Probabilistic Information Research Articles

Attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability

This study addresses the problem of attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability. Unlike traditional Markov/semi-Markov models that rely on transition probabilities, a novel switching rule is introduced that focuses on sojourn probability information associated with the target mode and sojourn time, which are easier to obtain than the well-known transition probabilities. Considering the vulnerability of stochastic switching systems to cyber-attacks, where the system state becomes unobservable and difficult to manage, a compensation-based output feedback controller framework is proposed. Using the Lyapunov method and stochastic analysis, sufficient conditions are provided to ensure system stability. Finally, the effectiveness and applicability of the developed approach are demonstrated using an F-404 aircraft engine system model.

Generalized load modeling approach considering multiple distributed generation integration

To solve the problem of node characteristic uncertainty caused by the connection of multiple distributed generations to the original load node, a new method for modeling the steady-state characteristics of generalized load nodes based on scene clustering was proposed. Scenario clustering takes into account three factors: wind farm output fluctuation, photovoltaic output fluctuation, and demand-side power load fluctuation, and extracts typical scenarios of generalized load data. According to the uncertainty change of node characteristics in different scenarios, the clustering data is segmented and refined, and its probability distribution is counted. Clustering information, probability information, and maximum value are used as three types of features of generalized load and are custom encoded as 12-dimensional feature vectors as neural network inputs. To improve the identification performance of generalized load features, the back propagation (BP) dichotomy neural network is used to build a unified model of node features. The simulation results show that the proposed method can not only model accurately but also introduce probability information through statistical data samples, which can provide an auxiliary reference for the prediction and regulation of generalized loads.

Selection of third-party logistics in supply chain finance under probabilistic complex hesitant fuzzy sets and distance measures

Financial institutions attach great importance to the multiple attribute decision-making (MADM) problems of selecting an effective third-party logistics (3PL) service provider in supply chain finance. A suitable 3PL service provider can not only assist financial institutions in carrying out supply chain finance activity but can also replace financial institutions in supervising the operation of a target financing supply chain, therefore minimizing financial institutions' operating risks. However, there is no sufficient study on the selection of a 3PL service provider. Hence, the objective of this paper is to establish an innovative concept of the “probabilistic complex hesitant fuzzy set” (PCHFS), which is a hybrid structure of the complex hesitant fuzzy set and the probabilistic fuzzy set to manage complex information in real-decision theory. This paper examines the periodicity of probabilistic fuzzy information and extends the range from [0,1] to the unit disc in the complex plane to provide more ability to describe the full meaning of the information in a mathematical model. Based on the internal structure of the set and to find the degree of discrimination between the pairs of PCHFSs, the generalized distance measures and modified generalized distance measures are defined. Several properties and their relationships between them are derived in detail. Also, several cases of the proposed measures are exposed, which reduces them to the existing studies. Furthermore, based on these proposed measures, a multiple-attribute decision-making (MADM) approach is established in an uncertain environment, and several numerical examples are given to examine the feasibility and validity of the explored measures. Finally, the modified and parameterized distance measures based on PCHFSs are verified by comparing them with some existing measures.

Digital twin Bayesian entropy framework for corrosion fatigue life prediction and calibration of bridge suspender

This paper proposes an intelligent digital twin framework for corrosion fatigue life prediction and calibration of suspender wires integrated with mechanism-driven, sensor-driven, and information fusion. A general probabilistic information fusion strategy is constructed to handle entropy-based external constraints and classical Bayesian updating. Statistical moment, range bound, and point data are considered to investigate the effect of various types and sequences of information. A small-time domain fatigue crack growth model is proposed to overcome the limitations of traditional cycle-based methods, which can capture the large and small cycles of random fatigue stress. The virtual sensor-based stress time-history response is obtained under different traffic flow densities through digital twin finite element model of a suspension bridge. The results show that with and without considering interval bound leads to different fatigue life prediction results, especially for statistical moment data fusion, and the maximum difference is approximately 54%. The average prediction life of suspender wires is gradually close to the actual service life as crack observations increase. The standard deviations of the corrosion fatigue life decrease by 88%, when simultaneously integrating moment, interval, and point data.

Temperature history reconstruction in steel box girders using limited data and proper orthogonal decomposition-based dimension reduction representation

The monitoring temperature history of steel box girders inevitably contains gaps or anomalies due to the malfunctions of the structural health monitoring system. Traditional methods for reconstructing temperature histories face the challenge in accounting for the randomness of temperature variations caused by the uncertainty of complex environmental factors. This research proposes a framework for reconstructing the long-term temperature of steel box girders by combining the limited measurements with a proper orthogonal decomposition (POD) based dimension reduction representation approach, to account for the stochastic nature of daily temperature variations. Unlike the conventional Monte Carlo-based POD method, the developed POD-based dimension reduction representation approach effectively reduces the number of elementary random variables from thousands to two by introducing random functions serving as constraints, overcoming the challenge of high-dimensional random variables inherent in the Monte Carlo methods. The proposed approach divides the measured temperature histories into random high-frequency (HF) and deterministic low-frequency (LF) components and establishes the theoretical models of power spectral density and coherence functions of HF temperature components to accommodate the generation of HF temperature component samples, and finally reconstructs temperature samples by superimposing the LF components and generated HF component samples. The results from a practical example demonstrate that the statistical characteristics of representative HF temperature component samples generated by the POD-based dimension reduction representation align well with the corresponding targeted values, and the proposed method outperforms the traditional POD method, yielding a 60% efficiency enhancement without compromising computational accuracy. The developed framework owns apparent superiority in accuracy compared to the traditional POD and the long short-term memory methods, particularly in continuous and extensive missing data. Moreover, the reconstructed temperature samples with assigned probabilities present complete probability information from the level of total probability. These results advance the probabilistic methods in tackling long-term temperature history reconstruction.

Relating in the Wild: Toward an Analysis of Equivalence Relations Under More Naturalistic Conditions.

Since the first proposal 50years ago, numerous experiments have documented how arbitrarily related stimuli can become functionally interchangeable. These studies have sought to understand how different variables can moderate the probability of equivalence class formation. However, the well-established evidence regarding this phenomenon in experimental settings does not necessarily guarantee an understanding about how equivalence relations are produced in natural settings. In typical experiments, experimenters control critical variables to produce equivalence relations, such as, the requirement of proficiency with baseline relations, the number of opportunities to relate two or more stimuli, the efforts to promote stimulus control topography coherent with the experimenter-defined relations, etc. All these variables, however, are not controlled in our daily lives. The present article elucidates how some differences between experimental and natural settings can likely affect how the phenomenon of equivalence relations can occur in noncontrolled, naturalistic environments. Furthermore, we suggest new areas of research to promote the generalization of basic experimental data to contingencies in our daily lives.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5–9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5–3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Singularity properties of the entropy in an enclosed system characterized by accumulated noise stochastic processes

Due to the irreversibility of processes in an enclosed system, the randomness in the system increases in time and can be represented by an accumulated noise stochastic process. In contrast to the thermodynamics theory, the analysis of the system is based on the information theory point of view. The system is defined by two states: a time state and a timeless state. Based on the central limit theorem, due to the additivity of the noise process, the time state is characterized by the time-dependent Gaussian stochastic process. In contrast to Shannon's theory, precise expressions for the probability density, information, and entropy functions of the random variables defining the time-dependent process are derived and analyzed for a finite, infinite, and zero value of the related time-dependent variance. It has been proven that the entropy rate is not necessarily inversely proportional to time, as presented in previous works. Furthermore, mathematical proofs are presented, showing that the system entropy is a singularity function that increases towards infinity in the time state and then drops to zero value when the system enters the timeless state. The timeless state is characterized by the undefined pdf function, infinite values of information, and zero entropy.

Accelerated discovery and formation mechanism of high-entropy carbide ceramics using machine learning based on low-cost descriptors

The vast compositional space of High-entropy carbide ceramics (HECCs) renders traditional experimental trial-and-error and computational simulations inadequate for rapid traversal and screening. In this work, leveraging retrievable and low-cost physicochemical parameters as feature inputs, we have successfully established an excellent predictive machine model (precision = 0.938, recall = 0.994, F1 score = 0.953) for HECCs, which is comparable to excellent model currently reported that was trained on density functional theory data, utilizing machine learning techniques. Further, we have identified compositional threshold conditions for the formation of (TiZrHf)C-based HECCs. Compared against literature data and experimental results, the constructed model is capable of reliably predicting the formation probabilities and phase diagrams for 5–9 component (TiZrHf)C-based HECCs with both equiatomic and non-equiatomic ratios those most have not been reported so far. Moreover, it has been discovered that the inability of equiatomic ratio formulations to form (TiZrHf)C-based HECCs does not necessarily imply that formulations across a global scope are incapable of forming (TiZrHf)C-based HECCs. Our developed model presents a highly efficient and low-cost technological approach for the swift screening of HECC phases.

Formation of human kinship structures depending on population size and cultural mutation rate

How does social complexity depend on population size and cultural transmission? Kinship structures in traditional societies provide a fundamental illustration, where cultural rules between clans determine people's marriage possibilities. Here, we propose a simple model of kinship interactions that considers kin and in-law cooperation and sexual rivalry. In this model, multiple societies compete. Societies consist of multiple families with different cultural traits and mating preferences. These values determine interactions and hence the growth rate of families and are transmitted to offspring with mutations. Through a multilevel evolutionary simulation, family traits and preferences are grouped into multiple clans with interclan mating preferences. It illustrates the emergence of kinship structures as the spontaneous formation of interdependent cultural associations. Emergent kinship structures are characterized by the cycle length of marriage exchange and the number of cycles in society. We numerically and analytically clarify their parameter dependence. The relative importance of cooperation versus rivalry determines whether attraction or repulsion exists between families. Different structures evolve as locally stable attractors. The probabilities of formation and collapse of complex structures depend on the number of families and the mutation rate, showing characteristic scaling relationships. It is now possible to explore macroscopic kinship structures based on microscopic interactions, together with their environmental dependence and the historical causality of their evolution. We propose the basic causal mechanism of the formation of typical human social structures by referring to ethnographic observations and concepts from statistical physics and multilevel evolution. Such interdisciplinary collaboration will unveil universal features in human societies.

A Sampled-Data-Based Secure Control Approach for Networked Control Systems Under Random DoS Attacks.

This article aims to analyze H∞ stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

Compound nucleus formation probability of heavy and superheavy nuclei synthesized using heavy ion fusion reactions

The role of entrance channel parameters such as Z2/A, charge asymmetry αz, mass-asymmetry (ηA), charge product (Z1Z2), mean fissility χm, Coulomb interaction parameter and (N−Z)/(N+Z) on compound nucleus formation of actinide nuclei using heavy ion fusion reactions were investigated. For the formation of compound nuclei, the considered atomic number range of the projectile varies between 5≤Z≤14 and the mass number lies between 10≤A≤34. Similarly, the studied target atomic number varies between 78≤Z≤92 and the mass number range is 197≤A≤238. Among these entrance channel parameters, PCN is more systematic for (N−Z)(N+Z), Z2/A and αz. In addition to entrance channel parameters, the Ecm and EBass also play a significant role in the prediction of PCN. The proposed empirical formulae are applicable to the compound nuclei from Fr to Sg. These findings are significant for the PCN prediction from Fr to Sg.

Social network group decision-making method based on stochastic multi-criteria acceptability analysis for probabilistic linguistic term sets

In social network group decision-making (SNGDM) problems, decision-makers (DMs) often express their opinions or preferences using probabilistic linguistic term sets (PLTSs). In this paper, a novel SNGDM method for probabilistic linguistic information is proposed. Firstly, to obtain the prioritization of DMs in the clustering process, a DM clustering method for SNGDM is developed considering the influence of trust relationships and opinions similarity among DMs. Then, to satisfy the requirements of the consensus reaching process in SNGDM, a dynamic consensus threshold calculation method based on an optimization model is introduced. Furthermore, in the consensus measure stage, a novel consensus measure method for both DMs and subgroups is proposed, using a stochastic multi-criteria acceptability analysis (SMAA) method. Based on the consensus measure method, a novel SNGDM method based on SMAA for PLTSs is proposed. Finally, a case study of service quality evaluation in institutional pensions is used to illustrate the effectiveness of the proposed method. The results of case show that the proposed method can adjust consensus thresholds dynamically based on each round of collective opinions, and can solve the SNGDM problems when DMs can not provide their preferences for criteria weights.

A dual-topological graph memory network for anti-noise multivariate time series forecasting

Multivariate time series (MTS) forecasting plays an essential role in the automation and optimization process of intelligent applications. However, capturing correlations and dependencies among variables in MTS data remains a major challenge for the forecasting models. Although existing methods explain these complex relationships by deeply analyzing variables and intervariable dependencies in MTS data, they tend to be affected by the noise interference prevalent in real-world data, which increases the difficulty for forecasting models to characterize the real features, and thus makes it difficult to achieve satisfactory forecasting results. To address these challenges, this paper designs a dual-topological graph memory (DualGM) network for anti-noise multivariate time series forecasting. The core idea is to analyze the complex relationships in MTS data in detail by constructing dual-topological graphs and to learn and retain the comprehensive information of sequence features by employing a bidirectional self-attention memory module. Specifically, the model first constructs the MTS data as a graph structure and creates a probability topological graph and a distance topological graph based on the similarity between the samples, which helps the model recognize and filter the noise more efficiently in noisy environments, and valuable information is extracted from the noisy data. Then, the topological neighborhood features of the MTS data in each layer are summarized via multilayer graph groups, and the probability and distance spatial information is fused using a bidirectional self-attention mechanism to further enhance the memory and retention of the topological features, thus improving the accuracy of the forecasting model. The experimental results on four real datasets demonstrate that the DualGM model outperforms previously reported methods under various settings; in particular, the RSE values of the DualGM model are higher than those of the second-best results by 12%, 37%, 0.8% and 16% on the four datasets. Moreover, the DualGM model provides a valuable solution for multivariate time series forecasting under noise interference.

Investigation of decay mechanisms and associated aspects of exotic Nobeliumisotopes using the Skyrme energy density formalism

Abstract Persistent theoretical and experimental attempts have been made to investigate the
heavy ion induced reactions and their subsequent decay mechanisms in superheavy mass region.
In addition, the region of transfermium elements is itself of great interest because of the neutron
/ proton shell effects. Here, we aim to study the subsequent decay mechanisms of two isotopes of
Z = 102 nucleus, i.e. 248No and 250No.
The dynamical cluster-decay model (DCM) based on Quantum Mechanical Fragmen-
tation Theory (QMFT), is employed to conduct a comprehensive analysis of compound nucleus
(CN) and non-compound nucleus (nCN) mechanisms such as fusion-fission (ff), Quasi fission (QF)
and fast fission (FF), the role of centre of mass energy (Ec.m.) and angular momentum (ℓ) for
248No and 250No isotopes. The nuclear interaction potential is obtained using the Skyrme energy
density formalism (SEDF) in the domain of GSkI force parameters. The probability of compound nucleus formation
(PCN ) is determined using a function that depends on the centre of mass energy. The lifetimes for
the fusion-fission (ff) quasi fission (QF) channels are explored.
Here, CN and nCN decay mechanisms for two isotopes of Z = 102 nobelium are studied
over the wide range of centre-of-mass (Ec.m.) by including the quadrupole deformation (β2) and
optimum orientations (θopt.) of decaying fragments. The fragmentation potential, preformation
probability, neck length parameter and reaction cross-sections are explored. Further, the calcula-
tions are done for PCN in order to identify the decay modes of 248No and 250No isotopes. The
fusion-fission lifetimes and quasi fission lifetimes are compared with the dinuclear system (DNS)
approach. The most probable fragments such as 122Sn and 128T e are observed near to the magic shell closure Z = 50 and
N = 82. The ff and qf lifetime decreases with increase in the excitation energy.

Threshold displacement in the tungsten-carbon system

ABSTRACT Threshold displacement energies (Ed) of carbon and tungsten in tungsten carbide (WC), W2C, tungsten and diamond are predicted using molecular dynamics. The spatial dependence of Ed is probed by considering a geodesic projection of a symmetrically distinct arc of crystallographic directions for each lattice site. Further, the definition of threshold displacement is explored by making the distinction between atomic displacement ( E ¯ d disp ) and defect formation ( E ¯ d def ). Predicted values of E ¯ d def compare favourably to experimental observations for tungsten and tungsten carbide. Results confirm that E ¯ d def and E ¯ d disp are strongly structure dependent. Differences between E ¯ d disp and E ¯ d def are commensurate with rapid defect recombination within the timeframe of the simulations for some species and structures but not universally. The probability of displacement and defect formation as a function of primary knock-on energy is also reported. Previously developed models for the average displacement of the primary knock-on atom based on kinetic energy and momentum-dependent drag terms are generally found to provide a useful level of approximation. Anisotropy is investigated and results highlight differences due to structures.

Communication of project risk assessment information through visuals

Risk assessment is aimed at providing necessary risk-related information to decision-makers for formulating risk mitigation strategies. Poor communication of its context and outputs may lead to misperceptions and wrong judgements. In this research, the role of visualization as an enabler of better communication of risk assessment information is explored using a design science approach. A questionnaire was carried out with project management professionals using the risk report of a case project to understand challenges in interpretation of risk assessment outputs, information and visualization requirements for making better sense of project risk. Based on the findings from the questionnaire, a visual representation, the Risk Box was developed and further tested by semi-structured interviews. Findings reveal that visuals are not only needed for better understanding of probabilistic information but also the context of risk assessment such as relations between individual risks, risk-prone variables, assumptions, scenarios and mitigation strategies.

Molecular insights into the impact of mineral pore size on methane hydrate formation

Natural gas hydrates are not only substantial energy sources but also have significant applications in the chemical industry and other fields. Although investigating hydrate formation in sediment minerals is crucial for their development and utilization, the underlying hydrate formation mechanism remains unclear. Here, molecular simulations were conducted in systems incorporating hydrophobic and hydrophilic pores of different sizes to investigate methane hydrate formation processes. The findings suggest that, as the hydrophobic slit size increases, there is a larger number of dissolved methane after the system reaches a metastable equilibrium state. The probability of cage formation indicates that hydrate cages readily form on hydrophobic surfaces or in the solution phase near the solution/gas interface. The larger slits are preferred for hydrate nucleation, regardless of whether the surface is hydrophobic, with most initial nuclei located near the liquid/methane interface. However, the interface perturbation can lead to the movement and growth of hydrate nuclei near the solution/methane interface into the bulk solution phase. Additionally, hydrate can nucleate and grow on the hydrophobic surface, facilitated by the adsorbed methane molecules and nonstandard cages. Pores hinder methane storage capacity in the hydrate phase due to the confinement effect and the amorphous nature of the hydrate formed. These molecular-level findings enhance our understanding of hydrate formation in sedimentary environments and porous materials, benefiting the development of natural gas hydrates and the use of porous materials for gas storage and transportation.

The continuous-time magnetic quantum walk and its probability invariance on a class of graphs

Let [Formula: see text] be a nonnegative integer and [Formula: see text] the power set of the set [Formula: see text]. Then there is an adjacency relation in [Formula: see text] such that [Formula: see text] together with the relation forms a regular graph. In this paper, we propose a model of continuous-time magnetic quantum walk (MQW) on the graph [Formula: see text], and investigate its properties from a viewpoint of probability and quantum information. We first introduce a magnetic Laplacian [Formula: see text] on the graph [Formula: see text] and examine its spectrum. And then, with [Formula: see text] as the Hamiltonian, we construct our model of continuous-time MQW on the graph [Formula: see text]. We find that the model has probability distributions that are completely independent of the magnetic potential at all times. And we show that it has perfect state transfer at time [Formula: see text] when the magnetic potential satisfies some mild conditions. Some other interesting results are also obtained.

A global-scale applicable framework of landslide dam formation susceptibility

The formation and failure of landslide dams is an important and understudied, multi-hazard topic. A framework of landslide dam formation susceptibility evaluation was designed for large-scale studies to avoid the traditional dependence on landslide volume calculations based on empirical relationships, which requires comprehensive local inventories of landslides and landslide dams. The framework combines logistic regression landslide susceptibility models and global fluvial datasets and was tested in Italy and Japan based on landslide and landslide dam inventories collected globally. The final landslide dam formation susceptibility index identifies which river reach is most prone to landslide dam formation, based on the river width and the landslide susceptibility in the adjacent delineated slope drainage areas. The logistic regression models showed good performances with area under the receiver operating characteristics curve values of 0.89 in Italy and 0.74 in Japan. The index effectively identifies the probability of landslide dam formation for specific river reaches, as demonstrated by the higher index values for river reaches with past landslide dam records. The framework is designed to be applied globally or for other large-scale study regions, especially for less studied data-scarce regions. It also provides a preliminary evaluation result for smaller catchments and has the potential to be applied at a more detailed scale with local datasets.