Kinds Of Uncertainties Research Articles

The Impact of Geopolitical Risks on Global Supply Chains - The Example of the Palestinian-Israeli Conflict

Geopolitical risk is an international risk that transcends the borders of a single country, and in response to the diversity and suddenness of the types of geopolitical risk-causing indices, there are all kinds of uncertainties in the process of global supply chain management affected by geopolitical risk, which makes the main body of the supply chain affected by the risk. With the development of globalisation, more and more enterprises extend their production and supply chains to all parts of the world, which makes geopolitical risk one of the important challenges that they must pay attention to and deal with. Factors such as regional political instability caused by the Israeli-Palestinian conflict, disruptions in the transport of goods, the impact on regional supply chains, and volatility in financial markets all pose challenges to the stability and reliability of global supply chains.

A study and development of high‐order fuzzy time series forecasting methods for air quality index forecasting

AbstractThe endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high‐order fuzzy TSF methods, TFS‐multilayer perceptron (MLP) and HFS‐MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long‐short term memory (LSTM), and convolutional neural network (CNN). The “Wilcoxon Signed‐Rank test” and “Friedman and Nemenyi hypothesis test” are applied to the results obtained by employing different ratios in the train‐validation‐test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS‐MLP method over all other crisp and fuzzy TSF methods employed in this paper.

Uncertainty, politics, and crises: The case for cash

We analyze the repercussions of different kinds of uncertainty on cash demand, including uncertainty of cashless infrastructures, confidence crises of the financial system, natural disasters, political uncertainties, and inflationary crises. Based on a comprehensive literature survey and complemented by case studies, we derive a classification scheme how cash holdings typically evolved in each of these types of uncertainty over the past 30 years by separating between demand for domestic and international cash as well as between transaction and store of value balances. Hereby, we focus on the stabilizing macroeconomic properties of cash and recommend guidelines for cash supply by central banks and the banking system. Finally, we exemplify our analysis with five case studies from the developing world, namely Venezuela, Zimbabwe, Afghanistan, Iraq, and Libya.

Absolute partial and total ionization cross sections of carbon monoxide with electron collision from 350 eV to 8000 eV

The absolute partial and total cross sections for electron impact ionization of carbon monoxide are reported for electron energies from 350 eV to 8000 eV. The product ions (CO+, C+, O+, CO2+, C2+, and O2+) are measured by employing an ion imaging mass spectrometer and two ion-pair dissociation channels (C+ + O+ and C2+ + O+) are identified. The absolute cross sections for producing individual ions and their total, as well as for the ion-pair dissociation channels are obtained by normalizing the data of CO+ to that of Ar+ from CO–Ar mixture target with a fixed 1:1 ratio. The overall errors are evaluated by considering various kinds of uncertainties. A comprehensive comparison is made with the available data, which shows a good agreement with each other over the energy ranges that are overlapped. This work presents new cross-section data with electron energies above 1000 eV.

A New Paradigm for Decision Making under Uncertainty in Signature Forensics Applications based on Neutrosophic Rule Engine

One of the most popular and legally recognized behavioral biometrics is the individual's signature, which is used for verification and identification in many different industries, including business, law, and finance. The purpose of the signature verification method is to distinguish genuine from forged signatures, a task complicated by cultural and personal variances. Analysis, comparison, and evaluation of handwriting features are performed in forensic handwriting analysis to establish whether or not the writing was produced by a known writer. In contrast to other languages, Arabic makes use of diacritics, ligatures, and overlaps that are unique to it. Due to the absence of dynamic information in the writing of Arabic signatures, it will be more difficult to attain greater verification accuracy. On the other hand, the characteristics of Arabic signatures are not very clear and are subject to a great deal of variation (features’ uncertainty). To address this issue, the suggested work offers a novel method of verifying offline Arabic signatures that employs two layers of verification, as opposed to the one level employed by prior attempts or the many classifiers based on statistical learning theory. A static set of signature features is used for layer one verification. The output of a neutrosophic logic module is used for layer two verification, with the accuracy depending on the signature characteristics used in the training dataset and on three membership functions that are unique to each signer based on the degree of truthiness, indeterminacy, and falsity of the signature features. The three memberships of the neutrosophic set are more expressive for decision-making than those of the fuzzy sets. The purpose of the developed model is to account for several kinds of uncertainty in describing Arabic signatures, including ambiguity, inconsistency, redundancy, and incompleteness. The experimental results show that the verification system works as intended and can successfully reduce the FAR and FRR.

The Professional Activity of the Head of the Educational Institution as a Sphere of Formation of his/her Professional Creativity

The aim of our research is to show the professional activity of the head of the educational institution as a sphere of formation of his/her professional creativity. Methods of the research. The following theoretical methods of the research were used to solve the tasks formulated in the article: a categorical method, structural and functional methods, the methods of the analysis, systematization, modeling and generalization. The results of the research. We believe that the professional creativity of a manager is a process of carrying out creative activity, because it is professional creativity aimed at solving tasks that are characterized by the absence of a solution method in a given subject area, or the absence of such a method in a given management subject. The latter can be explained by the lack of subjectspecific knowledge necessary for a specialist to solve this problem. In the process of implementing professional creativity, the manager becomes aware of new knowledge for him/her as a kind of indicative basis for further development of a method of solving managerial and professional problems and tasks. In professional creativity, at first, the manager is in some uncertainty: there is no clearly formed opportunity to develop new subjects of professional activity and, on their basis, to invent a way to solve the task; this kind of uncertainty is due to the absence of any other knowledge that will clearly determine the development in asking questions. Conclusions. The practice of the manager’s creative professional activity, its reflection in theoretical studies, allows us to conclude that professional creativity is a specific activity, which is characterized by a professional direction with the aim of finding new, non-standard, original and unique rational solutions, which facilitate a new solution to use creative problem tasks by individual tasks in the professional activity and their implementation into practice.

A novel hybrid time-variant reliability analysis method through approximating bound-most-probable point trajectory

In the engineering field, time-variant reliability analysis (TRA) is used to measure the safety level of structures under time-variant uncertainties. Lacking in information or data, some uncertainties cannot be directly quantified as stochastic models, which results in the simultaneous existence of aleatory and epistemic uncertainties in most of problems. In general, stochastic and interval models are respectively used to describe aleatory and epistemic uncertainties. For the hybrid TRA (HTRA) problem considering the two kinds of uncertainties, the existing method needs to excessively evaluate the original time-variant limit-state function, which is too expensive for engineering problems. To address this issue, we propose the concept of bound-most-probable point trajectory (BMPPT) which can be used to construct the approximation of the limit-state hyper-surface. Moreover, we develop a HTRA method based on approximating BMPPT which can further improve the computational efficiency. First, based on time discretization, we transform the HTRA problem into a time-independent series-system reliability problem which can be solved by searching the bound-most-probable point (BMPP) at all discrete time instants. Then, with the BMPPT, the lower and upper bounds of the time-variant limit-state function are linearized into two Gaussian processes. Finally, the expansion optimal linear estimation and Monte Carlo simulation are performed to estimate the time-variant reliability. To avoid excessive BMPP searches, the active learning Kriging is used to approximate the BMPPT. Two numerical examples including a cantilever beam, and a 10-bar truss, and two engineering applications of the solid rocket engine shell and the rocket inter-stage structure are investigated, and the results reveal that the proposed method can solve the HTRA problems with high accuracy and efficiency.

Dealing with uncertain situations

ABSTRACT There are two ways for a situation to be uncertain. Subjective uncertainty refers to not knowing facts. Objective uncertainty refers to future events that have not been determined yet. A wide ranging literature review finds that subjective uncertainty inhibits behavior, increases conserving resources and willpower, and stimulates search for information – though in crude, sometimes dysfunctional fashion. In contrast, objective uncertainty calls for action, reflected in agentic control, increased arousal, and greater willingness to take risks. Again, some responses are irrational, such as exerting more effort for less expected reward, and betting more on uncontrollable future than past events. With both kinds of uncertainty, attention is mobilized and emotion is prolonged. Our review uncovered multiple signs that some uncertainty is beneficial and heightens enjoyment as a kind of spice of life, or, in some cases, as a welcome hope that a bad outcome might still be avoided.

An Information Security Engineering Framework for Modeling Packet Filtering Firewall Using Neutrosophic Petri Nets

Due to the Internet’s explosive growth, network security is now a major concern; as a result, tracking network traffic is essential for a variety of uses, including improving system efficiency, fixing bugs in the network, and keeping sensitive data secure. Firewalls are a crucial component of enterprise-wide security architectures because they protect individual networks from intrusion. The efficiency of a firewall can be negatively impacted by issues with its design, configuration, monitoring, and administration. Recent firewall security methods do not have the rigor to manage the vagueness that comes with filtering packets from the exterior. Knowledge representation and reasoning are two areas where fuzzy Petri nets (FPNs) receive extensive usage as a modeling tool. Despite their widespread success, FPNs’ limitations in the security engineering field stem from the fact that it is difficult to represent different kinds of uncertainty. This article details the construction of a novel packet-filtering firewall model that addresses the limitations of current FPN-based filtering methods. The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area of firewall packet filtering that are characterized by imprecise knowledge. Because of SNPNs’ symbolic ability, the packet filtration model can be quickly and easily established, examined, enhanced, and maintained. Based on the idea that the ambiguity of a packet’s movement can be described by if–then fuzzy production rules realized by the truth-membership function, the indeterminacy-membership function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. In addition, we simulate the dynamic behavior of the tracking system in light of the ambiguity inherent in packet filtering by presenting a two-level filtering method to improve the ranking of the filtering rules list. Results from experiments on a local area network back up the efficacy of the proposed method and illustrate how it can increase the firewall’s susceptibility to threats posed by network traffic.

Using indirect estimates to improve development efficiency of the deposits with flooding application

The article summarizes the experience of two groups of deposits in terrigenous reservoirs of Western Siberia flooding. It is shown that in the presence of various kinds of uncertainties, the problems of deposits flooding efficiency increasing can be successfully solved based on a limited amount of information and indirect data. Algorithms are proposed for predicting the maximum values of cross-correlation functions, reducing the degree of uncertainty in flooding success assessing, predicting the maximum optimal monthly fluid production of wells during the deposits development in the natural regime, as well as wells surrounding injection wells to justify the selective and focal flooding organization, transferring idle wells to the producing-well stock, transferring wells from other horizons. The facilities to which the obtained results, algorithms, models and conclusions can be extended with minimal risks are proposed. Keywords: indirect information; flooding; geophysical data; reservoir properties; development efficiency; deposits of Western Siberia.

Benefits of decreasing the uncertainty in the current bridge inspection practice

Numerous bridges worldwide have surpassed their service-life. To ensure the user safety, visual inspections are commonly carried out with the consequent assignment of Defect Grades. On their basis, simplified risk evaluations and maintenance intervention prioritization are formulated. Per the inherent nature of visual inspections, these ones include two kinds of uncertainty: interpretation-related and representation-related one. If to attempt to reduce their influence in the inspective process, the former, being an intrinsic feature of inspections performed by humans, could not be tackled. The latter, instead, can be decreased on the basis of a novel semantics-based inspective methodology. On the grounds of a large set of real-life inspections outputs, the present article measures the inspection quality improvement following said uncertainty reduction. This was achieved through the Expected Utility Theory in terms of utility and costs. On the grounds of these two, a novel Uncertainty-induced Cost curve allows the assessment of the cost evolution as a function of the weight of the representation-related uncertainty. The proposed semantics-based inspective methodology represents an improvement over the current-day directly assigned condition grading one, thus improving the efficiency of structural reliability assessments. This leads to an improved prioritization of bridge maintenance interventions and to an increased user safety.

Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and flexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum offer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model different kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fluctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets, reserve regulations, and risk preferences.

Communicating scientific uncertainties

Abstract. Communicating a message of scientific uncertainty to a general audience is difficult because laypeople usually lack the conceptual tools to understand uncertainty (e.g., quantification methods). In addition, uncertainties can lead to mistrust and can be misused to prevent or delay radioactive waste management. On the proposed poster, we report the results of an experimental study (N=177) that investigated preferences for and trust in different visualizations of simulation results and tested different modes of communication (verbal, numerical, and visual/graphical). The participants processed three different parts, namely (1) the variation in the form of presentation of a scientific result, (2) the variation in the sender/source of a scientific result in numerical format, and (3) a direct comparison of two representations of simulation results (deterministic and probabilistic). Besides socio-demographics, we used items to check for the potential impact of numeracy and verbal skills, the need for a cognition concept, and questions on science in general, as well as the acceptance of a potential deep-ground repository. The dependent variable in each part was the degree of trust in the scientific validity of the information. For an example result, see Fig. 1. Clear influences of the non-dependent variables are only evident to a limited extent. Women rate their numerical abilities less highly than men do. There is no difference in verbal skills. The latter variable is also not explanatory, while numerical ability is relevant for the choice of the probabilistic variant and the corresponding trust rating in Experiment 3. The most striking result is that the attitude towards a deep geological repository for high-level nuclear waste (HLW; degree of acceptance) is the decisive parameter in all experimental conditions. The oral presentation will focus on another challenge of uncertainty communication, namely unquantifiable model uncertainties. In a repository, these may arise from the heterogeneity of the host rock. One of these potential uncertainties may be an undetected fault parallel in the drift axis in clayey host rocks. At the Konrad site, an anchor failure in an infrastructure drift resulted from such a fault. To deal with this kind of uncertainty, a certain kind of flexibility is needed to react to such events. The talk should initiate a discussion on the importance of communicating these potential uncertainties and on the impact of these uncertainties on the repository program.

On the Tremaine–Weinberg method: how much can we trust gas tracers to measure pattern speeds?

ABSTRACT Pattern speeds are a fundamental parameter of the dynamical features (e.g. bars, spiral arms) of a galaxy, setting resonance locations. Pattern speeds are not directly observable, so the Tremaine–Weinberg (TW) method has become the most common method used to measure them in galaxies. However, it has not been tested properly whether this method can straightforwardly be applied to gas tracers, despite this being widely done in the literature. When applied to observations, the TW method may return invalid results, which are difficult to diagnose due to a lack of ground truth for comparison. Although some works applying the TW method to simulated galaxies exist, only stellar populations have been tested. Therefore, here we explore the applicability of the TW method for gas tracers, by applying it to hydrodynamical simulations of galaxies, where we know the true value of the bar pattern speed. We perform some simple tests to see if the TW method has a physically reasonable output. We add different kinds of uncertainties (e.g. in position angle or flux) to the data to mock observational errors based on the magnitude of uncertainty present in the observations. Secondly, we test the method on 3D simulations with chemical networks. We show that in general, applying TW to observations of gas will not recover the true pattern speed. These results have implications for many ‘pattern speeds’ reported in the literature, and based on these tests we also give some best practices for measuring pattern speeds using gas tracers going forwards.

The problem with probability

Knightian uncertainty is the circumstance in which there is not enough information available to quantitatively measure in terms of probability the uncertainty that is present. Economic decision makers usually face that kind of uncertainty when attempting to determine their best route forward into the future. But instead of confronting Knightian uncertainty head on, economists generally have assumed it away by opting instead to employ what Knight called risk as measured by probability. But there are serious questions about whether probability has the capacity to coral in a meaningful way the uncertainty many decision makers actually face, and whether its use in models of decision making provides an appropriately relevant and realistic explanation of what is going on. This paper argues that it does not by examining the meaning of the probability concept as it relates to the true uncertain environment the decision maker faces. It also suggests that the alternative measure of surprise introduced by Shackle not only returns such analyses to the more realistic realm of Knightian uncertainty, but is also a workable and suitable replacement for probability in explanations of decision making that need to account for the unpredictability of the future.

Disrupting the opportunity narrative: navigating transformation in times of uncertainty and crisis

COVID-19 posed threats for health and well-being directly, but it also revealed and exacerbated social–ecological inequalities, worsening hunger and poverty for millions. For those focused on transforming complex and problematic system dynamics, the question was whether such devastation could create a formative moment in which transformative change could become possible. Our study examines the experiences of change agents in six African countries engaged in efforts to create or support transformative change processes. To better understand the relationship between crisis, agency, and transformation, we explored how they navigated their changed conditions and the responses to COVID-19. We document three impacts: economic impacts, hunger, and gender-based violence and we examine how they (re)shaped the opportunity contexts for change. Finally, we identify four kinds of uncertainties that emerged as a result of policy responses, including uncertainty about the: (1) robustness of preparing a system to sustain a transformative trajectory, (2) sequencing and scaling of changes within and across systems, (3) hesitancy and exhaustion effects, and (4) long-term effects of surveillance, and we describe the associated change agent strategies. We suggest these uncertainties represent new theoretical ground for future transformations research.

Fuzzy sampled‐data control of PMSG‐based wind turbine systems with random occurring uncertainties

SummaryThis article investigates the robust stability of permanent magnet synchronous generator (PMSG)‐based wind turbine (WT) systems with randomly occurring uncertainties (ROUs) under a fuzzy sampled‐data control scheme. To accomplish this, the norm‐bounded uncertainties in the inductance, system inertia, and stator resistance are considered, which are modeled as ROUs by using certain Bernoulli distributed white noise sequences. The nonlinear PMSG‐based WT systems can be expressed as local linear systems with a Takagi–Sugeno (T–S) fuzzy approach. To achieve stability performance against such kinds of uncertainties and external disturbances, fuzzy‐based sampled‐data control is designed for the proposed model. Then, the sufficient conditions are derived and expressed in the form of linear matrix inequalities based on looped‐Lyapunov functionals, which ensure the asymptotic stability of the closed‐loop T–S fuzzy model for the nonlinear PMSG–based WTs with an performance index. Finally, the derived conditions are validated with the proposed model and confirm the effectiveness of the proposed control scheme against the uncertainties and disturbances. Moreover, a comparison example is conducted to show the less conservative nature of the derived conditions.

Roadmap to a Sustainable Energy System: Is Uncertainty a Major Barrier to Investments for Building Energy Retrofit Projects in Wide City Compartments?

Along the roadmap to a Sustainable Real Estate-Scape, energy retrofit campaigns on wide city compartments represent a pivotal task, where the importance of the collaboration between the public and private sectors is crucial. Energy retrofit programs on building assets are subject to multiple uncertainty factors (e.g., climate, energy-economy forecasts, etc.) that act as a primary barrier to investment in this field. This paper aims to discuss risk management techniques to understand better how to deal with this kind of uncertainty. The research specifically addresses the techniques of sensitivity analysis and Monte Carlo simulation, focusing first on the phase of variables selection and their probability definition, including climatic, environmental, energy, economic, financial, and stochastic parameters. In this article, it is suggested to include correlation coefficients in the input variables of risk analysis, preferring the two-dimension Monte Carlo simulation to its simple version, since the results are more reliable when separating aleatory from epistemic uncertainty; thus, the results are more reliable. Moreover, it is seen how a sensitivity analysis based on percentile variations of the inputs provides a more accurate representation of risk if compared to the most common sensitivity analysis based on percentage deviations of the inputs. Conducting a sensitivity analysis using percentile variations gives realistic and reliable results, reflecting the tailored definition of uncertainty around the inputs on the basis of specific market analyses or historical series.

Nine Degrees of Uncertainty in Negotiations

Joint decision‐making processes such as negotiations play a vital role in diverse societal contexts spanning from business and politics to sustainability‐related negotiations. One of the most prominent examples of how negotiations play an important role in overcoming societal challenges was the COVID‐19 vaccine supply negotiations. These negotiations have put the spotlight on an aspect of joint decision‐making that always has been of great interest to both negotiation researchers and practitioners yet remains empirically understudied—the effect of uncertainty. In the present article, we develop a framework of uncertainty in negotiation using the COVID‐19 vaccine supply negotiations between the European Union and pharmaceutical companies as an example. More specifically, we categorize different kinds of uncertainty based both on mathematical considerations (i.e., differentiation between risk, ambiguity, and uncertainty) as well as on more circumstantial factors. To do so, we adapted the nine kinds of uncertainty in environmental governance proposed by Dewulf and Biesbroek to the more general context of negotiations. We first differentiate between three natures of uncertainty (i.e., lack of knowledge, unpredictability, and interpretations) and three objects of uncertainty (i.e., issue‐based, strategy‐based, and context‐based). Second, we illustrate the psychological barriers that negotiators face when handling uncertainty, before concluding with proposals for practitioners on how to manage different kinds of uncertainty. Overall, we aim at stimulating investigations of the effects of uncertainty in mixed‐motive decision‐making while simultaneously helping negotiation teachers and practitioners better cope with the additional demands created by specific kinds of uncertainty.

Modeling stochastic saline groundwater occurrence in coastal aquifers

The issue of freshwater salinization in coastal areas has grown in importance with the increase of the demand of groundwater supply and the more frequent droughts. However, the spatial patterns of salinity contamination are not easy to be understood, as well as their numerical modeling is subject to various kinds of uncertainty. This paper offers a robust, flexible, and reliable geostatistical methodology to provide a stochastic assessment of salinity distribution in alluvial coastal areas. The methodology is applied to a coastal aquifer in Campania (Italy), where 83 monitoring wells provided depth-averaged salinity data. A Monte Carlo (MC) framework was implemented to simulate depth-averaged groundwater salinity fields. Both MC stochastic fields and the mean across MC simulations enabled to the delineation of which areas are subject to high salinity. Then, a probabilistic approach was developed setting up salinity thresholds for agricultural use to delineate the areas with unsuitable groundwater for irrigation purposes. Furthermore, steady spatial patterns of saline wedge lengths were unveiled through uncertainty estimates of seawater ingression at the Volturno River mouth. The results were compared versus a calibrated numerical model with remarkable model fit (R2=0.96) and versus an analytical solution, obtaining similar wedge lengths. The results pointed out that the high groundwater salinities found inland (more than 2 km from the coastline) could be ascribed to trapped paleo-seawater rather than to actual seawater intrusion. In fact, the inland high salinities were in correspondence of thick peaty layers, which can store trapped saline waters because of their high porosity and low permeability. Furthermore, these results are consistent with the recognition of depositional environments and the position of ancient lagoon alluvial sediments, located in the same areas where are the highest (simulated) salinity fields. This robust probabilistic approach could be applied to similar alluvial coastal areas to understand spatial patterns of present salinization, to disentangle actual from paleo-seawater intrusion, and more in general to delineate zones with unsuitable salinity for irrigation purposes.