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Demands to advance toward more resilient and sustainable cities in terms of reducing casualties, economic losses, downtime, and environmental impacts derived from earthquake-induced damage are becoming more frequent. Indeed, accurate evaluations of the seismic performance of buildings via numerical simulations are crucial for the sustainable development of the built environment. Nevertheless, performance estimations could be influenced by alternative probabilistic methods that can be chosen throughout the procedure of building-specific risk assessment, specifically in the construction and validation of fragility functions. This study evaluates the numerical impacts of selecting different probabilistic models on seismic risk metrics for reinforced concrete dual wall–frame buildings. Specifically, alternative probabilistic models are implemented and evaluated for (i) the identification and elimination of unusual observations within the simulated data (i.e., outliers); (ii) the selection and implementation of different Probability Distribution Functions (PDFs) to estimate fragility functions at different limit states (LSs); and (iii) the application of goodness-of-fit tests and information criteria to assess the validity of proposed PDFs. According to the results, the risk measures showed large variability at the extreme building LS (collapse). On the other hand, for a lower LS (service level), the measures remain similar in all the cases despite the methods selected. Further, the variability observed in the collapse response is up to two times that after eliminating data outliers. Finally, the large variability obtained with the evaluated alternative probabilistic modeling methods suggests re-opening the technical discussion over the state of the practice often used in earthquake engineering to improve the decision-making process, mitigating earthquake-induced consequences in an environmentally, economically, and socially beneficial manner. [ABSTRACT FROM AUTHOR]

Theoretical and applied aspects of scenario analysis of investment projects of enterprises in the mineral resource sector of the economy are considered, its advantages and disadvantages are analyzed. Taking into account the organizational and economic features of mineral resources management, a number of new modifications of the scenario analysis method, aimed at solving an urgent problem - reducing the information uncertainty in assessing the expected efficiency and risk of investment projects, are proposed. The peculiarity of the proposed new modifications is the use of the interval-probabilistic approach in the implementation of the scenario analysis procedure. This approach is based on a moderately pessimistic system of preferences in obtaining point values of the investment project initial parameters. Fishburn estimates and the hierarchy analysis method were used to reduce subjective uncertainty. The maximum likelihood values in the sense of the maximum a priori probability are used as expected estimates. An additional indicator of risk assessment, which characterizes the probability of the event that the net present value of the project will take a value less than the specified one, is proposed. When analyzing one project, this indicator is more informative than the standard deviation. A statistical hypothesis was tested on the improvement of the validity of investment decisions developed using the modified scenario analysis method compared to the standard method.

Overuse of groundwater resources, drought, and population growth have drastically reduced the volume of water stored in aquifers. The main objective of the developed framework in the present study was the bivariate hydrological frequency analysis therefore was to establish a relationship between rainfall and groundwater fluctuations. Annual maximum sampling method and peaks-over-threshold sampling method were two kinds of data series that was used for bivariate hydrological frequency analysis. This multivariate system consisting of probabilistic modeling of rainfall characteristics has been developed with the aim of determining the severity and height of rainfall during the T-year return period and its results will be used to apply stress on the aquifer for surface artificial recharge. The results showed that the rainfall with a return period of 50-year increased the groundwater level by an average of about 170 cm at the recharge site. Furthermore, the effective radius values have been obtained more than 5 km in the flow path. Moreover, 6 effective parameters including permeability, hydraulic conductivity, saturated layer thickness, rainfall, slope and salinity were considered for the evaluation of artificial recharge locations using analytic hierarchy process (AHP). The results showed that about 9% of the study area is very suitable for constructing the structure. [ABSTRACT FROM AUTHOR]

An adaptive sparse polynomial chaos (PC) transfer matrix method (TMM) is proposed to study the dynamics modeling and analysis of probabilistic uncertain beam systems with geometric nonlinearity and thermal coupling effect. Based on the floating reference frame and the nonlinear elasticity theory, the dynamics equations of flexible beam with geometric nonlinearity and thermal coupling effect are deduced firstly, and then, its transfer equations and transfer matrices and that of the involved hinge elements are established, which can be used to assemble the overall transfer equation of beam system easily according to the system's topological structure. For a deterministic system, the dynamics of system can be obtained by solving the coupled transfer equation and transient heat conduction equation easily. The proposed TMM doesn't need the global differential–algebraic equation of system and greatly reduces the involved matrix order and calculation cost, so it is very useful for improving the valuation efficiency of the design of experiment in the subsequent uncertain analysis. When considering the stochastic parameters, above equations are all uncertain equations. Based on the hyperbolic basis truncation scheme, the least angle regression algorithm, and the posteriori cross-validation error estimation method, a nonintrusive adaptive sparse PC expansion method is adopted for uncertain analysis, which only needs a small number of model evaluations and could further reduce the calculation cost of uncertain analysis. The numerical simulation is presented to validate the correctness, feasibility, and computational efficiency of this method. By deducing new transfer equations of related body/hinge, this research can be extended to the dynamics modeling and analysis for general rigid–flexible–thermal coupling multibody systems with probabilistic uncertainties. [ABSTRACT FROM AUTHOR]

Data offloading to Mobile Edge Computing (MEC) servers is an attractive choice for resource-constrained Internet of Things (IoT) devices, towards reducing their computational effort. In this paper, we investigate the potential of partial data offloading to MEC servers, under the perspective of users' cognitive IoT devices presenting loss averse and gain seeking behavior. Due to the sharing nature of the access environment and the MEC server's computational characteristics, we treat the MEC server option as a common pool of resources with uncertain payoff returned to the users, while the local computation capability is treated as a safe option for each user. Following the properties of Prospect Theory, users' prospect-theoretic utilities are formulated exploiting the local computing and offloading overhead options under probabilistic uncertainty. Such a modeling allows for the infusion of human awareness, inherent cognitive biases and behavioral characteristics into the devices' operation, their data offloading decisions and the edge computing environment that the devices are interacting with. Accordingly, each user's optimal offloaded data to the MEC server is obtained as the outcome of a non-cooperative game, with users attempting to maximize their own utilities. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven under the probabilistic nature of the respective payoff functions, while a distributed algorithm that convergences to the PNE is designed. Numerical results are provided that demonstrate the operation and superiority of the proposed framework under different IoT scenarios and behaviors, considering both homogeneous and heterogeneous users.

Some innovative works have been done to probabilistic power flow (PPF) analysis for hybrid HVAC and LCC-VSC HVDC system in this paper. Firstly, a unified method considering precise model of converters is proposed to solve a general deterministic power flow (DPF) calculation including hybrid LCC (Line Current Converter) and VSC (Voltage Source Converter), the pure VSC-MTDC (Voltage Source Converter-Multiple Terminal Direct Current) and pure LCC system. Meanwhile, with a large amount of renewable energy sources integrated to the main grid through DC grids, it will impose a stochastic impact on the secure operation of such hybrid AC/DC grids. Therefore, it becomes necessary to model the probabilistic uncertainties and analyze their effects on the operation of hybrid AC/DC systems under different control modes. Nevertheless, most power flow analysis methods for hybrid AC/DC system are still deterministic in nature. Therefore, a probabilistic method based on the combination of Nataf transformation and Latin hypercube sampling (LHS) is developed and proposed to solve this complex PPF problem in an efficient manner, which considers correlated various probabilistic uncertainties, e.g. wind speeds, solar radiations and loads following different types of probability distribution. Finally, the effectiveness of the unified DPF method is validated in a modified IEEE 14-bus system, while the proposed PPF is verified in a modified IEEE 118-bus system and the effects of uncertainties on the diverse operation modes of hybrid AC/DC grids are discussed as well.

The uncertainty is a pivotal problem in Multi-Energy Carrier (MEC) systems, which leads to the strong demand of reasonable tools to evaluate uncertainties. When both possibilistic and probabilistic uncertainties exist in the real MEC systems, traditional possibilistic or probabilistic methods are no more suitable to be applied. Therefore, this paper proposes a hybrid possibilistic-probabilistic energy flow assessment method to evaluate these uncertainties. Firstly, to build a more precise uncertain model, the probabilistic and possibilistic uncertainties are respectively modeled by considering different uncertainties of sources, networks and loads of MEC systems, and the correlations among wind generation and energy loads. Then, the product t-norms of the extension principle plus $\alpha $ -cut method is firstly implemented in processing fuzzy energy flow, which can reduce overestimation compared with the sole $\alpha $ -cut method. Next, on the basis of Dempster-Shafer evidence theory, the hybrid possibilistic-probabilistic energy flow assessment approach is presented. Finally, two cases are carried out to verify the effectiveness and practicability of the proposed method.

Multi-access Edge Computing (MEC) has emerged as a flexible and cost-effective paradigm, enabling resource constrained mobile devices to offload, either partially or completely, computationally intensive tasks to a set of servers at the edge of the network. Given that the shared nature of the servers’ resources introduces high computation and communication uncertainty, in this paper we consider users’ risk-seeking or loss-aversion behavior in their final decisions regarding the portion of their computing tasks to be offloaded at each server in a multi-MEC server environment, while executing the rest locally. This is achieved by capitalizing on the power and principles of Prospect Theory and Tragedy of the Commons, treating each MEC server as a Common Pool of Resources available to all the users, while being rivarlous and subtractable, thus may potentially fail if over-exploited by the users. The goal of each user becomes to maximize its perceived satisfaction, as expressed through a properly formulated prospect-theoretic utility function, by offloading portion of its computing tasks to the different MEC servers. To address this problem and conclude to the optimal allocation strategy, a non-cooperative game among the users is formulated and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal data offloading, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation, while useful insights about the users’ data offloading decisions under realistic conditions and behaviors are presented. [ABSTRACT FROM AUTHOR]

We consider the difficult problem of choosing between alternatives where the payoffs for the alternatives are uncertain and modeled via discrete probability distributions. One popular approach for making a choice in this complex environment is to use the idea of expected value; we prefer alternatives with larger expected value. Here we suggest an approach for refining the calculation of the expected value to allow for the inclusion of a requirement that we prefer an alternative with payoff probability distribution P1 to an alternative with payoff probability distribution P2 by assuring that expected value of P1 is larger then the expected value of P2. [ABSTRACT FROM AUTHOR]

Process operational safety plays an important role in designing control systems for chemical processes. Motivated by this, in this work, we develop a process Safeness Index-based economic model predictive control system for a broad class of stochastic nonlinear systems with input constraints. A stochastic Lyapunov-based controller is first utilized to characterize a region of the state-space surrounding the origin, starting from which the origin is rendered asymptotically stable in probability. Using this stability region characterization and a process Safeness Index function that characterizes the region in state-space in which it is safe to operate the process, an economic model predictive control system is then developed using Lyapunov-based constraints to ensure economic optimality, as well as process operational safety and closed-loop stability in probability. A chemical process example is used to demonstrate the applicability and effectiveness of the proposed approach.

The paper establishes the necessary condition for the solution of a linear unconditional problem of combinatorial optimization on arrangements where coefficients of objective function are positive. These results are used to establish the properties of the solution of linear unconditional optimization problem on arrangements for the case where probabilistic uncertainty takes place in the definition of the feasible domain and the minimum is defined according to the linear order introduced on the set of discrete random variables: we formulate and prove the condition that can underlie the search for solution and the ways of constructing the solution in some special cases. [ABSTRACT FROM AUTHOR]

In the present paper, a probabilistic propagation model for assessing the uncertainty of the effective thermal conductivity was developed based on a combined conduction and radiation heat transfer model of a ceramic fiber blanket composite. The Monte Carlo technique was used to cope with the uncertainties in the material density, radiative properties, and boundary temperatures observed in experimental tests. The calculated effective thermal-conductivity distribution for the sample was compared with the experimental measurements performed on multiple samples, and the predicted mean values were in good agreement with the measured data. The result validates the thermal predictive model and demonstrates the suitability of the stochastic model containing statistical distributions in the input variables. Statistical information also indicates that the uncertainty effect can be enlarged at high temperatures. Response sensitivity analysis between the random inputs and the effective thermal conductivity demonstrates that the randomness in the hot-side temperature, the cold-side temperature, and extinction coefficient of the sample has a significant influence on the variability of thermal-conductivity properties. The extinction coefficient becomes more and more important with an increase of temperature due to the dominant radiative heat transfer contribution at high temperature. The analysis provides good insight into the scattering control in the experimental measurement and theoretical prediction of the effective thermal conductivity of a ceramic fiber composite. [ABSTRACT FROM AUTHOR]

Abstract: A simulation-based framework is discussed in this paper for quantification/evaluation of risk and development of automated risk assessment tools, focusing on applications to offshore wind turbines under extreme environmental conditions. The framework is founded on a probabilistic characterization of the uncertainty in the models for the excitation, the turbine and its performance. Risk is then quantified as the expected value of some risk consequence measure over the probability distributions considered for the uncertain model parameters. Stochastic simulation is proposed for the risk assessment, corresponding to the evaluation of some associated probabilistic integral quantifying risk, as it allows for the adoption of comprehensive computational models for describing the dynamic turbine behavior. For improvement of the computational efficiency, a surrogate modeling approach is introduced based on moving least squares response surface approximations. The assessment is also extended to a probabilistic sensitivity analysis that identifies the importance of each of the uncertain model parameters, i.e. risk factors, towards the total risk as well as towards each of the failure modes contributing to this risk. The versatility and computational efficiency of the advocated approaches is finally exploited to support the development of standalone risk assessment applets for automated implementation of the probabilistic risk quantification/assessment. [Copyright &y& Elsevier]

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The article discusses and proposes "robust satisficing" as an alternative to utility maximizing and as normative standard for rational decision making where probabilities can not be meaningfully specified. It argues that instead of seeking to maximize the expected value or the utility of the decision outcome, "robust satisficing" maximizes the robustness to uncertainties of a satisfactory outcome. It also explores conditions where "robust satisficing" is more appropriate than utility maximizing.

Measurement results (and, more generally, estimates) are never absolutely accurate: there is always an uncertainty, the actual value x is, in general, different from the estimate [image omitted] . Sometimes, we know the probability of different values of the estimation error [image omitted] , sometimes, we only know the interval of possible values of [image omitted] , sometimes, we have interval bounds on the cumulative distribution function of [image omitted] . To compare different measuring instruments, it is desirable to know which of them brings more information - i.e. it is desirable to gauge the amount of information. For probabilistic uncertainty, this amount of information is described by Shannon's entropy; similar measures can be developed for interval and other types of uncertainty. In this paper, we analyse the computational complexity of the problem of estimating information amount under different types of uncertainty. [ABSTRACT FROM AUTHOR]

A stochastic-based fuzzy risk assessment approach was developed by integrating stochastic simulation, expert involvement, and fuzzy logic within a general framework for systematically examining both the probabilistic and possibilistic uncertainties associated with land cover, environmental guidelines, and health evaluation criteria in an ambient air quality management system. The developed approach was applied to a case study in which sulfur dioxide (SO2) was of interest. Based on the SO2 dispersion modeling results from Monte Carlo simulation, an in-depth fuzzy risk assessment was further employed to quantify the environmental guideline-based risk and health risk due to SO2 inhalation. General risk levels were obtained through fuzzy membership functions and rule bases acquired from a comprehensive questionnaire survey. Scenarios with different air quality guidelines were also analyzed, leading to the variations of risk levels. Results indicated that the developed approach would offer an effective tool for quantifying uncertainties existing in air quality modeling parameters, evaluating their effects in risk levels and providing realistic support to related decision making in air quality management. [ABSTRACT FROM AUTHOR]

Abstract: Ontologies play a crucial role in the development of the Semantic Web as a means for defining shared terms in web resources. They are formulated in web ontology languages, which are based on expressive description logics. Significant research efforts in the semantic web community are recently directed towards representing and reasoning with uncertainty and vagueness in ontologies for the Semantic Web. In this paper, we give an overview of approaches in this context to managing probabilistic uncertainty, possibilistic uncertainty, and vagueness in expressive description logics for the Semantic Web. [Copyright &y& Elsevier]

The article presents abstracts of articles published within the issue about theory of social behavior which include "The Causal Power of Discourse," by Dave Elder-Vass, "Conceptualizing Racism and Its Subtle Forms," by Polycarp Ikuenobe and "Emotions and Narrative Analysis: A Methodological Approach," by Jochen Kleres.

Surrogate modeling is a useful tool for enabling uncertainty quantification (UQ) tasks that require many expensive model evaluations, as it replaces expensive high-fidelity models with cheap-to-evaluate surrogates. This paper investigates sparse polynomial chaos and Kriging methods for surrogate modeling of first-principles models with probabilistic uncertainty in parameters and initial conditions. The surrogate modeling methods are demonstrated on a 2-dimensional population balance (2D-PB) model for batch cooling crystallization of ibuprofen with 20 uncertain parameters. Our analysis indicates that not only sparse polynomial chaos expansions are powerful for probabilistic UQ, but also the approximation accuracy of Kriging surrogate models can be significantly improved when polynomial chaos expansions are used to describe their trend. A basis-adaptive least-angle-regression strategy is shown to be particularly useful for inducing sparsity in polynomial chaos expansions, allowing for dealing with problems with a relatively large number of uncertain inputs. The utility of sparse polynomial chaos- and Kriging-based surrogate models is illustrated for various forward and inverse UQ problems, including global sensitivity analysis as well as Bayesian and maximum a posteriori parameter estimation of the 2D-PB model, where massive savings in computational cost (up to 30,000-fold) are observed. [ABSTRACT FROM AUTHOR]

This paper extends the formulation of the Shannon entropy under probabilistic uncertainties which are basically established in terms or relative errors related to the theoretical nominal set of events. Those uncertainties can eventually translate into globally inflated or deflated probabilistic constraints. In the first case, the global probability of all the events exceeds unity while in the second one lies below unity. A simple interpretation is that the whole set of events losses completeness and that some events of negative probability might be incorporated to keep the completeness of an extended set of events. The proposed formalism is flexible enough to evaluate the need to introduce compensatory probability events or not depending on each particular application. In particular, such a design flexibility is emphasized through an application which is given related to epidemic models under vaccination and treatment controls. Switching rules are proposed to choose through time the active model, among a predefined set of models organized in a parallel structure, which better describes the registered epidemic evolution data. The supervisory monitoring is performed in the sense that the tested accumulated entropy of the absolute error of the model versus the observed data is minimized at each supervision time-interval occurring in-between each two consecutive switching time instants. The active model generates the (vaccination/treatment) controls to be injected to the monitored population. In this application, it is not proposed to introduce a compensatory event to complete the global probability to unity but instead, the estimated probabilities are re-adjusted to design the control gains.