Stochastic Model Research Articles

Entropy production and thermodynamic inference for stochastic microswimmers

The question of characterization of the degree of nonequilibrium activity in active matter systems is studied in the context of a stochastic microswimmer model driven by a chemical cycle. The resulting dynamical properties and entropy production rate unravel a complex interplay between the chemical and the hydrodynamic degrees of freedom beyond linear response, which is not captured by conventional phenomenological approaches. By studying the precision-dissipation trade off, a new protocol is proposed in which microscopic chemical driving forces can be inferred experimentally. Our findings highlight subtleties associated with the stochastic thermodynamics of autonomous microswimmers. Published by the American Physical Society 2024

A Kriging Surrogate Model for Ball Grid Array Electronic Packaging With Stochastic Material and Geometrical Parameters

Abstract Ball grid arrays (BGAs) offer significant advantages in the automotive industry, such as their small size and high integration density, making them a promising electronic packaging approach. However, the operating environment of automobiles is more complex compared to other applications, primarily due to vibrations generated by power engines and oscillations caused by pavement roughness. The resonant frequencies of electronic packaging structures play a crucial role in system reliability and safety. However, accurately describing the implicit relationship between system resonant frequencies and material and geometrical parameters can be challenging. A Kriging surrogate model (KSM) is proposed by the combination of the Latin Hypercube stochastic sampling with finite element computation. Four different BGA configurations are established with either the initial values in the deterministic model or the specified sampling interval ranges in the stochastic model. The results of the finite element model (FEM) for BGA electronic packaging are validated and demonstrate qualitative agreement with published literature. The impacts of material and geometrical parameters on the resonant frequencies are investigated and compared. The mean, maximum, minimum, and variance are recorded based on a large database of stochastic samples. The feasibility of KSM for the resonant frequency prediction of BGA is confirmed by its satisfactory accuracy and computational efficiency.

Global insights into seed dispersal mutualism with cooperative efforts and environmental variability

Seed dispersal mutualisms assume a crucial role in sustaining overall ecosystem functionality. This study extends the foundational framework of seed dispersal mutualism by incorporating animal cooperation and environmental variability. Through the integration of stability theory and phase analysis, we investigate the global dynamics of seed dispersal mutualism in deterministic environments. Theoretical findings reveal that the deterministic system exclusively exhibits equilibrium dynamics, ruling out periodic solutions and chaotic phenomena. Notably, in cases of multiple coexisting equilibria, adjacent equilibria display distinct stability properties. Furthermore, employing stochastic analysis and Lyapunov exponents, we ascertain the uniqueness of global positive solutions in the stochastic model, along with stochastic ultimate boundedness, and precise thresholds for the stochastic persistence and extinction. These results illuminate the theoretical mechanisms governing transitions in seed dispersal mutualism across various stable population levels and underscore the insights into how essential biological factors, including animal cooperation efforts and environmental variability, impact the long-term dynamics of seed dispersal mutualism.

The cycle of violence: effects of violence experience, behavior, and attitudes on adolescents’ peer rejection networks

Previous research on adolescent peer networks has mainly focused on friendship networks and their association with violence, but very limited research is available on peer rejection networks. This lack of knowledge hinders the effectiveness of preventing peer rejection and its negative effects. Based on the theory of the cycle of violence, the present study examines the extent to which parental physical abuse experiences, aggressive behavior toward peers and acceptance of violence are related to peer rejection networks at school. Social network analysis with a stochastic actor-oriented model of longitudinal data collected from high school students (Wave 1, n = 620; Wave 2, n = 590) confirms that adolescents who frequently use aggression toward their peers are more likely to be rejected, especially if those adolescents have experienced abuse. Similarly, peers are more likely to reject adolescents with high levels of accepting violence. The results also show that aggression toward peers generally tends to decrease over time but not for adolescents who reject a larger number of students. For students who reject many peers, aggression frequency increases. From the perspective of resilience theory, peer rejection, when combined with aggression toward peers and acceptance of violence, particularly in female adolescents, creates a significant risk factor for socio-emotional development. Therefore, tackling violence attitudes, experiences and behavior in the school environment and at home is crucial in overcoming the cycle of violence.

Ethical Considerations in AI Simulations for Designing Assistive Technologies

Current ethical debates on the use of artificial intelligence (AI) in healthcare approach AI technology in three primary ways. First, they assess the risks and potential benefits of current AI-enabled products using ethical checklists. Second, they propose ex ante lists of ethical values relevant to the design and development of assistive technologies. Third, they advocate for incorporating moral reasoning into AI's automation processes. These three perspectives dominate the discourse, as evidenced by a brief literature summary. We propose a fourth approach: viewing AI as a methodological tool to aid ethical reflection. This involves an AI simulation concept informed by three elements: 1) stochastic human behavior models based on behavioral data for simulating realistic scenarios, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components to illustrate the impact of variable changes. This approach aims to inform an interdisciplinary field about anticipated ethical challenges or trade-offs in specific settings, prompting a re-evaluation of design and implementation plans. This is particularly useful for applications involving complex values and behaviors or limited communication resources, such as dementia care or care for individuals with cognitive impairments. While simulation does not replace ethical reflection, it allows for detailed, context-sensitive analysis during the design process and before implementation.Finally, we discuss the quantitative analysis methods enabled by stochastic simulations and the potential for these simulations to enhance traditional thought experiments and future-oriented technology assessments.

Simulation Study on Student Residential Energy Use Behaviors: A Case Study of University Dormitories in Sichuan, China

Studying the energy use behavior of occupants is crucial for accurately predicting building energy consumption. However, few studies have considered the impact of occupant behaviors on energy consumption in university dormitories. The objective of this study is to establish an agent-based model of energy consumption for university dormitories based on energy use behavior. The dormitories of a typical university in Sichuan, China, were subdivided into three clusters using a two-step cluster analysis. Subsequently, the energy use behaviors of occupants in each type of dormitory were characterized to establish a stochastic energy use behavior model. On the basis of the above, NETLOGO was used to construct an agent-based model for dormitories’ energy consumption to dynamically simulate energy use behavior. The accuracy of the model was verified by comparing the simulated values with the measured data. Finally, a building-energy-friendly retrofit scheme was proposed, and it was found that the optimized dormitory reduced energy consumption by 16.07%. Therefore, the results can provide information support for energy-saving decisions during the early design and retrofit phases of buildings. With the popularity of centralized supply, the research methodology may provide an extensive reference for energy management policies and sustainable strategies in the building sector.

Dynamic properties, probability density, and estimation of an SIRIS epidemic model with an Ornstein-Uhlenbeck process★ ★This research is supported by the Research Innovation Program for Postgraduates of Xinjiang Uygur Autonomous Region under Grant (No. XJ2023G017), the National Natural Science Foundation of China (No. 12061070) and the Natural Science Foundation of Xinjiang Uygur Autonomous Region of China (No. 2021D01E13).

Considering the relapse and temporary immunity of some diseases, a stochastic SIRIS epidemic model with saturation incidence is developed in this paper, in which the Ornstein-Uhlenbeck process is introduced to describe the randomness of the model. The existence and uniqueness of the global solution are first proved to investigate the dynamic properties of the SIRIS model. By constructing a series of Lyapunov functions, the thresholds R0E and R0S are further derived to determine the extinction and persistence of the disease. Subsequently, we prove that the solution of the model admits a stationary distribution. The probability density function is obtained by the algebraic method. In addition, the unknown parameters of the density function are estimated by using the maximum likelihood estimation. Finally, numerical simulations verify the results of the above properties.

Elasto-Plastic Constitutive Behaviour Prediction and Uncertainty Quantification of Damaged Composite Structure under High-Strain Rate Loading

Abstract The current research assesses the consequences of various damages (crack and delamination) and high-strain loading conditions on the fibre-reinforced composite structure's elasto-plastic stress-strain (EPSS) characteristics. The constitutive responses are obtained numerically (finite element discretization) using higher-order polynomials (to count the deformation) with the help of the MATLAB platform. The EPSS responses are evaluated via the modified Ludwik equation and Cowper Symonds model under high strain rate loadings. The model accuracy has been tested through the comparison between the present numerical and the published experimental data available in open domain. Furthermore, various numerical examples present the effect of damages (debond and/or crack), forces and stress-strain characteristics for a wide range of strain rates (ranging from 0.001 s−1 to 50 s−1). The stochastic constitutive model is obtained to show the modelling importance and the corresponding analysis parameters including the uncertainty quantification. Finally, a detailed understanding of the damages and geometries on the polymeric composite structure are enumerated via the delve model for the futuristic design.

Seismic analysis of gravity dam-foundation systems using stochastic spectral finite element method

Seismic analysis of dam-foundation systems is inherently complex due to uncertainties in material properties. This study investigates the effects of material randomness and heterogeneity on the seismic response of gravity dam-foundation systems using the stochastic spectral finite element method (SSFEM) based on the Karhunen Loève expansion and the polynomial chaos expansion. The Pine Plane dam is taken as an analysis example. First, the appropriate Karhunen Loève expansion modes and polynomial chaos order are determined for SSFEM by comparing them with the Monte-Carlo simulations. Subsequently, the seismic response of the dam is analyzed in the stochastic and deterministic models, respectively. Results indicate that material randomness and heterogeneity may reduce the seismic response of gravity dams, and the deterministic model may overestimate dynamic responses. Subsequently, the parametric analysis of the correlation length, which measures the spatial correlation between variables in different positions and indicates the size of heterogeneity material patches, is conducted. It is demonstrated that the correlation length would not significantly affect the mean responses, but a larger correlation length leads to greater dispersions in responses. Finally, the effects of randomness in the foundation are investigated. The heterogeneities in the foundation affect the response of the dam in the high-frequency range. Therefore, the uncertainty in material properties should be fully considered in a structural assessment and risk analysis.

A Stochastic Semi-Parametric SEIR Model with Infectivity in an Incubation Period

This paper introduces stochastic disturbances into a semi-parametric SEIR model with infectivity in an incubation period. The model combines the randomness of disease transmission and the nonlinearity of transmission rate, providing a flexible framework for more accurate description of the process of infectious disease transmission. On the basis of the discussion of the deterministic model, the stochastic semi-parametric SEIR model is studied. Firstly, we use Lyapunov analysis to prove the existence and uniqueness of global positive solutions for the model. Secondly, the conditions for disease extinction are established, and appropriate stochastic Lyapunov functions are constructed to discuss the asymptotic behavior of the model’s solution at the disease-free equilibrium point of the deterministic model. Finally, the specific transmission functions are enumerated, and the accuracy of the results are demonstrated through numerical simulations.

Fractional stochastic volatility model

This article introduces a discrete‐time fractional stochastic volatility model (FSV) based on fractional Gaussian noise. The new model includes the standard stochastic volatility model as a special case and has the same limit as the fractional integrated stochastic volatility (FISV) model, which is the continuous‐time fractional Ornstein–Uhlenbeck process. A simulated maximum likelihood method, which maximizes the time‐domain log‐likelihood function calculated by the importance sampling technique, and a frequency‐domain quasi maximum likelihood method (or quasi Whittle) are employed to estimate the model parameters. Simulation studies suggest that, while both estimation methods can accurately estimate the model, the simulated maximum likelihood method outperforms the quasi Whittle method. As an illustration, we fit the FSV and FISV models with the proposed estimation techniques to the S&P 500 composite index over a sample period spanning 45 years.

CHORUS: A New Dataset of State Interest Group Policy Positions in the United States

Abstract Research on the activities and influence of interest groups in state legislatures faces a data problem: we are missing a comprehensive, systematic dataset of interest groups’ policy preferences on state legislation. We address this gap by introducing the Dataset on Policy Choice and Organizational Representation in the United States (CHORUS). This dataset compiles over 13 million policy positions stated by tens of thousands of interest groups and individuals on bills in 17 state legislatures over the past 25 years. We describe the process used to construct CHORUS and present a new network science technique for analyzing policy position data from interest groups: the layered stochastic block model, which groups similar interest groups and bills together, respectively, based on patterns in the policy positions. Through two demonstrative applications, we show the utility of these data, combined with our novel analytical approach, for understanding interest group configurations in different state legislatures and policy areas.

Energy flexibility in aerospace manufacturing: The case of low carbon intensity production

In this paper, the prospects of energy flexibility in mitigating the environmental impact of aerospace manufacturing are explored. In collaboration with a UK-based aerospace manufacturing enterprise, demand response, in particular production time, is explored under different stochastic scenarios. This is done through a decision-support framework that consists of a stochastic discrete-event simulation model that tests different scenarios under a full factorial design of experiments framework. The simulation model tests various improvement strategies pertaining to prioritisation rules, production start-up rules, and operating hours. The model aids in scheduling energy-intensive processes, so the time of performing such processes can coincide with times of the day when the energy’s carbon intensity is at its lowest. The use case constitutes a family of aluminium structural aerospace components that are characterised by high production rate. Results demonstrate promising potential of the proposed approach, with the best-case scenario resulting in a 7% reduction in CO2e emissions. Analysis of the results demonstrate that operational decisions that do not require infrastructural changes or capital expenditures can contribute favourably to achieving net-zero targets. This research offers useful insights on leveraging operational short-term decisions to meet the aerospace manufacturing’s sector decarbonisation targets.

Policy, practice, and prediction: model-based approaches to evaluating N. gonorrhoeae antibiotic susceptibility test uptake in Australia.

Antimicrobial resistance (AMR) represents a significant threat to global health with Neisseria gonorrhoea emerging as a key pathogen of concern. In Australia, the Australian Gonococcal Surveillance Program (AGSP) plays a critical role in monitoring resistance patterns. However, antibiotic susceptibility test (AST) uptake - a crucial component for effective resistance surveillance - remains to be a limiting factor. The study aims to model the processes involved in generating AST tests for N. gonorrhoea isolates within the Australian healthcare system and assess the potential impact of systematic and policy-level changes. Two models were developed. The first model was a mathematical stochastic health systems model (SHSM) and a Bayesian Belief Network (BBN) to simulate the clinician-patient dynamics influencing AST initiation. Key variables were identified through systematic literature review to inform the construction of both models. Scenario analyses were conducted with the modification of model parameters. The SHSM and BBN highlighted clinician education and the use of clinical support tools as effective strategies to improve AST. Scenario analysis further identified adherence to guidelines and changes in patient-level factors, such as persistence of symptoms and high-risk behaviours, as significant determinants. Both models supported the notion of mandated testing to achieve higher AST initiation rates but with considerations necessary regarding practicality, laboratory constraints, and culture failure rate. The study fundamentally demonstrates a novel approach to conceptualising the patient-clinician dynamic within AMR testing utilising a model-based approach. It suggests targeted interventions to educational, support tools, and legislative framework as feasible strategies to improve AST initiation rates. However, the research fundamentally highlights substantial research gaps in the underlying understanding of AMR.

Triangular solutions to the reflection equation for U q(sl̂n)

Abstract We study solutions of the reflection equation related to the quantum affine algebra Uq(sl̂n). First, we explain how to construct a family of stochastic integrable vertex models with fixed boundary conditions. Then, we construct upper- and lower-triangular solutions of the reflection equation related to the symmetric tensor representations of Uq(sl̂n) with arbitrary spin. We also prove the star-star relation for the Boltzmann weights of the Ising-type model, conjectured by Bazhanov and Sergeev, and use it to verify certain properties of the obtained solutions.

Untangling the stranglehold through mathematical modelling of Streptococcus equi subspecies equi transmission

Strangles, a disease caused by infection with Streptococccus equi subspecies equi (S. equi), is endemic worldwide and one of the most frequently diagnosed infectious diseases of horses. Recent work has improved our knowledge of key parameters of transmission dynamics, but important knowledge gaps remain. Our aim was to apply mathematical modelling of S. equi transmission dynamics to prioritise future research areas, and add precision to estimates of transmission parameters thereby improving understanding of S. equi epidemiology and quantifying the control effort required. A compartmental deterministic model was constructed. Parameter values were estimated from current literature wherever possible. We assessed the sensitivity of estimates for the basic reproduction number on the population scale to varying assumptions for the unknown or uncertain parameters of: (mean) duration of carriership (1∕γC), relative infectiousness of carriers (f), proportion of infections that result in carriership (p), and (mean) duration of immunity after natural infection (1∕γR). Available incidence and (sero-)prevalence data were compared to model outputs to improve point estimates and ranges for these currently unknown or uncertain transmission-related parameters. The required vaccination coverage of an ideal vaccine to prevent major outbreaks under a range of control scenarios was estimated, and compared available data on existing vaccines. The relative infectiousness of carriers (as compared to acutely ill horses) and the duration of carriership were identified as key knowledge gaps. Deterministic compartmental simulations, combined with seroprevalence data, suggest that 0.05<fˆ<0.5 and that the duration of protective immunity after infection is likely 4–6 years. The presence of carriers alone may suffice to keep S. equi endemic in a population, implying that carriers cannot be ignored in control efforts. Weekly screening of herds for signs of strangles could be sufficient to ensure R < 1, provided all horses are screened for carriership post-infection. In some of worst-case scenarios, vaccination alone would not suffice to prevent major outbreaks from occurring. A stochastic agent-based model was also constructed and validated, and used to simulate a remount depot, to evaluate whether historical incidence data of recurrence of strangles within individuals could be explained without the assumption that one in four horses fail to mount a lasting immune response. These simulations demonstrated that the observed data could have occurred without that assumption.

Population Dependent Two-Sex Branching Process with Random Mating and Overlapping Generations

Abstract This research focuses on stochastic modeling of the evolution over time of biological populations through branching processes. We introduce a new class of discrete time two-sex branching processes with random mating and overlapping generations. Mating and reproduction are considered to be influenced by the numbers of females and males existing in the population. This evolution over time with generational overlap is a novel research in two-sex branching process literature. We study general probabilistic properties and establish some limiting results of biological interest.

Stochastic model for migration and breakage of detrital and authigenic fines

Mobilisation of attached particles during flow in rocks occurs in geo-energy processes. Particle mobilisation, their migration through rocks and pore plugging yield significant decline in permeability and well injectivity and productivity. While much is currently known about the underlying mechanisms governing the detachment of detrital particles against attracting electrostatic forces, a critical gap exists in the theoretical understanding of detachment by breakage of widely spread authigenic particles, which naturally grow on rock grains during geological times. Previous works derived micro-scale mechanical equilibrium equations for both detrital and authigenic particles, and the upscaling procedure from particle to pore and core scales for detrital fines. In this paper, for the first time we derive a stochastic model for migration and breakage of authigenic fines and authigenic–detrital mixtures. This allows for core-scale transport modelling based on the particle-scale torque balance. We introduce a novel framework for predictive stochastic detachment modelling by particle–rock bond breakage that integrates the beam theory of elastic particle deformation, strength failure criteria and viscous flow around the attached particle. The analytical expressions for stress maxima and stress diagrams for a single particle allow determining the critical failure stresses, breakage points of the beam and breakage flow velocity. The mathematical model describing lab coreflood includes the maximum retention function for both authigenic and detrital fines. The matching laboratory coreflood data under increasing velocity at micro- and core-scales achieved high matching of the experimental data by the model. High matching validates the upscaling and downscaling procedures derived.

Forecasting Stock Indices: Stochastic and Artificial Neural Network Models

Forecasting Stock Indices: Stochastic and Artificial Neural Network Models

A Revisit to Sunk Cost Fallacy for Two-Stage Stochastic Binary Decision Making

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making.