approach provides a high predictive power while quantifying various sources of uncertainty. Our codes and examples can be accessed via GitLab.

Probabilistic assessment of failure of infiltration structures under model and parametric uncertainty

We focus on the quantification of the probability of failure (PF) of an infiltration structure, of the kind that is typically employed for the implementation of low impact development strategies in urban settings. Our approach embeds various sources of uncertainty. These include (a) the mathematical models rendering key hydrological traits of the system and the ensuing model parametrization as well as (b) design variables related to the drainage structure. As such, we leverage on a rigorous multi-model Global Sensitivity Analysis framework. We consider a collection of commonly used alternative models to represent our knowledge about the conceptualization of the system functioning. Each model is characterized by a set of uncertain parameters. As an original aspect, the sensitivity metrics we consider are related to a single- and a multi-model context. The former provides information about the relative importance that model parameters conditional to the choice of a given model can have on PF. The latter yields the importance that the selection of a given model has on PF and enables one to consider at the same time all of the alternative models analyzed. We demonstrate our approach through an exemplary application focused on the preliminary design phase of infiltration structures serving a region in the northern part of Italy. Results stemming from a multi-model context suggest that the contribution arising from the adoption of a given model is key to the quantification of the degree of importance associated with each uncertain parameter.

A probabilistic framework for forecasting maize yield response to agricultural inputs with sub-seasonal climate predictions

Crop yield results from the complex interaction between genotype, management, and environment. While farmers have control over what genotype to plant and how to manage it, their decisions are often sub-optimal due to climate variability. Sub-seasonal climate predictions embrace the great potential to improve risk analysis and decision-making. However, adequate frameworks integrating future weather uncertainty to predict crop outcomes are lacking. Maize (Zea mays L.) yields are highly sensitive to weather anomalies, and very responsive to plant density (plants m−2). Thus, economic optimal plat density is conditional to the seasonal weather conditions and can be anticipated with seasonal prospects. The aims of this study were to (i) design a model that describes the yield-to-plant density relationship (herein termed as yield–density) as a function of weather variables, and provides probabilistic forecasts for the economic optimum plant density (EOPD), and (ii) analyze the model predictive performance and sources of uncertainty. We present a novel approach to enable decision-making in agriculture using sub-seasonal climate predictions and Bayesian modeling. This model may inform crop management recommendations and accounts for various sources of uncertainty. A Bayesian hierarchical shrinkage model was fitted to the response of maize yield–density trials performed during the 2010–2019 period across seven states in the United States, identifying the relative importance of key weather, crop, and soil variables. Tercile forecasts of precipitation and temperature from the International Research Institute were used to forecast EOPD before the start of the season. The variables with the greatest influence on the yield–density relationship were weather anomalies, especially those variables indicating months with above-normal temperatures. Improvements on climate forecasting may also improve forecasts on yield responses to management, as we found reduced bias and error (by a factor >10), and greater precision (e.g. R2 increased from 0.26 to 0.32) for cases where weather forecasts matched observations. This study may contribute to the development of decision-support tools that can trigger discussions between farmers and consultants about management strategies and their associated risks.

Verification of a stochastic dynamic analysis method by shaking table tests of a slope retaining system

Various sources of uncertainty can greatly affect the seismic responses and seismic reliability of slope retaining systems. Many studies have observed and highlighted that ignoring the effect of uncertainties, especially the randomness of ground motions, would cause an inaccurate evaluation of seismic performance of slope retaining system. It is thus of great significance to employ the stochastic dynamic analysis method to quantify these uncertainties and corresponding effect. A recently developed stochastic dynamic analysis method, namely probability density evolution method (PDEM), have been proposed for the stochastic seismic responses analysis of geotechnical engineering. Although its efficiency and accuracy have already been proved by numerical cases, its feasibility and accuracy has not been verified from the experimental perspective, and its application in the stochastic seismic responses analysis using the field monitor data and experiment results in geotechnical engineering has not yet been studied. In the present study, a hard-strength rock slope model reinforced by pile-anchor structures is firstly constructed for the repeating shaking under a series of ground motions. A set of fully non-stationary stochastic ground motions are generated to quantify the randomness of input ground motions. Then, the stochastic seismic responses of the slope retaining system, including acceleration responses, amplification effect and earth pressure along pile-anchor structures, are analyzed and compared by the PDEM and various assumed probabilistic distribution models to illustrate the feasibility and accuracy of PDEM from the experimental perspective. The results demonstrate that the PDEM can obtain relatively more accurate stochastic seismic response results, and can also be used to analyze the experimental data from the probabilistic perspective.

Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The increasing complexity of multi-energy coordinated microgrids presents a challenge for traditional demand response providers to adapt to end users’ multi-energy interactions. The primary aim of demand response providers is to maximize their total profits via designing a pricing strategy for end users. The main challenge lies in the fact that DRPs have no access to the end users’ private preferences. To address this challenge, we propose a deep reinforcement learning-based approach to devise a coordinated scheduling and pricing strategy without requiring any private information. First, we develop an integrated scheduling model that combines power and gas demand response by converting multiple energy sources with different types of residential end users. Then, we formulate the pricing strategy as a Markov Decision Process with an unknown transition. The novel soft actor-critic algorithm is utilized to efficiently train neural networks with the entropy function and to learn the pricing strategies to maximize demand response providers’ profits under various sources of uncertainties. Case studies are conducted to demonstrate the effectiveness of the proposed approach in both deterministic and stochastic environment settings. Our proposed approach is also shown to be effective in handling different levels of uncertainties and achieving the near-optimal pricing strategy.

Comparative Analysis of Standard and Advanced USL Methodologies for Nuclear Criticality Safety

The American National Standards Institute/American Nuclear Society national standards 8.1 and 8.24 provide guidance on the requirements and recommendations for establishing confidence in the results of the computerized models used to support operation with fissionable materials. By design, the guidance is not prescriptive, leaving freedom to the analysts to determine how the various sources of uncertainties are to be statistically aggregated. Due to the involved use of statistics entangled with heuristic recipes, the resulting safety margins are often difficult to interpret. Also, these technical margins are augmented by additional administrative margins, which are required to ensure compliance with safety standards or regulations, eliminating the incentive to understand their differences. With the new resurgent wave of advanced nuclear systems, e.g., advanced reactors, fuel cycles, and fuel concepts, focused on economizing operation, there is a strong need to develop a clear understanding of the uncertainties and their consolidation methods to reduce them in manners that can be scientifically defended. In response, the current studies compare the analyses behind four notable methodologies for upper subcriticality limit estimation that have been documented in the nuclear criticality safety literature: the parametric, nonparametric, Whisper, and TSURFER methodologies. Specifically, the work offers a deep dive into the various assumptions of the noted methodologies, their adequacies, and their limitations to provide guidance on developing confidence for the emergent nuclear systems that are expected to be challenged by the scarcity of experimental data. To limit the scope, the current work focuses on the application of these methodologies to criticality safety experiments, where the goal is to calculate a bias, a bias uncertainty, and a tolerance limit for keff in support of determining an upper subcriticality limit for nuclear criticality safety.

Sources of uncertainty and their impact on stock prices evidence from emerging economies

Abstract This study investigates the short- and long-term effects of various sources of uncertainty on the share prices of key exchanges in emerging nations. The sample comprises monthly time series data from January 2017 to December 2021 for China, India, Russia, and Brazil. The study contains a version of Autoregressive-Distributive-Lag (ARDL) with error correction as well as other relevant approaches to time series. Economic policy, climate policy, pandemics, and Twitter-based uncertainty may cause a long-term decline in SSE (Shanghai Stock Exchange) composite index and BSE (Bombay Stock Exchange) Sensex index. In China, geopolitical, climatic, and pandemic uncertainty are short-term sources of uncertainty, and in India, economic policy, geopolitical, and pandemic uncertainty. Moreover, no sources of uncertainty have a long-term impact on Russia’s Moscow Exchange (MOEX) index. All sources except climate uncertainty are short-term MOEX index contributors. Pandemics and Twitter-based uncertainty are long-term sources, whereas economic policy and Twitter-based uncertainty are short-term sources for Brazilian Stock Exchange (BOVESPA) Index. This research adds to the literature by examining the relationship between distinct sources of uncertainty and an emerging market share prices index. It provides the behavior of leading share price indexes in the presence of uncertainty. The study’s conclusions only apply to emerging economies. Future research may take into account a panel dataset consisting of a large number of emerging nations to examine the same set of variables.

Tight basis cycle representatives for persistent homology of large biological data sets.

Persistent homology (PH) is a popular tool for topological data analysis that has found applications across diverse areas of research. It provides a rigorous method to compute robust topological features in discrete experimental observations that often contain various sources of uncertainties. Although powerful in theory, PH suffers from high computation cost that precludes its application to large data sets. Additionally, most analyses using PH are limited to computing the existence of nontrivial features. Precise localization of these features is not generally attempted because, by definition, localized representations are not unique and because of even higher computation cost. Such a precise location is a sine qua non for determining functional significance, especially in biological applications. Here, we provide a strategy and algorithms to compute tight representative boundaries around nontrivial robust features in large data sets. To showcase the efficiency of our algorithms and the precision of computed boundaries, we analyze the human genome and protein crystal structures. In the human genome, we found a surprising effect of the impairment of chromatin loop formation on loops through chromosome 13 and the sex chromosomes. We also found loops with long-range interactions between functionally related genes. In protein homologs with significantly different topology, we found voids attributable to ligand-interaction, mutation, and differences between species.

Dynamic State Estimation of Nonlinear Differential Algebraic Equation Models of Power Networks

This paper investigates the joint problems of dynamic state estimation of algebraic variables (voltage and phase angle) and generator states (rotor angle and frequency) of nonlinear differential algebraic equation (NDAE) power network models, under uncertainty. Traditionally, these two problems have been decoupled due to complexity of handling NDAE models. In particular, this paper offers the first attempt to solve the aforementioned problem in a coupled approach where the algebraic and generator states estimates are simultaneously computed. The proposed estimation algorithm herein is endowed with the following properties: <i>(i)</i> it is fairly simple to implement and based on well-understood Lyapunov theory; <i>(ii)</i> considers various sources of uncertainty from generator control inputs, loads, renewables, process and measurement noise; <i>(iii)</i> models phasor measurement unit installations at arbitrary buses; and <i>(iv)</i> is computationally less intensive than the decoupled approach in the literature.

A Planner-Trader Decomposition for Multimarket Hydro Scheduling

Multimarket Multireservoir Hydro Scheduling Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. In their paper titled “A Planner-Trader Decomposition for Multimarket Hydro Scheduling” Schindler, Rujeerapaiboon, Kuhn, and Wiesemann propose a bi-layer stochastic programming framework that jointly optimizes the trading strategies on the spot and reserve markets. The model faithfully accounts for uncertainty in electricity prices, water inflows, and reserve activations, and it ensures that the hydropower producers can fulfill their market commitments under any circumstances. The model is numerically challenging due to the various sources of uncertainty that are revealed at different time scales and that affect the problem's objective function and constraints, and the authors propose a new planner-trader decomposition and an information restriction for its solution. A case study based on real data from Austria reveals significant benefits of simultaneously participating in the spot and the reserve markets.

Dissociation and integration of outcome and state uncertainty signals in cognitive control.

Signals related to uncertainty are frequently observed in regions of the cognitive control network, including anterior cingulate/medial prefrontal cortex (ACC/mPFC), dorsolateral prefrontal cortex (dlPFC), and anterior insular cortex. Uncertainty generally refers to conditions in which decision variables may assume multiple possible values and can arise at multiple points in the perception-action cycle, including sensory input, inferred states of the environment, and the consequences of actions. These sources of uncertainty are frequently correlated: noisy input can lead to unreliable estimates of the state of the environment, with consequential influences on action selection. Given this correlation amongst various sources of uncertainty, dissociating the neural structures underlying their estimation presents an ongoing issue: a region associated with uncertainty related to outcomes may estimate outcome uncertainty itself, or it may reflect a cascade effect of state uncertainty on outcome estimates. In this study, we derive signals of state and outcome uncertainty from mathematical models of risk and observe regions in the cognitive control network whose activity is best explained by signals related to state uncertainty (anterior insula), outcome uncertainty (dlPFC), as well as regions that appear to integrate the two (ACC/mPFC).

System dynamics model for improving the robustness of a fresh agri-food supply chain to disruptions

The agri-food sector is subject to various sources of uncertainty and risk that can have a negative impact on its supply chain performance if not properly managed. In order to determine what actions the supply chain (SC) should take to protect itself against risks, it is necessary to analyze whether the supply chain is robust to them. This paper proposes a tool based on a system dynamics model to determine the robustness of an already designed five-stage fresh agri-food supply chain (AFSC) and its planting planning to disruptions in demand, supply, transport, and the operability of its nodes. The model is validated using the known behavior replication test and the extreme conditions test. In order to guide decision-makers in the different uses of the above system dynamic model, a methodology for the improvement of the AFSC robustness is presented and applied to a case study. As a result, the SC robustness to the defined disruptions is provided. For critical disruptions, protective actions are defined. Finally, the model is re-run to evaluate the impact of these proactive strategies on the AFSC in order to finally select the most beneficial for improving its robustness.

Preparation of Fluorine Certified Reference Material for Fluoroether Rubber

High-pressure free radical emulsion polymerization was used to prepare the fluorine certified reference material candidates for fluoroether rubber. Ion chromatography method and high performance liquid chromatography were adopted for the determination of the certified reference material, and various sources of uncertainty in the determination process were analyzed and calculated to gain the expanded uncertainty by synthesis. The homogeneity of the certified reference material was checked by F test and the stability was investigated by t test. The results show that the prepared fluorine certified reference material for fluoroether rubber has accurate value and good homogeneity and stability.

On the uncertainty in the segmentation of ultrasound images reconstructed with the total focusing method

This study presents an investigation into the uncertainty of images reconstructed by the total focusing method (TFM) using non-destructive evaluation (NDE) and phased array probes. Four neural network architectures based on the U-Net model are used to probabilistically segment TFM images and evaluate the uncertainty of the segmentation results. The models are trained on three simulated phased-array datasets, which contain various sources of uncertainty from the simulated defects or surrounding material. Physical limitations, such as the defect’s shadow zone, led to high uncertainty. Results demonstrate that probabilistic segmentation can be helpful in determining the source of uncertainty within segmented TFM images. The model performance is investigated based on several metrics, and the influence of defect size on model performance is shown. The probabilistic U-Net shows the highest F1-score overall test datasets. This study contributes to the advancement of NDE using TFM by providing insights into the uncertainty of the reconstructed images and proposing a solution for addressing it.

Uncertainty quantification analysis for simulation of wakes in wind-farms using a stochastic RANS solver, compared with a deep learning approach

Quantification of uncertainties in Reynolds–Averaged Navier–Stokes (RANS) simulations has gained a considerable interest in turbulence modeling. We present two different approaches for the quantification and propagation of model-form and operational uncertainties in context of wind turbine RANS simulations. The first approach is based on a stochastic RANS solver in OpenFOAM using intrusive polynomial chaos method (Parekh and Verstappen, 2023). Here the uncertainties are propagated through a single (large) simulation for the coupled coefficients of the polynomial expansion. The second approach is a surrogate based uncertainty quantification (SBUQ) method. The surrogate model comprises of a 3D U-Net neural network (trained over a single wind turbine) combined with a wake superposition model in order to the prediction of flow field in an array of wind turbines. The above-mentioned approaches are applied for uncertainty quantification analysis in RANS simulations of two turbulent engineering flow problems — (i) a wake past a single wind turbine, and (ii) wake interactions and power losses in an array of wind turbines. The results show that the uncertain RANS solutions from the two approaches are able to reasonably capture the reference high-fidelity solution. We also discuss comparisons between the two approaches including computational cost, applicability, generality etc. The two methods can be further explored and applied to engineering applications where it is critical to compute the turbulent RANS solution in presence of various sources of uncertainties.

Generalized uncertainty in surrogate models for concrete strength prediction

Applied soft computing has been widely used to predict material properties, optimal mixture, and failure modes. This is challenging, especially for the highly nonlinear behavior of brittle materials such as concrete. This paper proposes three surrogate modeling techniques (i.e., polynomial chaos expansion, Kriging, and canonical low-rank approximation) in concrete compressive strength regression analysis. A benchmark database of high-performance concrete is used with over 1,000 samples, and various sources of uncertainties in surrogate modeling are quantified, including meta-modeling assumptions, solvers, and sampling size. Two generalized extreme value distributional models are developed for error metrics using an extensive database of collected data in the literature. Bias and dispersion in the developed surrogate models are empirically compared with those distributions to quantify the overall accuracy and confidence level. Overall, the Kriging-based surrogate models outperform 80%–90% of the existing predictive models, and they illustrate more stable results. The selection of a proper optimization algorithm is the most important factor in surrogate modeling. For any practical purposes, the Kriging regression outperforms the polynomial chaos expansion and the low-rank approximation. The Kriging model is reliable for the pilot database and is less sensitive to modeling uncertainties. Finally, a series of composite error metrics is discussed as a decision-making tool that facilitates the selection of the best surrogate model using multiple criteria.

Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease

Bovine respiratory disease (BRD) is a major cause of illness and death in cattle; however, its global extent and distribution remain unclear. As climate change continues to impact the environment, it is important to understand the environmental factors contributing to BRD’s emergence and re-emergence. In this study, we used machine-learning models and remotely sensed climate data at 2.5 min (21 km2) resolution environmental layers to estimate the risk of BRD and predict its potential future distribution. We analysed 13,431 BRD cases from 1727 cities worldwide between 2005 and 2021 using two machine-learning models, maximum entropy (MaxEnt) and Boosted Regression Trees (BRT), to predict the risk and geographical distribution of the risk of BRD globally with varying model parameters. Different re-sampling regimes were used to visualise and measure various sources of uncertainty and prediction performance. The best-fitting model was assessed based on the area under the receiver operator curve (AUC-ROC), positive predictive power and Cohen’s Kappa. We found that BRT had better predictive power compared with MaxEnt. Our findings showed that favourable habitats for BRD occurrence were associated with the mean annual temperature, precipitation of the coldest quarter, mean diurnal range and minimum temperature of the coldest month. Similarly, we showed that the risk of BRD is not limited to the currently known suitable regions of Europe and west and central Africa but extends to other areas, such as Russia, China and Australia. This study highlights the need for global surveillance and early detection systems to prevent the spread of disease across borders. The findings also underscore the importance of bio-security surveillance and livestock sector interventions, such as policy-making and farmer education, to address the impact of climate change on animal diseases and prevent emergencies and the spread of BRD to new areas.

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. A robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

Application of machine learning in seismic fragility assessment of bridges with SMA-restrained rocking columns

This paper evaluates the seismic fragility of a two-span reinforced concrete (RC) bridge with shape memory alloy (SMA)-restrained rocking (SRR) columns through machine learning (ML) techniques. SRR columns incorporate a combination of replaceable superelastic NiTi (SMA) links and mild steel energy-dissipating links to achieve self-centering and energy dissipation, respectively, while their rocking joints are protected against compressive concrete damage through steel jacketing. To produce seismic fragility functions, initially, multi-parameter probabilistic seismic demand models (PSDMs) are generated for various engineering demand parameters through five different ML techniques (including neural network) and considering various sources of uncertainty, and the most accurate PSDMs are selected. The selected PSDMs are then interpreted using four different methods to investigate the effects of two key SRR column design parameters (self-centering coefficient and SMA link initial strain) and ambient temperature on the seismic performance of SRR columns. Subsequently, using neural networks, the PSDMs developed earlier, and appropriate capacity models, multi-parameter fragility functions are developed for various bridge damage states. After examining the effects of the two SRR column design parameters on the seismic fragility of the bridge, its seismic fragility is compared with those of the same bridge with monolithic RC and posttensioned (PT) rocking columns. It is shown that, in general, increasing the initial strain of the SMA links and decreasing the self-centering coefficient as possible (i.e., without compromising the self-centering) reduce the overall bridge damage. In addition, even considering the ambient temperature’s uncertainty, SRR columns are proven, at least, as effective as PT columns in mitigating the seismic damage of the bridges of monolithic RC columns.