Effect Of Uncertainty Research Articles

Educational Risk Management to Prevent Stakeholder Decline

In carrying out management education, there is something that is adapted to the culture. This article is inseparable from the educational management paradigm, using educational risk management to prevent a decline in stakeholders. The writing design used in this paper is exposition (descriptive). Explaining risk management to prevent the decline in risk. Various data are taken from several opinions and research results. The conclusion is that there is a crisis of survival of stakeholders and institutional capabilities in the form of uncertainty, inadequate information, weak control, and external observation. Then risk management needs to be carried out to prevent a decline in stakeholders by identifying, measuring and dealing with the causes and consequences of uncertainty in an organization

On tidal array layout sensitivity to regional and device model representation

In-stream tidal devices are ready to be deployed, yet the largest operational commercial array is limited to 6MW. Upcoming government support should see the size of such arrays increase by orders of magnitude, and thus the optimal placement of turbines within tidal arrays is an emerging challenge for successful commercial integration. Hydrodynamic models are required to predict the power produced by an array and the impact on the surrounding environment. The influence of common model inputs to layout optimisation are investigated herein. This is achieved using a shallow water equation based tidal array modelling framework, Thetis, coupled with a low cost analytical wake model (FLORIS) that allows for rapid assessment of the impact of small changes in hydrodynamic results on array micro-siting. The sensitivity of array optimisation at an intermediate development point (43 turbines) is interrogated through both artificial flow field manipulation and the variation of model input pertinent to the optimisation. A small margin exists in which an optimised array layout will perform efficiently for a deviation in flow prediction accuracy. However, incorrect flow predictions by a range sensitive to model inputs had a reduction of almost 8% on array efficiency relative to a control case. The sensitivity of flow field variance by model input changes, on extractable energy and array layout are substantial. Comparing arrays sited on flow fields using different bathymetry resolution leads to a discrepancy on average of over 2% to average array power. Arrays sited for different mesh resolution and friction representation also see average changes up to and exceeding 0.75%. For array developers and the future of this nascent industry, re-calibration of the model at every stage of data collection coupled with early acquisition of bathymetry data is critical not just for accurate power quantification, but also array efficiency. At a regional scale, quantification of the potential tidal resource must consider the consequences of uncertainty in model input data and site constraints that cannot yet be accounted for.

How certain are we about the role of uncertainty in the economy?

AbstractWhile causes and consequences of uncertainty in the US economy have attracted viable interest, the literature still lacks a consensus on several aspects. To name two matters of debate, it remains unclear whether uncertainty shocks are a source or the result of recessions and whether uncertainty shocks have adverse (or even stimulating) effects on the economy. We find that ambiguous results in these regards can be traced back to the selection of an appropriate identification strategy in structural vector autoregressive models. We find that both macroeconomic and financial uncertainty are exogenous to business cycle fluctuations and cause economic slowdowns.

TEACHER RESILIENCE: MANAGING STRESS AND ANXIETY TO THRIVE IN THE CLASSROOM, Author: Jamie Thom

Teacher resilience has become a more significant concern for teachers and educators worldwide than ever before since the educational crisis in the 2020s when Covid-19 started. Teacher Resilience: Managing Stress and Anxiety to Thrive in the Classroom by Jamie Thom, published by John Catt Educational in 2020, has timely addressed the real issues facing teachers and provided strategies that can empower them to be more resilient and thrive in the daily demands of teaching. Through anecdotal sections that reflect personal experiences in surviving teaching practice and overcoming/alleviating stress and anxiety, the author provides a thoughtful outlook on teacher resilience and addresses teachers’ mental health and well-being during a challenging time in education. This book is a toolkit with practical advice underpinned by well-established research. It is valuable for a more resilient mindset to help school teachers cope with the consequences of uncertainty and the demands of teaching and being their best.

Corporate cash holdings through economic policy uncertainty: An Australian study

This paper replicates Duong et al. (2020) and extends their work to the Australian context. We find that economic policy uncertainty (EPU) is positively associated with corporate cash holdings in both the United States and Australia, supporting the findings of the original study. However, corporate cash holdings in Australia do not mitigate the negative effects of EPU on investment in the Australian context. This study builds on the evidence base for economic policy uncertainty's effect on corporate cash holdings and provides a novel study in the Australian context.

Real-time control of combined sewer systems: Risks associated with uncertainties

Model Predictive Control (MPC) of combined sewer systems can reduce environmental degradation caused by uncontrolled overflows. However, practical uncertainties are often neglected when assessing the potential of MPC strategies. This paper aims to understand the risks associated with using a non-perfect internal MPC-model, real precipitation forecast, and realistic dynamic system capacity fluctuations. An MPC with the objective to reduce the total combined sewer overflow (CSO) volume was implemented in the case study of Eindhoven in the Netherlands where highly sensitive waterways receive the sewer overflows. Two types of risks were identified: relative system performance loss and operative deterioration. The former entails a practical decrease in efficacy of controlling CSO spills compared to the theoretical situation, whereas the latter describes the aggravation of environmental pollution compared to a static form of system operation. The results obtained demonstrate that precipitation forecast uncertainty is associated with a small relative system performance loss. Opposite to this, significant performance loss was observed as a consequence of uncertainties in the internal MPC model and the actual sewer system capacity available. The latter caused additional combined sewer overflows compared to a statically optimised control for smaller precipitation events.

Uncertainties Influencing Transportation System Performances

The design and operation of transportation systems, as with any large complex technical system, are marked by indetermination—risks and uncertainties (scientific/methodologic and/or socio-economic). This paper analyzes the occurrence and consequences of uncertainties, defined as completely unknown random events (“unknown unknowns”), on transportation system performances. Interest in the topic is justified by the considerable value and long life of transportation system components. In order to reduce the effects of uncertainties, a holistic approach to all technical infrastructures in society, regardless of the flow category (material, energy, information), is necessary. Technological progress and changes in territorial activity systems historically confirm the dynamism of the competition and complementarity relations between civil and industrial infrastructures and transport infrastructures, as well as among different modal transport/traffic infrastructures. Declining discount rates are applied to compensate for the effects of uncertainties on investment project opportunities on long time horizons. There is no unanimous agreement on the discount rate values. Unforeseen exogenous events are considered differentiated/non-systemic or undifferentiated/systemic uncertainties. They can have significant consequences on the performance of a transport system, including a change in the transport market share. Therefore, an adaptive policy is required to reduce the methodological/scientific and socio-economic uncertainties that affect the design and operation of any transportation system.

Uncertainty and gender inequality: A global investigation

There is increasing interest in the consequences of uncertainty, yet research on its effects on gender equality is scarce. This study investigates the impact of uncertainty on gender inequality using a sample of 100 countries over the period 1991–2017. The analysis is carried out for 22 indicators of gender inequality in four dimensions (employment, health, education, rights) and five uncertainties (domestic uncertainty, world uncertainty, world trade uncertainty, global economic policy uncertainty, geopolitical risk). First, uncertainty appears to increase gender inequality in employment by affecting vulnerable employment, unemployment, and self-employment; and by reducing waged and salaried employment, numbers of contributing family workers, labour force participation, and employment in industry and services. Second, uncertainty is found to be very harmful in its effect on gender equality in health, as it decreases life expectancy and survival to age 65, and increases the mortality of women. Third, uncertainty improves gender equality in education, as it increases school enrolment at primary and tertiary levels. Fourth, uncertainty improves the Women, Business and the Law Index, but has mixed effects on women’s economic, political, and social rights.

Innovations in Coastline Management With Natural and Nature-Based Features (NNBF): Lessons Learned From Three Case Studies

Coastal communities around the world are facing increased coastal flooding and shoreline erosion from factors such as sea-level rise and unsustainable development practices. Coastal engineers and managers often rely on gray infrastructure such as seawalls, levees and breakwaters, but are increasingly seeking to incorporate more sustainable natural and nature-based features (NNBF). While coastal restoration projects have been happening for decades, NNBF projects go above and beyond coastal restoration. They seek to provide communities with coastal protection from storms, erosion, and/or flooding while also providing some of the other natural benefits that restored habitats provide. Yet there remain many unknowns about how to design and implement these projects. This study examines three innovative coastal resilience projects that use NNBF approaches to improve coastal community resilience to flooding while providing a host of other benefits: 1) Living Breakwaters in New York Harbor; 2) the Coastal Texas Protection and Restoration Study; and 3) the South Bay Salt Pond Restoration Project in San Francisco Bay. We synthesize findings from these case studies to report areas of progress and illustrate remaining challenges. All three case studies began with innovative project funding and framing that enabled expansion beyond a sole focus on flood risk reduction to include multiple functions and benefits. Each project involved stakeholder engagement and incorporated feedback into the design process. In the Texas case study this dramatically shifted one part of the project design from a more traditional, gray approach to a more natural hybrid solution. We also identified common challenges related to permitting and funding, which often arise as a consequence of uncertainties in performance and long-term sustainability for diverse NNBF approaches. The Living Breakwaters project is helping to address these uncertainties by using detailed computational and physical modeling and a variety of experimental morphologies to help facilitate learning while monitoring future performance. This paper informs and improves future sustainable coastal resilience projects by learning from these past innovations, highlighting the need for integrated and robust monitoring plans for projects after implementation, and emphasizing the critical role of stakeholder engagement.

Hierarchical Bayesian Approach for Model Parameter Updating in Piezoelectric Energy Harvesters

This work proposes a hierarchical Bayesian framework to identify electromechanical properties of Piezoelectric Energy Harvesters (PEHs) and associated uncertainties based on experimental frequency response functions (FRFs). The framework allows the use of experimental data from multiple devices, potentially defined by different electromechanical properties. In the proposed hierarchical scheme, the FRF dispersion experimentally observed in groups of PEHs is explicitly modeled as a consequence of uncertainties in the model parameters rather than as a consequence of only the model prediction error typically used in classical Bayesian scheme. The Transitional Markov Chain Monte Carlo (TMCMC) method is used to establish the full posterior distribution of the model parameters. Preference towards selection of the hierarchical scheme is further confirmed by using Bayesian model class selection to compare the posterior probabilities of selecting the hierarchical or the classical scheme. The proposed framework is applied to identification of model parameters for both a single device and groups of devices. Results show that the proposed hierarchical scheme present significant advantages compared to other Bayesian based approaches for PEHs. First, it extracts more information about the model parameters using the same experimental observations (i.e., extract information at both device and group level); second, it accounts for the model parameter uncertainties across different devices; third, it could be used to identify objective priors for classical Bayesian approach.

Sustainable finance in cybersecurity investment for future profitability under uncertainty

ABSTRACT This paper examines the sustainable financing of R&D activities in the cybersecurity industry as a long-term investment to generate future profits and protect societies from the consequences of uncertainty as an essential part of a nation’s security strategy. Since this study utilises a dynamic panel model, the hierarchy of financing sources for R&D activities is determined by controlling for specific fixed effects. The estimator's efficiency is improved by adopting the system Generalised Method of Moments (GMM), which relies on the current interaction of explanatory variables to achieve a future factor of returns using STATA. The study sample extracted from the Orbis database consists of 51 cybersecurity leaders in the US and the UK from 2016 to 2020. The findings indicate that cybersecurity leaders use external funds to finance R&D activities to achieve high future returns. Although uncertainty increases significantly in the short run, all other else being equal, average future returns are high.

Regime Uncertainty and Interstate Conflict

Abstract Regime type is an important variable in international relations. Numerous scholars have theorized its effects on actors’ crisis behavior and outcomes. Despite regime type's importance, the literature has not focused on the role its uncertainty might play in interstate politics. This is in stark contrast to the scholarly attention given to uncertainty about other similarly important variables like actor capabilities, intentions, or fighting costs. In this paper, we aim to address this gap in the literature by providing a theory of regime uncertainty's effects on conflict and developing a novel measure of uncertainty about regime type in interstate relations to test our hypotheses. We find that regime uncertainty breeds caution rather than conflict: higher uncertainty about the opponent's regime type makes conflict initiation and escalation less likely in disputes, and dyads with more uncertainty are less likely to experience conflict onset.

Identifying the effect of forecast uncertainties on hybrid power system operation: A case study of Longyangxia hydro–photovoltaic plant in China

Power system operations are unavoidably influenced by forecast uncertainties. The forecast uncertainty's effect on operations was quantified in individual systems, however, was seldom investigated in a hybrid system. This study aims to quantify the effect of forecast uncertainties on the hybrid hydro–photovoltaic system operation. First, the operation model is built considering streamflow and photovoltaic forecast uncertainties with long- and short-time scales. Second, total release uncertainty is determined from the sum of squares for releases under different forecast scenarios. Finally, a variance partitioning method is used to divide the total release uncertainty into the uncertainties induced by individual forecast errors. Longyangxia hybrid hydro–photovoltaic power plant from China was selected as a case study. Results provide following insights: (1) long-term inflow forecast uncertainty accounts for 98.89% of the contribution to the long-term release uncertainty, while photovoltaic uncertainty accounts for 1.11%; (2) long-term inflow forecast uncertainty still dominates the short-term release uncertainty, with the contribution of 62.06%; and (3) the total release uncertainty decreases in long-term hybrid operation compared with the reservoir operation, indicating the complementary relationship between long-term hydropower and photovoltaic power. These implications highlight the importance of improving forecast accuracies for varied energy sources at different time scales.

The Black Hole Mass of NGC 4151 from Stellar Dynamical Modeling

Abstract The mass of a supermassive black hole (M BH) is a fundamental property that can be obtained through observational methods. Constraining M BH through multiple methods for an individual galaxy is important for verifying the accuracy of different techniques and for investigating the assumptions inherent in each method. However, there exist only a few galaxies where multiple M BH measurement techniques can be applied. NGC 4151 is one of these rare galaxies for which multiple methods can be used: stellar and gas dynamical modeling because of its proximity (D = 15.8 ± 0.4 Mpc from Cepheids), and reverberation mapping because of its active accretion. In this work, we reanalyzed H-band integral field spectroscopy of the nucleus of NGC 4151 from Gemini NIFS, improving the analysis at several key steps. We then constructed a wide range of axisymmetric dynamical models with the new orbit-superposition code Forstand. One of our primary goals is to quantify the systematic uncertainties in M BH arising from different combinations of the deprojected density profile, inclination, intrinsic flattening, and mass-to-light ratio. As a consequence of uncertainties on the stellar luminosity profile arising from the presence of the active galactic nucleus, our constraints on M BH are rather weak. Models with a steep central cusp are consistent with no black hole; however, in models with more moderate cusps, the black hole mass lies within the range of 0.25 × 107 M ⊙ ≲ M BH ≲ 3 × 107 M ⊙. This measurement is somewhat smaller than the earlier analysis presented by Onken et al. but agrees with previous M BH values from gas dynamical modeling and reverberation mapping. Future dynamical modeling of reverberation data, as well as IFU observations with JWST, will aid in further constraining the mass of the central supermassive black hole in NGC 4151.

Program Evaluation of Population- and System-Level Policies: Evidence for Decision Making.

Policy evaluations often focus on ex post estimation of causal effects on short-term surrogate outcomes. The value of such information is limited for decision making, as the failure to reflect policy-relevant outcomes and disregard for opportunity costs prohibits the assessment of value for money. Further, these evaluations do not always consider all relevant evidence, other courses of action, or decision uncertainty. In this article, we explore how policy evaluation could better meet the needs of decision making. We begin by defining the evidence required to inform decision making. We then conduct a literature review of challenges in evaluating policies. Finally, we highlight potential methods available to help address these challenges. The evidence required to inform decision making includes the impacts on the policy-relevant outcomes, the costs and associated opportunity costs, and the consequences of uncertainty. Challenges in evaluating health policies are described using 8 categories: 1) valuation space; 2) comparators; 3) time of evaluation; 4) mechanisms of action; 5) effects; 6) resources, constraints, and opportunity costs; 7) fidelity, adaptation, and level of implementation; and 8) generalizability and external validity. Methods from a broad set of disciplines are available to improve policy evaluation, relating to causal inference, decision-analytic modeling, theory of change, realist evaluation, and structured expert elicitation. The targeted review may not identify all possible challenges, and the methods covered are not exhaustive. Evaluations should provide appropriate evidence to inform decision making. There are challenges in evaluating policies, but methods from multiple disciplines are available to address these challenges. Evaluators need to carefully consider the decision being informed, the necessary evidence to inform it, and the appropriate methods.[Box: see text].

Usage of the net present value-at-risk to design ground-coupled heat pump systems under uncertain scenarios

Hybrid ground-coupled heat pump systems can efficiently heat and cool buildings by exchanging heat with geological materials. Their sizing is, however, complex and their financial profitability is hard to establish during the design phase due to uncertainties that taint important design parameters. Historically, impacts of uncertainties are assessed after sizing completion using sensitivity analyses. Unfortunately, these analyses cannot mitigate design risks, should the consequences of uncertainties be significant. Here, we show how the net present value-at-risk, a stochastic financial indicator inspired by a metric used in the financial sector, can weave the impacts of uncertainties throughout the design phase. The net present value-at-risk is compared to the net present value in a case study that considers uncertainty of construction costs, building’s heat load, energy tariffs and ground thermal conductivity. Results show that this financial indicator, although it comes at a higher computational price than traditional net present value indicator, leads to shorter payback periods, greatly reduces the risks of unforeseen financial losses and does not require further sensitivity analysis. By describing uncertain parameters with statistical distributions during sizing, the proposed designs are more conservative, still efficient and financially viable while being shielded from worst case scenarios.

Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators.

Biophysically detailed cardiac cell models reconstruct the action potential and calcium dynamics of cardiac myocytes. They aim to capture the biophysics of current flow through ion channels, pumps, and exchangers in the cell membrane, and are highly detailed. However, the relationship between model parameters and model outputs is difficult to establish because the models are both complex and non-linear. The consequences of uncertainty and variability in model parameters are therefore difficult to determine without undertaking large numbers of model evaluations. The aim of the present study was to demonstrate how sensitivity and uncertainty analysis using Gaussian process emulators can be used for a systematic and quantitive analysis of biophysically detailed cardiac cell models. We selected the Courtemanche and Maleckar models of the human atrial action potential for analysis because these models describe a similar set of currents, with different formulations. In our approach Gaussian processes emulate the main features of the action potential and calcium transient. The emulators were trained with a set of design data comprising samples from parameter space and corresponding model outputs, initially obtained from 300 model evaluations. Variance based sensitivity indices were calculated using the emulators, and first order and total effect indices were calculated for each combination of parameter and output. The differences between the first order and total effect indices indicated that the effect of interactions between parameters was small. A second set of emulators were then trained using a new set of design data with a subset of the model parameters with a sensitivity index of more than 0.1 (10%). This second stage analysis enabled comparison of mechanisms in the two models. The second stage sensitivity indices enabled the relationship between the L-type Ca2+ current and the action potential plateau to be quantified in each model. Our quantitative analysis predicted that changes in maximum conductance of the ultra-rapid K+ channel IKur would have opposite effects on action potential duration in the two models, and this prediction was confirmed by additional simulations. This study has demonstrated that Gaussian process emulators are an effective tool for sensitivity and uncertainty analysis of biophysically detailed cardiac cell models.

Why inequality persists in Africa

SUMMARY How to explain persistent inequality in Africa and its widespread consequences of uncertainty and social costs continues to be the focus of heated debate. In this debate piece, I argue that the contending orthodox, heterodox and political economy explanations are not satisfactory. Instead, stratification economics, centred on property and institutions, offers a more compelling elucidation of why stratification and inequality persist in Africa.

A Different Cosmology—Thoughts from Outside the Box

In this paper, we present a new cosmology based on the idea of a universe dominated by vacuum energy with time-varying curvature. In this model, the universe began with an exponential Plank era inflation before transitioning to a spacetime described by Einstein’s equations. While no explicit model of the Plank era is yet known, we do establish a number of properties that the vacuum of that time must have exhibited. In particular, we show that structures came into existence during that inflation that were later responsible for all cosmic structures. A new solution of Einstein’s equations incorporating time-varying curvature is presented which predicts that the scaling was initially power law with a parameter of y=1/2 before transitioning to an exponential acceleration of the present-day scaling. A formula relating the curvature to the vacuum energy density is also a part of the solution. A non-conventional model of nucleosynthesis provides a solution for the matter/antimatter asymmetry problem and a non-standard origin of the CMB. The CMB power spectrum is shown to be a consequence of uncertainties embedded during the initial inflation and the existence of superclusters. Using Einstein’s equations, we show that so-called dark matter is, in fact, vacuum energy. A number of other issues are discussed.

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

Stochastic partial differential equations (SPDEs) are ubiquitous in engineering and computational sciences. The stochasticity arises as a consequence of uncertainty in input parameters, constitutive relations, initial/boundary conditions, etc. Because of these functional uncertainties, the stochastic parameter space is often high-dimensional, requiring hundreds, or even thousands, of parameters to describe it. This poses an insurmountable challenge to response surface modeling since the number of forward model evaluations needed to construct an accurate surrogate grows exponentially with the dimension of the uncertain parameter space; a phenomenon referred to as the curse of dimensionality. State-of-the-art methods for high-dimensional uncertainty propagation seek to alleviate the curse of dimensionality by performing dimensionality reduction in the uncertain parameter space. However, one still needs to perform forward model evaluations that potentially carry a very high computational burden. We propose a novel methodology for high-dimensional uncertainty propagation of elliptic SPDEs which lifts the requirement for a deterministic forward solver. Our approach is as follows. We parameterize the solution of the elliptic SPDE using a deep residual network (ResNet). In a departure from traditional squared residual (SR) based loss function for training the ResNet, we introduce a physics-informed loss function derived from variational principles. Specifically, our loss function is the expectation of the energy functional of the PDE over the stochastic variables. We demonstrate our solver-free approach through various examples where the elliptic SPDE is subjected to different types of high-dimensional input uncertainties. Also, we solve high-dimensional uncertainty propagation and inverse problems.