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We use clickstream data to show that investors' demand for information about macroeconomic factors affecting the path of future interest rates is a measure of their uncertainty about this path. In particular, an increase in information demand ahead of influential economic announcements affecting investors' beliefs about future interest rates predicts a stronger reaction of U.S. Treasury note yields to these announcements, as it should if information demand positively covaries with uncertainty. This relationship does not vanish after using standard measures of uncertainty as predictors, suggesting that clickstream data contain unique information about investors' uncertainty. [ABSTRACT FROM AUTHOR]

This study examines the effect of the Jumpstart Our Business Startups Act (JOBS Act) on information uncertainty in IPO firms. The JOBS Act creates a new category of issuer, the Emerging Growth Company (EGC), and exempts EGCs from several disclosures required for non-EGCs. Our findings are consistent with proprietary cost concerns motivating EGCs to eliminate some of the previously mandatory disclosures, which increases information uncertainty in the IPO market, attracts investors who rely more on private information, and leads EGCs to provide additional post-IPO disclosures to mitigate the increased information uncertainty. Our results also are consistent with agency explanations, whereby EGCs exploit the JOBS Act provisions to avoid compensation-related disclosures, which results in larger IPO underpricing for such firms. Overall, we provide evidence on how reduced mandatory disclosure affects the IPO market. [ABSTRACT FROM AUTHOR]

Instead of heuristical heterogeneity assumption in the current heterogeneous agent models (HAMs), we derive the trading heterogeneity by introducing information uncertainty about the fundamental value to a HAM. Conditional on their private information about the fundamental value, agents choose different trading strategies when optimizing their expected utilities. This provides a micro-foundation to heterogeneity and switching behavior of agents. We show that the HAM with trading heterogeneity originating from the incomplete information performs equally well, if not better than existing HAMs, in generating bubbles, crashes, and mean-reverting prices. The simulated time series matches with the S&P 500 in terms of power law distribution in returns, volatility clustering and long memory in volatility. [ABSTRACT FROM AUTHOR]

This paper demystifies the proposed analog circuit performance evaluation methods based on improved LSSVR (ILSSVR) by examining the arithmetic speed and the evaluation reliability online. The ILSSVR performance evaluation scheme has the robustness for the signal information uncertainty, which may be deduced by nonlinear feature, time varying feature and contain faults value about industrial field data information. More specially, the self-update via incremental and reduced interaction is employed to detect the interests both on history data information and the updated data information, and the features extraction nonlinear independent component analysis (NICA) is proposed, then the number of the feature data is controlled and desired time consumed is guaranteed. In addition, the multi-kernel and weighted idea have also been employed to interfuse quite flexibility to the bandwidths of kernel online. The proposed analog circuit performance evaluation scheme ILSSVR is evaluated for two filter circuit: leapfrog filter circuit and self-adapting filter circuit. And the effectiveness is illustrated through a numerical example. [ABSTRACT FROM AUTHOR]

We examine how information uncertainty surrounding IPO (initial public offering) firms influences earnings management and long-run stock performance. For low-information-uncertainty issuers, at-issue earnings' management is positively related to subsequent unmanaged earnings and has no relationship to market reaction to earnings announcement and long-run stock performance following the offering. For high-information-uncertainty issuers, however, at-issue earnings' management is unrelated to subsequent unmanaged earnings and negatively related to market reaction to earnings announcement and long-run stock performance following the offer. The evidence suggests that, on average, managers in low-information-uncertainty firms tend to engage in earnings' management for informative purposes, while managers in high-information-uncertainty firms engage in earnings' management for opportunistic purposes. [ABSTRACT FROM AUTHOR]

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]

We propose a shift from the current paradigm of electricity markets treating stochastic producers similarly to conventional ones in terms of their offers. We argue that the producers' offers should be probabilistic to reflect the limited predictability of renewable energy generation, while we should design market mechanisms to accommodate such offers. We argue that the transition from deterministic offers is a natural next step in electricity markets, by analytically proving our proposal's equivalence with a two-price conventional market. [ABSTRACT FROM PUBLISHER]

Results of modeling of the Raman spectrograms of polyester fibers are presented when drawing nanoparticles of gold on them. As a result of the carried-out modeling were allocated: initial ranges, background components and the Raman ranges without background components. For simplification of procedures of mathematical modeling it was chosen 7 informative resonant peaks of the received spectrograms. Further processing was carried out only on coordinates of resonant peaks. For detection of reliability of control of nanoparticles of gold on making components of the Raman ranges when using conditions of information uncertainty in work the method of an assessment of multidimensional reliability on joint probability of crossing of dispersions of normal distributions of intensivnost of the Raman spectrograms without nano-particles and with nanoparticles on fibers depending on wave numbers on all range of their changes is offered. Values of reliability of control on making components of the Raman spectrograms were as a result received and the most exact method for monitoring procedure of nanoparticles of gold on a surface of fibers is revealed. [ABSTRACT FROM AUTHOR]

This is the first study to examine the post-earnings-announcement drift anomaly in a Real Estate Investment Trust (REIT) context. The efficient markets hypothesis suggests that unexpected earnings should be fully incorporated into asset prices soon after being publicly announced. We hypothesize that publicly announced earnings signals may be more certain for REITs due to the presence of a parallel (private) asset market, suggesting less drift for REIT stocks. However, we find a large REIT drift component that is both statistically and economically significant. Furthermore, while the initial earnings surprise response is more muted for REITs, we find that the magnitude of the drift is significantly larger for REITs than for ordinary common stocks (NonREITs). Thus, information does not appear to move between the private and public asset markets in such a way as to render REIT earnings signals more certain than NonREIT earnings signals. [ABSTRACT FROM AUTHOR]

Shannon entropy is used to measure information uncertainty, while the information dimension is used to measure information complexity. Given two probability distributions, the difference can be measured by relative entropy. However, the existing relative entropy does not consider the effect of information dimension. To improve the existing entropy, a new relative entropy is presented in this paper. The information fractal dimension is considered in the proposed relative entropy. The new relative entropy is more generalized than the initial relative entropy. When dimension is not considered, it will degenerate to the initial relative entropy. Another interesting point is that the new relative entropy may have negative values when calculating. The physical meaning is still under exploration. Finally, some application examples are provided to exemplify the utilization of the proposed relative entropy. [ABSTRACT FROM AUTHOR]

Information uncertainty is inherent in many problems and is often subtle and complicated to understand. Although visualization is a powerful means for exploring and understanding information, information uncertainty visualization is ad hoc and not widespread. This paper identifies two main barriers to the uptake of information uncertainty visualization: first, the difficulty of modeling and propagating the uncertainty information and, second, the difficulty of mapping uncertainty to visual elements. To overcome these barriers, we extend the spreadsheet paradigm to encapsulate uncertainty details within cells. This creates an inherent awareness of the uncertainty associated with each variable. The spreadsheet can hide the uncertainty details, enabling the user to think simply in terms of variables. Furthermore, the system can aid with automated propagation of uncertainty information, since it is intrinsically aware of the uncertainty. The system also enables mapping the encapsulated uncertainty to visual elements via the formula language and a visualization sheet. Support for such low-level visual mapping provides flexibility to explore new techniques for information uncertainty visualization. [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]

The purpose is to use Shannon entropy measures to develop classification techniques and an index which estimates the separation of the groups in a finite mixture model. These measures can be applied to machine learning techniques such as discriminant analysis, cluster analysis, exploratory data analysis, etc. If we know the number of groups and we have training samples from each group (supervised learning) the index is used to measure the separation of the groups. Here some entropy measures are used to classify new individuals in one of these groups. If we are not sure about the number of groups (unsupervised learning), the index can be used to determine the optimal number of groups from an entropy (information/uncertainty) criterion. It can also be used to determine the best variables in order to separate the groups. In all the cases we assume that we have absolutely continuous random variables and we use the Shannon entropy based on the probability density function. Theoretical, parametric and non-parametric techniques are proposed to get approximations of these entropy measures in practice. An application to gene selection in a colon cancer discrimination study with a lot of variables is provided as well. [ABSTRACT FROM AUTHOR]

In this study, we use a series of computational models to investigate an information processing perspective on organizational control use. We evaluate and compare the information processing capabilities of various formal and informal control configurations under different information uncertainty conditions. We find that a wide range of formal controls can be used to direct subordinates performing interdependent tasks while a more narrow range of informal controls are most effective for directing subordinates who perform complex tasks. Results of this study provide a basis for formalizing an information processing perspective on organizational control implementation that differs but is complementary to the current emphasis on agency in organizational control research. [ABSTRACT FROM AUTHOR]

This paper presents the results from a minimum-effort game in which individuals can observe the choices of others in real time. We find that under perfect monitoring almost all groups coordinate at the payoff-dominant equilibrium. However, when individuals can only observe the actions of their immediate neighbors in a circle network, monitoring improves neither coordination nor efficiency relative to a baseline treatment without real-time monitoring. We argue that the inefficacy of imperfect monitoring is due to information uncertainty, that is, uncertainty about the correct interpretation of a neighbor's actions. Information uncertainty prevents individuals from inferring safely that their group has managed to coordinate from the available information. [ABSTRACT FROM AUTHOR]

In order to simulate the real world effectively, this paper proposes a multi-agent model that integrates a price negotiation support system based on the use of intelligent agents capable of processing information uncertainty. The certainty factor is integrated in expert systems to support the decision of agents. In the model, each agent is integrated with an expert system to deal with the uncertainty information. A real example of supply chains is chosen to show the validity of the proposed multi-agent model. Virtual companies can use the uncertainty information to support decisions. Multiple experiments are conducted to work on the coordination of the supply chain. The selling and purchasing prices in the supply chain are proposed in the experimental results. These prices are similar with the data in a real supply chain and are the optimal price strategy for the supply chain. The model was programmed using Jess and Swarm, which was run on Windows XP. The multi-agent model in the paper is beneficial to the reasoning of uncertainty information in multi-agent systems. [ABSTRACT FROM AUTHOR]

Analyzes the historical development of the uncertainty construct. Conceptualization of environmental uncertainty; Details of information uncertainty and resource dependence theory; Measures of environmental uncertainty; Role of external environment in management theory.

• 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]

We examine whether firms' information environment influences the market valuation of their hard‐to‐value knowledge assets: R&D capital. We find that a better information environment, as measured by greater analyst coverage, lower earnings forecast dispersion, and greater earnings forecasting accuracy, is associated with higher stock market valuation of firms' R&D capital. We conduct additional analyses to rule out alternative explanations and test for the direction of causality. Our causal evidence suggests that analysts significantly reduce the information uncertainty surrounding firms' R&D capital. [ABSTRACT FROM AUTHOR]

Portfolio management typically aims to achieve better returns per unit of risk by building efficient portfolios. The Markowitz framework is the classic approach used when decision-makers know the expected returns and covariance matrix of assets. However, the theory does not always apply when the time horizon of investments is short; the realized return and covariance of different assets are usually far from the expected values, and considering additional factors, such as diversification and information ambiguity, can lead to better portfolios. This study proposes models for constructing efficient portfolios using fuzzy parameters like entropy, return, variance, and entropy membership functions in multi-criteria optimization models. Our approach leverages aspects related to multi-criteria optimization and Shannon entropy to deal with diversification, and fuzzy and fuzzy entropy variants provide a better representation of the ambiguity of the information according to the investors' deadline. We compare 418 optimal portfolios for different objectives (return, variance, and entropy), using data from 2003 to 2023 of indexes from the USA, EU, China, and Japan. We use the Sharpe index as a decision variable, in addition to the multi-criteria decision analysis method TOPSIS. Our models provided high-efficiency portfolios, particularly those considering fuzzy entropy membership functions for return and variance. [ABSTRACT FROM AUTHOR]

The purpose of this research was to investigate the effects that user task load level has on the relationship between an individual's trust in and subsequent use of a system's automation. Military decision-makers trust and use information system automation to make many tactical judgments and decisions. In situations of information uncertainty (information warfare environments), decision-makers must remain aware of information reliability issues and temperate their use of system automation if necessary. An individual's task load may have an effect on his use of a system's automation in environments of information uncertainty. It was hypothesized that user task load will have a moderating effect on the positive relationship between system automation trust and use of system automation. Specifically, in situations of information uncertainty (low trust), high task load will have a negative effect on the relationship. To test this hypothesis, an experiment in a simulated command and control micro-world was conducted in which system automation trust and individual task load were manipulated. The findings from the experiment support the positive relationship between automation trust and automation use found in previous research and suggest that task load does have a negative effect on the positive relationship between automation trust and automation use. Experiment participant who incurred a higher task load exhibited an over-reliance on their automated information systems to assist them in their decision-making activities. Such an over-reliance can lead to vulnerabilities of deception and suggests the need for automated deception detection capabilities. [ABSTRACT FROM AUTHOR]

The expected claim frequency and the expected claim severity are used in predictive modelling for motor insurance claims. There are two category of claims were considered, namely, third party property damage (TPPD) and own damage (OD). Data sets from the year 2001 to 2003 are used to develop the predictive model. The main issues in modelling the motor insurance claims are related to the nature of insurance data, such as huge information, uncertainty, imprecise and incomplete information; and classical statistical techniques which cannot handle the extreme value in the insurance data. This paper proposes the back propagation neural network (BPNN) model as a tool to model the problem. A detailed explanation of how the BPNN model solves the issues is provided. [ABSTRACT FROM AUTHOR]

In this paper we propose an advanced Dempster-Shafer (D-S) Evidence Theory based Fuzzy Trust model (ETFT) for Peer-to-Peer (P2P) networks. The primary goal of ETFT is to be able to address trust information uncertainty and fuzzy trust inference to deal with inconsistent or conflicting recommendation problems in a reputation based P2P environment. The D-S theory is therefore introduced to our trust model. To make the D-S theory fit into P2P systems, we creatively revise the combination rules and achieve greatly improved results. To further improve the accuracy and performance, ETFT filters out noisy referrals before combining the evidences. From the theoretical analyses and experimental results, it is evident that the proposed ETFT has a clear advantage in modeling dynamic trust relationship and aggregating recommendation information. Results also demonstrate that ETFT is more robust and can generate higher successful transaction rate than most existing frameworks. [ABSTRACT FROM AUTHOR]

Decision making is thought to be guided by the values of alternative options and involve the accumulation of evidence to an internal bound. However, in natural behavior, evidence accumulation is an active process whereby subjects decide when and which sensory stimulus to sample. These sampling decisions are naturally served by attention and rapid eye movements (saccades), but little is known about how saccades are controlled to guide future actions. Here we review evidence that was discussed at a recent symposium, which suggests that information selection involves basal ganglia and cortical mechanisms and that, across different contexts, it is guided by two central factors: the gains in reward and gains in information (uncertainty reduction) associated with sensory cues. [ABSTRACT FROM AUTHOR]

Prestressed steel strands in prestressed structures offset or reduce the tensile stress caused by external loads, making them the primary load-bearing components. Great concerns have been raised about prestress monitoring due to the growing use of structural health monitoring (SHM). Piezoceramic (PZT) active sensing methods are commonly used in this field. However, there appears to be a problem of "energy saturation" in the utilization of piezoceramic active sensing methods. In this study, a smart anchor washer with semi-cylinders was developed to alleviate the saturation problem. An intelligent monitoring system is formed by combining the upper and lower annular cylinders with two piezoelectric patches. The piezoelectric patch on the upper annular cylinder is used as an actuator to emit signals through the contact interface of the smart anchor washer, which are then received by the piezoelectric patch on the lower annular cylinder. Based on wavelet packet decomposition, we investigate the correlation between the energy of the received signal and the applied tension force. Finally, a prestressing force index is developed for monitoring prestressing force using Shannon entropy. It is found that the index decreases with the increase in tension. The proposed design and index are also sensitive to early monitoring of prestressing force and can be used to monitor the entire prestressing process of steel strands. [ABSTRACT FROM AUTHOR]

One of the alternative versions of Shannon entropy is a measure of information which is called exponential entropy. Shannon and exponential entropies depend only on the event probabilities. These measures have also been extended to incorporate a set of weights associated with the events. Such weights may reflect some additional characteristics of the events such as their relative importance. In this paper, Axiomatic derivations and properties of weighted exponential entropy parallel to those achieved for weighted entropy are investigated. The relation between weighted exponential entropy of X and a strictly monotone and nonnegative function of X has obtained. The generalized weighted entropy and the generalized weighted exponential entropy for continuous random variable have been presented. [ABSTRACT FROM AUTHOR]

Wireless technologies are becoming an essential part of our daily life. These technologies are expected to provide a wide variety of real-time applications; hence, there is a vital need to provide quality-of-Service (QoS) support. One of the key mechanisms to support QoS is traffic regulation. The basic idea behind traffic regulation is to measure the network state (e.g., load) in order to adapt the rate of carefully selected application flows. In this paper, we propose a novel model, called FuzzyWMN, which can be used to implement traffic adaptation in Wireless Mesh Networks (WMNs).The objective of FuzzyWMN is to compute the rate adaptation to apply to application flows according to the current network state; it relies on two parameters to meet this objective: (1) packet delays between sources and destinations; and (2) buffer occupancy of network nodes. The proposed model combines the essential notions of both fuzzy logic theory and Petri nets; this enables FuzzyWMN to realize traffic adaptation in networks characterized by information uncertainty and imprecision due to the dynamic traffic behavior, channel interferences, etc. Extensive simulations show that FuzzyWMN achieves stable end-to-end delay and good throughput under different network conditions. [ABSTRACT FROM AUTHOR]

This study investigates whether financial analysts incorporate accounting conservatism into their earnings forecasts and whether it is more difficult for them to forecast earnings for less conservative firms, and then examines the impact of the findings on the return predictability of the value-to-price ( V/P) ratio. After controlling for the other factors affecting forecast accuracy, such as earnings predictability and information uncertainty, I find that analysts incorporate accounting conservatism into their earnings forecasts and that forecasting earnings is more difficult for less conservative firms. Consequently, the return predictability of the V/P ratio is stronger for more conservative firms, and previously reported return predictability of the V/P ratio is an average across firms with differing levels of conservatism. [ABSTRACT FROM AUTHOR]

We empirically test the association between conditional conservatism and cost of equity capital. Conditional conservatism imposes stronger verification requirements for the recognition of economic gains than economic losses, resulting in earnings that reflect losses faster than gains. This asymmetric reporting of gains and losses is predicted to lower firm cost of equity capital by increasing bad news reporting precision, thereby reducing information uncertainty (Guay and Verrecchia ) and the volatility of future stock prices (Suijs ). Using standard asset-pricing tests, we find a significant negative relation between conditional conservatism and excess average stock returns over the period 1975-2003. This evidence is corroborated by further tests on the association between conditional conservatism and measures of implied cost of capital derived from analysts' forecasts. [ABSTRACT FROM AUTHOR]

Abstract Cycling as a mode of transportation brings certain benefits to the user, such as improving health and saving money. It can provide a significant improvement in the quality of city life with minimal ecological impact. Thus, public bike-sharing systems have been adopted as a modal choice in many cities world-wide. However, there are some problems that must be addressed when designing a bike-sharing system, one of which is how to improve service quality and attract more users. This study proposes a hybrid model, combining the Decision-Making Trial and Evaluation Laboratory (DEMATAL) method with the Analytical Network Process (ANP) approach (called DANP), which addresses the dependent relationships between the various criteria for the quality of service for bike-sharing systems to better reflect the real-world situation. After building a complex evaluation system, a modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method is applied to explore the weighted-gap to the aspiration levels. Furthermore, given the vagaries of human judgments and ratings, preferences cannot be assigned an exact numerical value, which can make it difficult to evaluate the performance or make comparisons. One way to improve upon this uncertainty is to apply Grey Theory. The proposed method is then applied using data for Taipei's Youbike and Obike systems. The analytical results and the resultant managerial applications for improving the service quality of both bike-sharing systems are also discussed with particular attention to the current policies of Taipei City. Highlights • We propose a model to evaluate and improve the service quality of public bike-sharing systems. • The model addresses the dependent relationships among various criteria and information uncertainty. • Our results indicated the infrastructure is the causal factor and safety has the largest gap for improvement. • Results are discussed with regards to the current policies of Taipei City. [ABSTRACT FROM AUTHOR]

In this paper, we propose an uncertainty parameter weighted entropy-based fuzzy c-means clustering algorithm for noisy volumetric (3D) brain MR image segmentation using complemented global and spatially constraint local membership functions. Due to inherent noise and intensity inhomogeneity (IIH), the acquired MR images have blurry tissue boundaries. This leads to a situation where uncertainty arises while labeling a pixel/voxel into its proper tissue region. Further, it magnifies in the regions of different tissue boundaries. The proposed algorithm addresses this issue by introducing a class-level uncertainty parameter for each voxel and weightedly incorporating in the fuzzy objective function using complemented global and spatially constraint local fuzzy membership functions. It also incorporates total uncertainties in the 3D image domain by means of Shannon entropy. The complemented local fuzzy membership function estimates the degree of non-association, constraint by the local region-level intensity distribution. This framework allows the algorithm to utilize the spatial intensity distribution both in locally and globally within the image domain and produce more accurate cluster prototypes. We validate the proposed algorithm qualitatively and quantitatively with 10 3D simulated brain MR images, contaminated by noise and intensity inhomogeneity, 4 volumes of clinical real patient 3D brain MR images and a synthetic 3D image with added Rician noise. The simulation results in several evaluation indices indicate its superiority over state-of-the-art algorithms developed in recent past. • The algorithm uses complemented global and local fuzzy membership functions. • It introduces class-level uncertainty parameter for each voxel. • It estimates total information uncertainty in the form of entropy. • It generates the final membership function by utilizing two weighting parameters. • Results indicate it is superior to the other competitive methods. [ABSTRACT FROM AUTHOR]

In asset management for energy portfolios, quantitative methodologies are typically employed. In Brazil, the NEWAVE computational model is universally used to generate scenarios of hydraulic production and future prices, which result in revenue distributions. These distributions are then used to estimate the portfolio's revenue and assess its risk. Although this is a well-established analysis, it has some shortcomings that are not always considered. The validity of the revenue series constructed by NEWAVE, especially in long-term analysis, is a real problem for agents concerning the acquisition of assets such as power plants. Another issue is the disregard for other objectives that are important for the operationality of the management task and are often ignored, such as operational risk. To address these limitations, this work combines the areas of multicriteria decision making under uncertainty and risk management and presents a methodology for evaluating the acquisition of long-term energy assets, as well as a practical application of the proposed method. Investment alternatives are evaluated in multiple developed scenarios, so it is possible to measure how robust a given option is. By analyzing several scenarios simultaneously, a larger region of uncertainties can be covered, and therefore, decision making becomes more secure. The proposed methodology includes six objectives, designed to address a wider range of stakeholder needs. This approach is applied to an illustrative portfolio, producing results that allow for a more comprehensive understanding of decision attributes. Therefore, this work not only addresses the current limitations in the field but also adds an original contribution by considering simultaneously several scenarios and integrating multiple objectives in a robust and secure decision-making framework. [ABSTRACT FROM AUTHOR]

In this paper, under the symmetric entropy and the scale squared error loss functions, we consider the maximum likelihood (ML) estimation and Bayesian estimation of the Shannon entropy and Rényi entropy of the two-parameter inverse Weibull distribution. In the ML estimation, the dichotomy is used to solve the likelihood equation. In addition, the approximation confidence interval is given by the Delta method. Because the form of estimation results is more complex in the Bayesian estimation, the Lindley approximation method is used to achieve the numerical calculation. Finally, Monte Carlo simulations and a real dataset are used to illustrate the results derived. By comparing the mean square error between the estimated value and the real value, it can be found that the performance of ML estimation of Shannon entropy is better than that of Bayesian estimation, and there is no significant difference between the performance of ML estimation of Rényi entropy and that of Bayesian estimation. [ABSTRACT FROM AUTHOR]

Hick's law describes the time that individuals take to make a preference decision when presented with a set of possible choices. Basically speaking, the law states that decision time is a logarithmic function of the number of choices when the choices are equiprobable. However, the evidence examined here suggests that this, and a variant of the law for non-equiprobable choices based on Shannon entropy, are not effective at predicting decision reaction times involving structured sets of alternatives. The purpose of this report is to communicate a theoretical alternative to Hick's law that is derived from a mathematical law of invariance for conceptual behavior at the heart of Generalized Invariance Structure Theory (Vigo, 2013, 2015). We argue that such an alternative accounts more precisely for decision reaction times on structured sets. Furthermore, we argue that Hick's law is a special case of this more general law of choice reaction times for categories with zero degree of invariance. [ABSTRACT FROM AUTHOR]

The effect of In-doping on the quantum information entropy of the hydrogenic impurity states in the InxGa1-xN quantum dot has been studied for the first time. By exploring the Shannon entropy in the position space (Sr), in the momentum-space (Sp) and the Shannon entropy sum St, we find some novel and interesting phenomenon. First, although Sr increases and Sp decreases monotonically with the radius of InxGa1-xN quantum dot, the Shannon entropy sum St exhibits a series of peaks and valleys with the confinement radius. The physical origin of the minimum and maximum values in the Shannon entropy sum St is analysed in great detail. Second, the BBM entropy uncertainty principle ( S t ≥ 3 [ 1 + ln ⁡ (π) ]) holds for the hydrogenic impurity state in the semiconductor quantum dot, which can be used to replace the Heisenberg uncertainty principle in the quantum mechanics. Third, the content of the doping In element in the InxGa1-xN quantum dot has a great influence on the Shannon entropy of the hydrogenic impurity state. Since the Shannon entropy sum St is usually used to measure the position-momentum correlation, therefore, we can explore the uncertainty of position and momentum of the hydrogenic impurity states by changing the content of the doping element in the semiconductor quantum dots. This study has some practical applications in semiconductor material and quantum information measurement, and may guide future experimental researches for the localisation and delocalisation characteristics of hydrogenic impurities states in the semiconductor quantum dots. [ABSTRACT FROM AUTHOR]

Path planning is an essential tool for the robots that explore the surface of Mars or other celestial bodies such as dwarf planets, asteroids, or moons. These vehicles require expert and intelligent systems to adopt the best decisions in order to survive in a hostile environment. The planning module has to take into account multiple factors such as the obstacles, the slope of the terrain, the surface roughness, the type of ground (presence of sand), or the information uncertainty. This paper presents a path planning system for rovers based on an improved version of the Fast Marching (FM) method. Scalar and vectorial properties are considered when computing the potential field which is the basis of the proposed technique. Each position in the map of the environment has a cost value (potential) that is used to include different types of variables. The scalar properties can be introduced in a component of the cost function that can represent characteristics such as difficulty, slowness, viscosity, refraction index, or incertitude. The cost value can be computed in different ways depending on the information extracted from the surface and the sensor data of the rover. In this paper, the surface roughness, the slope of the terrain, and the changes in height have been chosen according to the available information. When the robot is navigating sandy terrain with a certain slope, there is a landslide that has to be considered and corrected in the path calculation. This landslide is similar to a lateral current or vector field in the direction of the negative gradient of the surface. Our technique is able to compensate this vector field by introducing the influence of this variable in the cost function. Because of this modification, the new method has been called Fast Marching (subjected to a) vector field (FMVF). Different experiments have been carried out in simulated and real maps to test the method performance. The proposed approach has been validated for multiple combinations of the cost function parameters. [ABSTRACT FROM AUTHOR]

Wildfires are destructive fires over the wildland that can wipe out large areas of vegetation and infrastructure. Such fires are hard to control and manage as they can change directions almost instantly, driven by changing environmental conditions. Effective response to such events requires the ability to monitor and predict the behavior of the fire as fast as they change. The WIFIRE project builds an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction, and visualization of wildfire behavior. One goal of WIFIRE is to provide the tools to predict a more accurate rate of a spreading wildfire. To this end, WIFIRE has developed interfaces for ingesting and visualizing high-density sensor networks to improve fire and weather predictions, and has created a data model for wildfire resources including sensed and archived data, sensors, satellites, cameras, modeling tools, workflows, and social information including Twitter feeds for wildfire research and response. This paper presents WIFIRE’s Firemap web platform to make these geospatial data and products accessible. Through a web browser, Firemap enables geospatial information visualization and a unified access to geospatial workflows using Kepler. Using GIS capabilities combined with scalable big data integration and processing, Firemap enables simple execution of the model with options for running ensembles by taking the information uncertainty into account. The results are easily viewable, sharable, repeatable, and can be animated as a time series. [ABSTRACT FROM AUTHOR]

With Chinese nuclear power restarting, supplier selection in quality-sensitive nuclear power industry has become increasingly urgent and necessary. However, the current research on supplier selection in nuclear power industry is rather few. Moreover, there is still one great problem in the present methods: the description of the information uncertainty is inadequate. This paper proposes an extended Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) under linguistic information to evaluate the uncertainty of potential supplier quantitatively and scientifically. The cloud model is used to handle imprecise numerical quantities, which can give consideration to both fuzziness and randomness of uncertain information. An empirical example of a nuclear power plant in China illustrates an application to supplier selection in nuclear power industry, which proves the effectiveness of the proposed method. Finally, a comparative analysis with fuzzy VIKOR and sensitivity analysis of results are presented to verify the correctness and robustness of the extended method respectively. [ABSTRACT FROM AUTHOR]

Multi-source information fusion is a sophisticated estimating technique that enables users to analyze more precisely complex situations by successfully merging key evidence in the vast, varied, and occasionally contradictory data obtained from various sources. Restricted by the data collection technology and incomplete data of information sources, it may lead to large uncertainty in the fusion process and affect the quality of fusion. Reducing uncertainty in the fusion process is one of the most important challenges for information fusion. In view of this, a multi-source information fusion method based on information sets (MSIF) is proposed in this paper. The information set is a new method for the representation of granularized information source values using the entropy framework in the possibilistic domain. First, four types of common membership functions are used to construct the possibilistic domain as the information gain function (or agent). Then, Shannon agent entropy and Shannon inverse agent entropy are defined, and their summation is used to evaluate the total uncertainty of the attribute values and agents. Finally, an MSIF algorithm is designed by infimum-measure approach. The experimental results show that the performance of Gaussian kernel function is good, which provides an effective method for fusing multi-source numerical data. [ABSTRACT FROM AUTHOR]

Dempster–Shafer theory has been widely used in many applications, especially in the measurement of information uncertainty. However, under the D-S theory, how to use the belief entropy to measure the uncertainty is still an open issue. In this paper, we list some significant properties. The main contribution of this paper is to propose a new entropy, for which some properties are discussed. Our new model has two components. The first is Nguyen entropy. The second component is the product of the cardinality of the frame of discernment (FOD) and Dubois entropy. In addition, under certain conditions, the new belief entropy can be transformed into Shannon entropy. Compared with the others, the new entropy considers the impact of FOD. Through some numerical examples and simulation, the proposed belief entropy is proven to be able to measure uncertainty accurately. [ABSTRACT FROM AUTHOR]

This paper dwells upon the problem of synthesising structures under indefiniteness. Herein is proposed a classification of initial information uncertainty types that influence the parameters of transport networks. It is shown that different types of uncertainties can be narrowed down to two types, internal and external. The article presents formal models of problems reflecting the said types of uncertainties and different ways of describing them by means of the interval analysis and fuzzy sets. Schemes for solving the problems posed are given based on the decomposition approach. [ABSTRACT FROM AUTHOR]

Abstract: Biodiesel, a clean-burning alternative fuel, can be produced using different types of feedstock and processing technologies. However, how to strategically plan for sustainable biodiesel manufacturing in large geographical region is very challenging, mainly because of technical/non-technical complexity, economic, various environmental and social concerns, and data and information uncertainty. In this paper, we introduce an interval-parameter-programming (IPP)-based strategic planning methodology. By this methodology, the sustainability performance of biodiesel manufacturing technologies and processes can be assessed systematically, and the IPP-based optimization can then generate optimal strategies for sustainable distributed biodiesel manufacturing under uncertainty. The efficacy of the introduced methodology is demonstrated by a sophisticated case study. [Copyright &y& Elsevier]

Abstract: Phase-Contrast (PC) MRI utilizes signal phase shifts resulting from moving spins to measure tissue motion and blood flow. Time-resolved 4D vector fields representing the motion or flow can be derived from the acquired PC MRI images. In cardiovascular PC MRI applications, visualization techniques such as vector glyphs, streamlines, and particle traces are commonly employed for depicting the blood flow. Whereas these techniques indeed provide useful diagnostic information, uncertainty due to noise in the PC-MRI measurements is ignored, which may lend the results a false sense of precision. In this work, the statistical properties of PC MRI flow measurements are investigated and a probabilistic flow tracking method based on sequential Monte Carlo sampling is devised to calculate flow uncertainty maps. The theoretical derivations are validated using simulated data and a number of real PC MRI data sets of the aorta and carotid arteries are used to demonstrate the flow uncertainty mapping technique. [Copyright &y& Elsevier]

Abstract: Sustainable transportation systems are the need of modern times. There has been an unexpected growth in the number of transportation activities over years and the trend is expected to continue in the coming years. This has obviously associated environmental costs like air pollution, noise, etc. which is degrading the quality of life in modern cities. To cope us this crisis, municipal administrations are investing in sustainable transportation systems that are not only efficient, robust and economical but also friendly towards environment. The challenge before the transportation decision makers is how to evaluate and select such sustainable transportation systems. In this paper, we present a multicriteria decision making approach for selecting sustainability transportation systems under partial or incomplete information (uncertainty). The proposed approach comprises of three steps. In step 1, we identify the criteria for sustainability assessment of transportation. In step 2, experts provide linguistic ratings to the potential alternatives against the selected criteria. Fuzzy TOPSIS is used to generate aggregate scores for sustainability assessment and selection of best alternative. In step 3, sensitivity analysis is performed to determine the influence of criteria weights on the decision making process. A numerical illustration is provided to demonstrate the applicability of the approach. The strength of the proposed work is its practical applicability and the ability to generate good quality solutions under uncertainty. [Copyright &y& Elsevier]

This paper studies bilateral multi-issue negotiation between self-interested autonomous agents. Now, there are a number of different procedures that can be used for this process; the three main ones being the package deal procedure in which all the issues are bundled and discussed together, the simultaneous procedure in which the issues are discussed simultaneously but independently of each other, and the sequential procedure in which the issues are discussed one after another. Since each of them yields a different outcome, a key problem is to decide which one to use in which circumstances. Specifically, we consider this question for a model in which the agents have time constraints (in the form of both deadlines and discount factors) and information uncertainty (in that the agents do not know the opponent's utility function). For this model, we consider issues that are both independent and those that are interdependent and determine equilibria for each case for each procedure. In so doing, we show that the package deal is in fact the optimal procedure for each party. We then go on to show that, although the package deal may be computationally more complex than the other two procedures, it generates Pareto optimal outcomes (unlike the other two), it has similar earliest and latest possible times of agreement to the simultaneous procedure (which is better than the sequential procedure), and that it (like the other two procedures) generates a unique outcome only under certain conditions (which we define). [ABSTRACT FROM AUTHOR]

The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level decisions also has its shortcomings. One problem that occurs is that deterministic mixed integer decision problems are often used for long-term planning, but the real production system faces a set of stochastic influences. Therefore, a planned utilisation factor has to be included into this deterministic aggregate planning problem. In practice, this decision is often based on past data and not consciously taken. In this paper, the effect of long-term forecast error on the optimal planned utilisation factor is evaluated for a production system facing stochastic demand and the benefit of exploiting this decision’s potential is discussed. Overall costs including capacity, backorder and inventory costs, are determined with simulation for different multi-stage and multi-item production system structures. The results show that the planned utilisation factor used in the aggregate planning problem has a high influence on optimal costs. Additionally, the negative effect of forecast errors is evaluated and discussed in detail for different production system environments. [ABSTRACT FROM AUTHOR]

It is widely held that better financial reporting makes investors more confident in their predictions of future cash flows and reduces their required risk premia. The logic is that more information leads necessarily to more certainty, and hence lower subjective estimates of firm 'beta' or covariance with other firms. This is misleading on both counts. Bayesian logic shows that the best available information can often leave decision makers less certain about future events. And for those cases where information indeed brings great certainty, conventional mean-variance asset-pricing models imply that more certain estimates of future cash payoffs can sometimes bring a higher cost of capital. This occurs when new or better information leads to sufficiently reduced expected firm payoffs. To properly understand the effect of signal quality on the cost of capital, it is essential to think of what that information says, rather than considering merely its 'precision,' or how strongly it says what it says. [ABSTRACT FROM AUTHOR]