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• We construct a new measure of firm-level information uncertainty. • In aggregate, our uncertainty measure comoves with other uncertainty measures. • This measure positively predicts future stock returns. • This measure primarily captures the idiosyncratic component in the cross-section. Firm-level uncertainty is difficult to measure in nature. We construct a new measure of firm-level information uncertainty based on uncertainty premium implied by earnings announcement returns. This new measure fundamentally differs from other firm-level uncertainty measures. We find that high-uncertainty firms outperform low-uncertainty firms by 9.59 % per annum on a risk-adjusted basis. Furthermore, this return predictability persists for up to five quarters. Our uncertainty measure and its return predictability are primarily driven by the idiosyncratic component. Overall, our results support the existence of an uncertainty premium and cast doubt on the hedgeability of uncertainty. [ABSTRACT FROM AUTHOR]

Using a sample of Taiwan stock market, this paper investigates the role of information uncertainty in the profitability of technical analysis by applying a moving average (MA) strategy to portfolios grouped according to whether firms issue stock options. Results indicate that, even though considering transaction costs, the MA strategy significantly outperforms the buy-and-hold strategy on the portfolio without option issuance, but not on the portfolio with option issuance. The results support the hypothesis that stocks that do not issue options exhibit greater information uncertainty, and thus greater price continuation, which in turn implies a superior performance of the MA strategy. [ABSTRACT FROM AUTHOR]

This paper considers a problem of source data uncertainty concerning the technical state of gas supply systems. Decision-making in the region of managing and development planning of the gas supply systems (production, transport, distribution, storage), as a rule, occurs by lacking of information, and the validity of some data is questionable. We propose to formalize the information uncertainty in terms of membership functions of the fuzzy sets. Mathematical models and algorithms for solving the various modelling and optimization problems of the natural gas transport are worked out. The results of calculations demonstrate the potential of the technique. • Fuzzy models for the technical state parameters of pipelines and compressors. • An algorithm for calculating the maximum flow capacity of a gas-transport system with fuzzily-assigned parameters. • An optimization algorithm of the operation modes of a gas transport system with fuzzily-assigned parameters. • Application of the proposed models and algorithms to the illustrative and real-world gas transport systems. [ABSTRACT FROM AUTHOR]

• COVID-19 pandemic had an adverse impact on the IPO market. • IPOs during the pandemic experience greater information uncertainty compared to those before the pandemic. • Increased uncertainty is mainly driven by the IPOs from the high-technology and the healthcare sectors. • IPO underpricing and post-IPO return volatility is positively associated with pandemic intensity and the associated government responses. Anecdotal evidence seems to suggest that the initial public offering (IPO) market performed remarkably well through the COVID-19 pandemic. To further understand this peculiar observation, we carry out a comprehensive analysis of IPOs during the pandemic vis-a-vis IPOs before the pandemic. Our findings imply that IPOs during the pandemic experience greater information uncertainty compared to those before the pandemic, and this greater uncertainty is mainly driven by the IPOs from the high-technology and the healthcare sectors. Furthermore, we find that an average IPO firm experiences larger underpricing and more post-IPO return volatility as the pandemic and the associated government responses increase in severity before the offering. Overall, our study indicates that the COVID-19 pandemic had an adverse impact on the IPO market. [ABSTRACT FROM AUTHOR]

The physical-chemical monitoring of vast oceanic areas aims at assessing the status and evolution of the environmental resource for its exploration, protection and/or better understanding. However, the interpretation of monitoring data is affected by ocean seasonality and heterogeneity, and by the quality of sampling and characterization tools used to study the environment. All these factors contribute to the uncertainty of collected information that should be expressed in determined parameter values or trends. A trend of a studied parameter quantified by values difference is significant if the observed absolute value of the difference is larger than their expanded uncertainty. The correlation of studied parameters, useful for their interpretation, is equality affected by the mentioned sources of uncertainty. This work describes the metrologically sound evaluation of trends and correlations of physicochemical parameters of vast oceanic areas where all uncertainty sources affecting the information are considered by simulating their complex impact by the Monte Carlo Method. The described methodology was successfully used to study the impact of summer upwelling in an 800 km2 coastal area offshore two large cities in Portugal. Nutrients, conductivity, salinity and temperature trends and correlations are distinguished from system heterogeneity, sampling and sample analysis uncertainty for a 99% confidence level. [Display omitted] • Simulation of the impact of the heterogeneity, and sampling and analysis uncertainty on oceanic area characterisation. • Mean value of nutrients and fundamental oceanographic parameters from a vast oceanic area reported with uncertainty. • Evaluation of the variation of mean composition values between October 2019 and May 2018 for a 99% confidence level. • Correlations between nutrients and fundamental oceanic parameters evaluated taking information uncertainty into account. • Upwelling trends and correlations confirmed from modelled spatial variation of a vast oceanic area with uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents results of research related to multicriteria decision making under information uncertainty. The Bellman–Zadeh approach to decision making in a fuzzy environment is utilized for analyzing multicriteria optimization models ( models) under deterministic information. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. This circumstance permits one to generalize the classic approach to considering the uncertainty of quantitative information (based on constructing and analyzing payoff matrices reflecting effects which can be obtained for different combinations of solution alternatives and the so-called states of nature) in monocriteria decision making to multicriteria problems. Considering that the uncertainty of information can produce considerable decision uncertainty regions, the resolving capacity of this generalization does not always permit one to obtain unique solutions. Taking this into account, a proposed general scheme of multicriteria decision making under information uncertainty also includes the construction and analysis of the so-called models (which contain fuzzy preference relations as criteria of optimality) as a means for the subsequent contraction of the decision uncertainty regions. The paper results are of a universal character and are illustrated by a simple example. [Copyright &y& Elsevier]

Abstract: One of the problems encountered in the design of manufacturing systems is how to arrange the machines on the surface of the workshop, which is commonly referred to as a layout problem. Such a problem has been widely investigated in the literature. Most approaches use optimization technique to determine the position of each facility, assuming that the required data is available. Unfortunately, this assumption is often unrealistic, since the study design of a workshop is obviously conducted much before it is operating, so that data related to customer demands, for example, is generally not known with enough precision. Indeed, if good forecasts about what is to be produced in the next weeks can be available, they will obviously become more and more unreliable as the considered period of time will increase, so that layout found using classical approaches can turn out not to be relevant on the medium or long term. We propose an approach to design a robust layout in a context where the certainty of the information available decreases over time, which is usually the case for real applications. We propose a resolution approach based on a fuzzy evolutionary algorithm, which includes uncertain customer demands for each product. We show how this problem can be stated as a fuzzy dynamic layout problem with growing uncertainty over time. We suggest an evolutionary algorithm with adapted operators. Their performances are first tested using 2crisp layout problems already published. Then the impact of increasing uncertainty is studied using a suggested benchmark. The results of our experiments show the importance of considering the degradation of the information for designing robust layouts. [Copyright &y& Elsevier]

Abstract: Recent evidence supports the existence of a mutator phenotype in cancer cells, although the mechanistic basis remains unknown. In this paper, it is shown that this enhanced genetic instability is generated by an amplified measurement uncertainty on genetic information during DNA replication. At baseline, an inherent measurement uncertainty implies an imprecision of the recognition, replication and transfer genetic information, and forms the basis for an intrinsic genetic instability in all biological cells. Genetic information is contained in the sequence of DNA bases, each existing due to proton tunnelling, as a coherent superposition of quantum states composed of both the canonical and rare tautomeric forms until decoherence by interaction with DNA polymerase. The result of such a quantum measurement process may be interpreted classically as akin to a Bernoulli trial, whose outcome X is random and can be either of two possibilities, depending on whether the proton is tunnelled (X =1) or not (X =0). This inherent quantum uncertainty is represented by a binary entropy function and quantified in terms of Shannon information entropy H(X)=−P(X =1)log2 P(X =1)− P(X =0)log2 P(X =0). Enhanced genetic instability may either be directly derived from amplified uncertainty induced by increases in quantum and thermodynamic fluctuation, or indirectly arise from the loss of natural uncertainty reduction mechanisms. [Copyright &y& Elsevier]

A simple simulation model is the best approach to describe the relationship between farming practices and water quality. The linked simulation-optimization Sediment Economic Simulation and Optimization (SEDEC) model is described, and the Monte Carlo Simulation and Generalized Sensitivity and Regret Analyses are used for accounting for uncertainty in SEDEC model inputs. A case-study based on the Highland Silver Lake Watershed in Illinois is discussed.

Numerous studies have demonstrated that connected autonomous vehicles and human-driven vehicles are now coexisting throughout a transitional phase. Traffic flow can be improved, the system can be stabilized, and less energy will be used with dedicated lanes for connected autonomous vehicles. Additionally, with few communication resources, no communication delivery is ever perfect, leading to issues with poly-information uncertainty. First, this paper provides a macroscopic model of heterogeneous traffic flow from the perspective of vehicle dynamics employing electronic throttle dynamics and poly-information uncertainty with/without dedicated lanes. Second, the mixed traffic flow linear stability criterion is derived using the linear stability theory. The third portion, which was based on the theoretical analysis, focused on the consequences of dedicated lane configurations on traffic flow as well as a discussion of the effects of various parameters on the stability of mixed traffic flow and energy consumption emissions. Finally, we modeled the Huanshan Road in Jinan, China using the experimental VISSIM platform. The analysis and demonstration of a two-way, four-lane road with or without dedicated lanes. The findings demonstrate that increasing connected autonomous vehicles penetration and dedicated lanes construction can increase traffic capacity, enhance the stability of traffic flow, and lower energy use and additional emissions. It is important to keep in mind that dedicated lanes must be built at an appropriate connected autonomous vehicle penetration rate to boost traffic flow without squandering resources. • A macro model is developed to describe the CAV dedicated lane setups and poly-information uncertainties • The performance of the system is optimized from the smoothness and stability of the traffic flow density wave • The impacts of poly-information uncertainties on the stability and energy consumption of traffic flow system • Trialability of dedicated lanes in a comparative comparison of real-world settings under specific circumstances [ABSTRACT FROM AUTHOR]

Older adults are more vulnerable to the negative impacts of infectious diseases than younger individuals. However, regardless of the importance and effectiveness of vaccines to reduce morbidity and mortality, issues remain with vaccine hesitancy among this population. Older adults' sources of immunization information and their level of trust in those sources may play a role in their vaccination behaviors. This research aimed to better understand the role of information sources and related issues of trust as related to vaccine uptake among older adults. A community-based, cross-sectional survey was conducted with 901 older adults in North Dakota in May-July 2022. Measures included extent of reliance on specific sources of immunization information, levels of trust, and uptake for influenza, pneumonia, shingles, and COVID-19 vaccinations. Immunization information sources were grouped into medical experts, informal, and public outlets. Results indicated older adults were more likely to rely on medical experts than informal sources or public outlets for immunization information. Greater reliance on medical experts was associated with a greater likelihood of vaccine uptake for all vaccines, while reliance on public outlets was associated with a greater likelihood of vaccine uptake only for COVID primary series and boosters. Reliance on informal sources for immunization information was associated with a reduced likelihood of vaccine uptake for all vaccines except shingles. Nearly half of respondents were uncertain who to trust for vaccine information. Uncertainty who to trust for immunization information significantly mediated the associations between reliance on medical experts and uptake for most vaccines indicating that trust in medical experts fosters vaccine uptake. Increasing reliance on medical experts as sources of immunization information is vital to increasing vaccine uptake among older adults. Additionally, this population must be assisted in increasing their ability to successfully assess the trustworthiness of immunization information sources. [ABSTRACT FROM AUTHOR]

Abstract: Real applications based on type-2 (T2) fuzzy sets are rare. The main reason is that the T2 fuzzy set theory requires massive computation and complex determination of secondary membership function. Thus most real-world applications are based on one simplified method, i.e. interval type-2 (IT2) fuzzy sets in which the secondary membership function is defined as interval sets. Consequently all computations in three-dimensional space are degenerated into calculations in two-dimensional plane, computing complexity is reduced greatly. However, ability on modeling information uncertainty is also reduced. In this paper, a novel methodology based on T2 fuzzy sets is proposed i.e. T2SDSA-FNN (Type-2 Self-Developing and Self-Adaptive Fuzzy Neural Networks). Our novelty is that (1) proposed system is based on T2 fuzzy sets, not IT2 ones; (2) it tackles one difficult problem in T2 fuzzy logic systems (FLS), i.e. massive computing time of inference so as not to be applicable to solve real world problem; and (3) membership grades on third dimensional space can be automatically determined from mining input data. The proposed method is validated in a real data set collected from Macao electric utility. Simulation and test results reveal that it has superior accuracy performance on electric forecasting problem than other techniques shown in existing literatures. [Copyright &y& Elsevier]

We suggest that dispersion in news sentiment, representing the dispersion in informative M&A performance forecasts, effectively captures M&A information uncertainty, disseminates information about transaction risks, and enhances investors' ability to discern high-risk acquisition attempts. We find that consistent with the information uncertainty perspective, dispersion in news sentiment is associated with higher change in the acquirer's information uncertainty, lower M&A announcement return, and lower M&A completion probability. Examining the underlying channel, we show that media reduces information asymmetry between managers and investors and managers listen to the market due to the loss of reputational capital. Our findings are robust to additional analysis, endogeneity test, and robustness check. Overall, we argue that media can function as an alternative channel for corporate governance through dispersion in news sentiment. [Display omitted] • Dispersion in news sentiment (DNS) increases the acquirer's information uncertainty. • DNS reduces M&A announcement return and M&A completion probability. • This negative link is strong for acquirers with low average in news sentiment and larger size. • Acquirer's political connection can suppress DNS when the M&A deal is covered by government-controlled media. • Acquirer's political connection can suppress DNS when the M&A deal is covered by local media. [ABSTRACT FROM AUTHOR]

• The deviations between measured information and true information are quantified by information uncertainty level. • A generic model is developed to describe the scenario where multiple information uncertainties exist. • The impacts of multiple information uncertainties on stability of connected automated vehicles flow are explored. • A novel car-following controller is developed to address the adverse effects of multiple information uncertainties. Connected automated vehicles have the capability to operate autonomously by monitoring real-time traffic information through on-board sensors, such as velocity and distance. However, no measurement can be perfect, and sensors are no exception, especially in challenging road and weather conditions, leading to the deviations between multiple information measured by vehicles and true information. Since the sizes of sensor detection errors are uncertain, we call this issue as multiple information uncertainties. This issue affects not only the normal operation of host vehicle directly but also the connected automated vehicular flow indirectly through wireless communication, resulting in the instability of car-following behavior and further deteriorating traffic congestion. So far, it is hard for us to obtain the repeatable, transferable, and even comparable results due to the lack of generic model. Therefore, this study develops a generalized model by using the uncertainty levels of multiple information to describe the dynamics of connected automated vehicles under the influence of sensor detection errors based on car-following theory. The theoretical and simulation-based investigations present a complete method to analyze the stability of traffic flow under multiple information uncertainties. Analytical results show that traffic stability will be reduced when the velocity measured by sensors is smaller than true velocity (i.e., negative uncertainty level of velocity information) or the headway monitored by sensors is bigger than real headway (i.e., positive uncertainty level of headway information), whereas the velocity and headway of equilibrium state will be enlarged. Otherwise, the opposite. These findings indicate that the impacts of multiple information uncertainties are double-edged swords, depending on the uncertainty levels of different information. To improve the adverse impacts of multiple information uncertainties on traffic stability, this study proposes a novel car-following controller and verifies its effectiveness. Overall, the present study provides a set of theoretical frameworks to investigate and improve traffic stability under multiple information uncertainties. All results contribute to enhancing the stability of traffic flow and further easing traffic congestion. [ABSTRACT FROM AUTHOR]

In group decision making (GDM), due to complexity of various factors, decision makers (DMs) often provide incomplete preference relations (PRs) in preference matrix. Interval-valued probabilistic uncertain linguistic term set (IVPULTS) is a flexible and accurate tool to depict evaluation information of experts. In this paper, we mainly propose a new concept pertaining to interval-valued probabilistic uncertain linguistic preference relation (IVPULPR) that applies the IVPULTS to preference relations. Firstly, some new basic theoretical concepts of IVPULTS are developed including ordered IVPULTS, normalization method and new expectation function. Secondly, we establish several goal programming models to estimate the unknown elements in incomplete IVPULPR and propose the expected additive consistency of IVPULPR. To improve the consistency level, two optimization models are constructed based on the idea of minimum adjustment. Thirdly, we derive the experts' weights in terms of information uncertainty, where a new method to measure information uncertainty of IVPULTS is proposed. For the sake of improving group consensus, we construct a group consensus index (GCI) and two optimization models depending on the adjustment mechanism of expert weight. Finally, a complete GDM framework with incomplete IVPULPR is devised based on the analysis of IVPULPR consistency and group consensus. Through an experiment analysis by using an UCI dataset, we find that the proposed GDM model can not only precisely express fuzzy preference information of DMs, but also ensure achievement of acceptable consistency and group consensus under the condition of not changing the initial preference as much as possible. [ABSTRACT FROM AUTHOR]

• There is a U-shaped relationship between the performance of carbon neutrality and firm value. • The U-shaped relationship is weaker in state-owned firms and firms operating in B2C industries. • The U-shaped relationship is stronger when information uncertainty is higher. Carbon neutrality is critical in addressing the climate crisis. Using the data of Chinese listed firms from 2018 to 2022, this paper shows a significant U-shaped association between the performance of carbon neutrality and firm value. Our key findings hold after a battery of robustness tests and endogeneity concerns. The U-shaped relationship is weaker in state-owned firms and those operating in B2C industries, and stronger when information uncertainty is higher. This paper has implications for the promotion of carbon-neutral practices in enterprises and the realization of sustainable development. [ABSTRACT FROM AUTHOR]

• The general membership functions of four types thresholds for the grades on attribute eigenvalue are proposed. • The trust information uncertainty degree in social network is researched. • A new method to generate the incomplete social network based on the trust information uncertainty degree and variable weight function is proposed. • A novel multi-objective decision optimization model for network security situation grade assessment under multi-source information is proposed. Grade assessment of network security situation is summarized as a typical multi-index grade assessment problem. However, the existed methods for grade assessment of network security situation do not consider multi-source information such as the trust information among experts, the preference information among companies and heterogeneous information of companies. The above problems are unable to be solved through traditional assessment methods. The aim of this paper is to establish a novel multi-objective decision model for the grade assessment of network security situation under multi-source information. On the basis of describing the grade assessment problem of network security situation, the membership functions of four-type thresholds for the grades on attribute eigenvalue are put forward. Two trust information uncertainty degrees in social network are defined, and the trust transfer operator based on trust information uncertainty degree and multi-path trust integration method based on the variable weight function is proposed. Afterwards, a new method to generate the incomplete social network is used to identify the weights of experts. Then, a multi-objective decision grade assessment optimization model is further established to obtain the network security situation grades and grade discrimination based on the two-tuple linguistic operator. The proposed method provides a theoretical basis for constructing and testing the grade assessment of network security situation. Meanwhile, it develops the grade assessment system of advanced network security situation and improves the ability to protect network security. [ABSTRACT FROM AUTHOR]

The rising regulatory and public pressure to align investments with sustainability causes an increasing need to analyze how investment institutions conduct and execute investment strategies related to their environmental, social, and corporate governance (ESG) funds. A thorough literature review shows that multiple criteria decision-making models are rarely used to discuss the relationships and weights of ESG fund indices. Furthermore, no ESG investment strategy evaluation framework has been created to deal with such funds. Therefore, a comprehensive framework is developed in this study to help select appropriate investment trust companies (ITCs). Z-numbers are incorporated into the proposed decision-making model in order to reflect information uncertainty and to assess the confidence of expert evaluations. First, a Z-based decision-making trial and evaluation laboratory approach is used to obtain the relationships and weights of indices to generate a causality diagram that allows decision-makers to quickly recognize the critical influencing factors in their evaluation systems. Then, a Z-based reference ideal method is applied to integrate the performance of the ITCs. The empirical application provides a demonstration of the usefulness of the proposed approach for selecting ITCs, which can aid to formulate appropriate strategies. • A comprehensive framework is proposed for selecting ITCs aligned with ESG factors for pension fund management. • Z-numbers are used to reflect information uncertainty and assess expert evaluations. • Four dimensions and 16 indices for evaluating ESG investment strategies are considered. • Key indices for selecting ITCs are identified. • The study can aid the development of ESG investment strategies in the public sector. [ABSTRACT FROM AUTHOR]

In this paper, a deep reinforcement learning-based energy optimization management method for hydrogen-electric coupling system is proposed for the conversion and utilization and joint optimization operation of hydrogen, wind and solar energy forms considering information uncertainty on the demand side of smart grid. Based on the wind energy, photovoltaic energy generation and load forecast information, the method uses deep Q network to simulate the energy management strategy set of the hydrogen-electric coupling system, and obtains the optimal strategy through reinforcement learning to finally realize the optimal operation of the hydrogen-electric coupling system based on the demand response. Firstly, based on the energy management model, a research framework and equipment model for integrated energy systems is established. On the basis of fundamental theories of reinforcement learning framework, Q-learning algorithm and DQN algorithm, the empirical replay mechanism and freezing parameter mechanism to improve the performance of DQN are analyzed, and the energy management and optimization of integrated energy system is completed with the objective of economy. By comparing the performance of DQN algorithms with different parameters in integrated energy system energy management, the simulation results demonstrate the improvement of algorithm performance after inheriting the set of strategies, and verify the feasibility and superiority of deep reinforcement learning compared to genetic algorithm in integrated energy system energy management applications. • A hydrogen-electric coupling regional comprehensive energy system was established. • Deep reinforcement learning algorithm is used to manage energy system. • Hydrogen energy as the main energy storage mode was investigated. • The hydrogen-electric coupling integrated energy system was optimized. [ABSTRACT FROM AUTHOR]

• We propose a novel Wavelet-based downsampling module (HWD) for CNNs. To the best of our knowledge, our method is the first attempt to explore feasibility by prohibiting (impeding) information loss in the downsampling stage of DCNNs for the semantic segmentation task. • We explore the measurement of information uncertainty across layers in CNNs, and propose a novel metric, named Feature Entropy Index (FEI), to evaluate the information uncertainty or feature importance between the downsampled feature maps and the prediction results. • The proposed HWD can be directly replaced the strided convolution or pooling layer without significant increase of computation overhead and be easily integrated into the current segmentation architectures. Comprehensive experiments demonstrate the effectiveness of the HWD module when comparing with seven state-of-the-art segmentation methods. Downsampling operations such as max pooling or strided convolution are ubiquitously utilized in Convolutional Neural Networks (CNNs) to aggregate local features, enlarge receptive field, and minimize computational overhead. However, for a semantic segmentation task, pooling features over the local neighbourhood may result in the loss of important spatial information, which is conducive for pixel-wise predictions. To address this issue, we introduce a simple yet effective pooling operation called the Haar Wavelet-based Downsampling (HWD) module. This module can be easily integrated into CNNs to enhance the performance of semantic segmentation models. The core idea of HWD is to apply Haar wavelet transform for reducing the spatial resolution of feature maps while preserving as much information as possible. Furthermore, to investigate the benefits of HWD, we propose a novel metric, named as feature entropy index (FEI), which measures the degree of information uncertainty after downsampling in CNNs. Specifically, the FEI can be used to indicate the ability of downsampling methods to preserve essential information in semantic segmentation. Our comprehensive experiments demonstrate that the proposed HWD module could (1) effectively improve the segmentation performance across different modality image datasets with various CNN architectures, and (2) efficiently reduce information uncertainty compared to the conventional downsampling methods. Our implementation are available at https://github.com/apple1986/HWD. [ABSTRACT FROM AUTHOR]

This paper incorporates information uncertainty and social interaction among investors into a random utility framework and develops a dynamic equilibrium model of asset pricing and investor choice. We show that strong social interaction can lead to endogenous switching between two persistent regimes for the mean choice fraction of investor population, which can simultaneously generate volatility clustering and time-series momentum in asset returns. By using StockTwits post volume as a proxy for social interaction, we provide empirical evidence for the model predictions for various equity indices. [ABSTRACT FROM AUTHOR]

• In light of modeling with uncertainty, a thorough review of oil condition monitoring is presented. • The modeling of the oil state is outlined in terms of spatial assessment and temporal prediction. • A comparison between traditional modeling and uncertainty modeling is conducted to identify the source of constraints. Lubricating oil plays a vital role as the information carrier for equipment tribological performance. Therefore, oil condition monitoring (OCM) serves as a crucial technology for assessing and predicting state degradation, providing the first-line defense against functional failure. However, limited by random fluctuation and insufficient knowledge, the modeling of OCM has suffered from uncertainty problems, leading to poor applicability and insufficient generalizability. The existing reviews are less elaborated on the information uncertainty for description, characterization, and treatment, which expresses the essence of modeling constraints. To bridge this gap, the paper provides a comprehensive review of the oil state modeling in terms of uncertainty. The existing methods and metrics are reviewed from the perspective of the spatial oil state assessment (OCA) and temporal remaining useful life (RUL) prediction modeling. Further, the existing methods are analyzed to solve the uncertainty problem, and then the solutions in oil state modeling considering uncertainty are discussed. Finally, targeting the challenges of the monitoring technology, the future trends of OCM are presented. [ABSTRACT FROM AUTHOR]

Safety evaluation of urban rail transit (URT) operation can not only have a great impact on passenger travel options, but is also related to planning of urban development. The evaluation results of URT operational safety are important for transit operators and passengers. In this study, we develop an integrated multi-stage evaluation framework to assess URT operation from the perspective of safety, which considers information uncertainty of key influence factors (KIFs), and risk preference of decision-makers (DMs). Firstly, to identify and select KIFs, this study makes systematic analysis of impacts on URT operational safety from five aspects: passengers, management, equipment, environment, and disaster respectively. Vensim software is used to build a stock flow model based on system dynamics method, and a two-level index system for assessing URT operational safety is established. Secondly, DMs' weights are specified based on interval valued triangular fuzzy (IVTF)-TOPSIS method, and IVTF-AHP-entropy is introduced to determine the combined weight of each indicator. Thirdly, an S-shaped utility function under IVTF environment is developed to get the ranking order of research objects, and operational safety levels are determined based on cloud model. Finally, Changsha subway network is selected as a case study to test our proposed evaluation framework, and some related suggestions for operators and managers have been put forward in the future. • Proposed a hybrid six-phase evaluation model. • Identify key influence factors (KIFs) of URT operational safety. • Take risk attitude of decision-makers (DMs) into consideration. • Verify our proposed method by the Changsha subway network. [ABSTRACT FROM AUTHOR]

Abstract We simulate the asset pricing in the framework of information networks when the number of agents is constant and tends to infinity. When the number of agents is a constant, we find that a higher risk aversion coefficient, a lower information uncertainty, or a higher standard variance of payoff volatility induces a lower asset price; a higher number of agents induces a higher aggregate demand. When the number of agents tends to infinity, we study and simulate the closed form expressions for asset price with risk aversion coefficient. We find that a higher network connectedness or a lower risk aversion coefficient induces a higher information driven volatility component and a lower Sharpe ratio; a higher network connectedness or a lower risk aversion coefficient induces a higher market efficiency. Liquidity driven volatility component, trading profit, price volatility are non-monotonic functions of network connectedness, or risk aversion coefficient. Highlights • We simulate the asset price in the framework of information networks. • A higher risk aversion coefficient, a lower information uncertainty, or a higher standard variance of payoff volatility induces a lower asset price. • A higher number of agents induces a higher aggregate demand. • A higher network connectedness or a lower risk aversion coefficient induces a higher information driven volatility component as well as a higher market efficiency and a lower Sharpe ratio. • Liquidity driven volatility component, trading profit, price volatility are non-monotonic functions of network connectedness or risk aversion coefficient. [ABSTRACT FROM AUTHOR]

Abstract Failure mode and effect analysis (FMEA) is a forward-looking risk-management technique used in various industries for promoting the reliability and safety of products, processes, structures, systems, and services. However, FMEA has many defects in practical experimentation. Therefore, this paper proposes a new model that uses multiple-criteria decision-making in combination with grey theory for FMEA. This approach has several advantages, such as being able to add the expected cost into the original risk priority number (RPN) to reflect the actual resource limitations, consider the different weights of severity, occurrence, detectability, and cost based on the best–worst method in RPN element calculation, and use the grey interval linguistic variables to manage information uncertainty. Furthermore, this study applied probability-based grey relational analysis to calculate the RPN, which preserves the information of prioritized failure modes through interval analysis. To demonstrate the usefulness and effectiveness of the proposed model, real data from an international electronics company were applied. The proposed model can provide an alternative risk priority solution for product development. Graphical abstract Highlights • This paper proposed a novel hybrid model for failure mode and effect analysis. • The expected cost was added in the original risk priority number (RPN) calculation. • We applied BWM in RPN element calculation and grey interval to manage information uncertainty. • The real data from an international electronics company were applied to demonstrate the model. [ABSTRACT FROM AUTHOR]

Each new generation of students brings with them new information beliefs and practices, with Generation Z being no different, evolving along with the world while finding ways to meet their information needs. Using previous research that had established a scale for information beliefs created by Brenda Dervin, we were able to approach the complexity of "information beliefs" with greater ease. In this study we've explored these information beliefs through distributing a Likert scale survey to all incoming freshmen students at a regional campus of a large state flagship university in 2017 and 2019. Through exploratory data analysis we were able to see that students did not consistently agree or disagree with Dervin's Ten Information Assumptions, but clearly have a common theme. These themes revolved around the value of informal sources and being overwhelmed by information which results in information uncertainty, leaving students still searching even when they feel they have found a source. This study helps to shed light on areas where librarians, in particular instructional librarians, can help this current generation of students grapple with the perceived availability of information and how authority is context dependent. • Generation Z's information assumptions range widely, both accurately and inaccurately. • They experience information overload which alters the types of information they use. • They also attempt to discover a perfect source even if one does not exist. [ABSTRACT FROM AUTHOR]

This paper argues fundamental information help resolve information uncertainty that leads to high idiosyncratic volatility premium. The IVOL-return relation is negative for stocks with poor fundamental strength but positive for stocks with strong fundamental strength. The arrival of fundamental news weakens the negative IVOL effect. Our findings are robust for alternative model specifications. Moreover, the negative IVOL effect dominates the positive IVOL effect due to arbitrage asymmetry that buying is easier than short selling stocks. Consistent with arbitrage asymmetry, the negative IVOL effect is stronger for stocks with low institutional ownership and following high investor sentiment. Overall, we provide a simple fundamental-based explanation for idiosyncratic volatility puzzle. [Display omitted] • Fundamental information help resolve information uncertainty. • The IVOL-return relation is negative for stocks with poor fundamental strength. • The arrival of fundamental news weakens the negative IVOL effect. • We provide a simple fundamental-based explanation for idiosyncratic volatility puzzle. [ABSTRACT FROM AUTHOR]

We extract the news component of short-selling activity by accounting for important cross-sectional, distributional differences in short interest data. The resulting measure of surprise in short interest negatively predicts the cross section of both U.S. and international equity returns. Our results also indicate that this predictability originates from short sellers' informed trading on mispricing and investors' underreaction due to their anchoring on past short interest. Finally, consistent with the notion of costly arbitrage, the return predictability is stronger among illiquid, volatile stocks and stocks with high information uncertainty, but importantly, unrelated to short-selling frictions. • We propose a new measure of investors' underreaction to informed short-selling activity. • Surprise in short interest (SUSIR) is motivated by investors' anchoring on historical long-term information. • SUSIR predicts the cross section of both U.S. and international equity returns. • Mispricing due to investors' underreaction to fundamental information is a more plausible explanation than risk. • The return predictability is stronger among illiquid, volatile stocks and stocks with high information uncertainty, but importantly, unrelated to short-selling frictions. [ABSTRACT FROM AUTHOR]