Stochastic Model Research Articles

Dynamical behaviors of a stochastic SIRV epidemic model with the Ornstein–Uhlenbeck process

AbstractVaccination is an important tool in disease control to suppress disease, and vaccine-influenced diseases no longer conform to the general pattern of transmission. In this paper, by assuming that the infection rate is affected by the Ornstein–Uhlenbeck process, we obtained a stochastic SIRV model. First, we prove the existence and uniqueness of the global positive solution. Sufficient conditions for the extinction and persistence of the disease are then obtained. Next, by creating an appropriate Lyapunov function, the existence of the stationary distribution for the model is proved. Further, the explicit expression for the probability density function of the model around the quasi-equilibrium point is obtained. Finally, the analytical outcomes are examined by numerical simulations.

Optical variability of the blazar 3C 371: From minute to year timescales

The BL Lac object 3C 371 was observed by the Transiting Exoplanet Survey Satellite (TESS) for approximately a year, between July 2019 and July 2020, with an unmatched two-minute imaging cadence. In parallel, the Whole Earth Blazar Telescope (WEBT) Collaboration organized an extensive observing campaign, providing three years of continuous optical monitoring between 2018 and 2020. These datasets allow for a thorough investigation of the variability of the source. The goal of this study is to evaluate the optical variability of 3C 371. Taking advantage of the remarkable cadence of TESS data, we aim to characterize the intra-day variability (IDV) displayed by the source and identify its shortest variability timescale. With this estimate, constraints on the size of the emitting region and black hole mass can be calculated. Moreover, WEBT data are used to investigate long-term variability (LTV), including in terms of the spectral behavior of the source and the polarization variability. Based on the derived characteristics, we aim to extract information on the origin of the variability on different timescales. We evaluated the variability of 3C 371 by applying the variability amplitude tool, which quantifies variability of the emission. Moreover, we employed common tools, such as ANOVA (ANalysis Of VAariance) tests, wavelet and power spectral density (PSD) analyses to characterize the shortest variability timescales present in the emission and the underlying noise affecting the data. We evaluated the short- and long-term color behavior to understand to understand its spectral behavior. The polarized emission was analyzed, studying its variability and possible rotation patterns of the electric vector position angle (EVPA). Flux distributions of the IDV and LTV were also studied with the aim being to link the flux variations to turbulent and/or accretion-disk-related processes. Our ANOVA and wavelet analyses reveal several entangled variability timescales. We observe a clear increase in the variability amplitude with increasing width of the time intervals evaluated. We are also able to resolve significant variations on timescales of as little as sim 0.5 hours. The PSD analysis reveals a red-noise spectrum with a break at IDV timescales. The spectral analysis shows a mild bluer-when-brighter (BWB) trend on long timescales. On short timescales, mixed BWB, achromatic and redder-when-brighter (RWB) signatures can be observed. The polarized emission shows an interesting slow EVPA rotation during the flaring period where a simple stochastic model can be excluded as the origin with a 3sigma significance . The flux distributions show a preference for a Gaussian model for the IDV, and suggest it may be linked to turbulent processes, while the LTV is better represented by a log-normal distribution and may have a disk-related.

Adaptive network traffic control with approximate dynamic programming based on a non-homogeneous Poisson demand model

ABSTRACT In this study, we develop a stochastic dynamic traffic-flow model subject to practical restrictions under the non-homogeneous Poisson vehicle arrival process. Using the cell transmission strategy, we establish traffic dynamics between two intersections. We also discuss simulating the random demand of source links from an estimated intensity function. Additionally, we propose an algorithm to optimize time interval division for aggregated data, aiming to enhance estimation performance. We explore applying our traffic flow model to the adaptive traffic network management problem, which is formulated as a Markov decision process. Leveraging approximate dynamic programming with recursive least squares-temporal difference learning, we achieve adaptive optimal policies. To validate our approach, we conduct a series of numerical experiments with random demands. The results of non-homogeneous Poisson demand conducted using random numbers and a real-word dataset indicate high efficiency with the piecewise constant, I-SMOOTH, and MNO-PQRS estimators. Compared to the Webster and Max-pressure control systems, our proposed approximate dynamic programming-based model exhibits superior stability and applicability.

Variance and volatility swaps and options under the exponential fractional Ornstein–Uhlenbeck model

Considering the fair strike values of variance and volatility swaps, we use a stochastic volatility model in which the log volatility is given by a fractional Ornstein–Uhlenbeck process with two versions; a stationary version and a version with a deterministic initial value. Under these versions, the fair strike formulas are obtained in exact form for variance swaps and approximated fair strike formulas are derived for volatility swaps based on the fact that an aggregation of log-normal variables is well-approximated by shifted log-normal or log-normal distribution. In addition, we obtain two approximate pricing formulas for European options on the realized variance and volatility. The accuracy and robustness of the approximated fair strike formulas are examined via Monte-Carlo computations. We conduct calibration experiments to show that the Hurst exponent and the mean-reversion property of the fractional Ornstein–Uhlenbeck process are able to produce various shapes resembling the market term-structures of variance swaps when they are put together.

REHEATFUNQ (REgional HEAT-Flow Uncertainty and aNomaly Quantification) 2.0.1: a model for regional aggregate heat flow distributions and anomaly quantification

Abstract. Surface heat flow is a geophysical variable that is affected by a complex combination of various heat generation and transport processes. The processes act on different lengths scales, from tens of meters to hundreds of kilometers. In general, it is not possible to resolve all processes due to a lack of data or modeling resources, and hence the heat flow data within a region is subject to residual fluctuations. We introduce the REgional HEAT-Flow Uncertainty and aNomaly Quantification (REHEATFUNQ) model, version 2.0.1. At its core, REHEATFUNQ uses a stochastic model for heat flow within a region, considering the aggregate heat flow to be generated by a gamma-distributed random variable. Based on this assumption, REHEATFUNQ uses Bayesian inference to (i) quantify the regional aggregate heat flow distribution (RAHFD) and (ii) estimate the strength of a given heat flow anomaly, for instance as generated by a tectonically active fault. The inference uses a prior distribution conjugate to the gamma distribution for the RAHFDs, and we compute parameters for a uninformed prior distribution from the global heat flow database by Lucazeau (2019). Through the Bayesian inference, our model is the first of its kind to consistently account for the variability in regional heat flow in the inference of spatial signals in heat flow data. Interpretation of these spatial signals and in particular their interpretation in terms of fault characteristics (particularly fault strength) form a long-standing debate within the geophysical community. We describe the components of REHEATFUNQ and perform a series of goodness-of-fit tests and synthetic resilience analyses of the model. While our analysis reveals to some degree a misfit of our idealized empirical model with real-world heat flow, it simultaneously confirms the robustness of REHEATFUNQ to these model simplifications. We conclude with an application of REHEATFUNQ to the San Andreas fault in California. Our analysis finds heat flow data in the Mojave section to be sufficient for an analysis and concludes that stochastic variability can allow for a surprisingly large fault-generated heat flow anomaly to be compatible with the data. This indicates that heat flow alone may not be a suitable quantity to address fault strength of the San Andreas fault.

Efficient Algorithm for Proportional Lumpability and Its Application to Selfish Mining in Public Blockchains

This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and is applicable to Markov chains demonstrating structural regularity. Proportional lumpability extends this idea, proposing that the transition rates of a Markov chain can be modified by certain factors, resulting in a lumpable new Markov chain. This concept facilitates the derivation of precise performance indices for the original process. This paper establishes the well-defined nature of the problem of computing the coarsest proportional lumpability that refines a given initial partition, ensuring a unique solution exists. Additionally, a polynomial time algorithm is introduced to solve this problem, offering valuable insights into both the concept of proportional lumpability and the broader realm of partition refinement techniques. The effectiveness of proportional lumpability is demonstrated through a case study that consists of designing a model to investigate selfish mining behaviors on public blockchains. This research contributes to a better understanding of efficient approaches for handling large stochastic models and highlights the practical applicability of proportional lumpability in deriving exact performance indices.

Environmental Regulation and Stormwater Management Strategies for an Urban River in Northwest China: A Sustainable Approach

Low-impact developments (LIDs) have emerged as effective strategies for mitigating the adverse impacts of urbanization on river environments. This study aims to enhance river environment quality by examining the effects of LIDs and land use/cover change (LUCC) in the context of river environment and hydrological conditions. Using the Stormwater Management Model (SWMM) in an urban river setting, the study investigates the impact of LIDs on urban river water volume. An analysis of river runoff quality and quantity is conducted, followed by the development of an optimal river water regulation scheme through a multi-objective ecological scheduling model. The results reveal that the incorporation of LIDs can substantially decrease river runoff yield for varying recurrence periods of design rainstorms. Consequently, the flood peak reduction rate ranged from 10% to 18%, and the flood volume experienced a reduction of 10–29% in the study area. The combination of river water regulation, LIDs and LUCC leads to a decrease in river water volume within the lower river channel by up to 47% especially in a typical dry year and dry season, accompanied by a decline in river flow velocity and water self-purification capacity. A risk-based multi-objective stochastic optimization model is employed to ensure sustainable management of urban river runoff in terms of both quantity and quality. This research contributes to the advancement of knowledge in sustainable basin management practices and offers practical insights for policymakers involved in the management of water resources and environmental conservation in semi-arid basins.

Structural heterogeneity in tetra-armed gels revealed by computer simulation: Evidence from a graph theory assisted characterization.

Designing homogeneous networks is considered one typical strategy for solving the problem of strength and toughness conflict of polymer network materials. Experimentalists have proposed the hypothesis of obtaining a structurally homogeneous hydrogel by crosslinking tetra-armed polymers, whose homogeneity was claimed to be verified by scattering characterization and other methods. Nevertheless, it is highly desirable to further evaluate this issue from other perspectives. In this study, a coarse-grained molecular dynamics simulation coupled with a stochastic reaction model is applied to reveal the topological structure of a polymer network synthesized by tetra-armed monomers as precursors. Two different scenarios, distinguished by whether internal cross-linking is allowed, are considered. We introduce the Dijkstra algorithm from graph theory to precisely characterize the network structure. The microscopic features of the network structure, e.g., loop size, dispersity, and size distribution, are obtained via the Dijkstra algorithm. By comparing the two reaction scenarios, Scenario II exhibits an overall more idealized structure. Our results demonstrate the feasibility of the Dijkstra algorithm for precisely characterizing the polymer network structure. We expect this work will provide a new insight for the evaluation and description of gel networks and further help to reveal the dynamic process of network formation.

Investigating Microstructure–Property Relationships of Nonwovens by Model-Based Virtual Material Testing

AbstractQuantifying the relationship between geometric descriptors of microstructure and effective properties like permeability is essential for understanding and improving the behavior of porous materials. In this paper, we employ a previously developed stochastic model to investigate microstructure–property relationships of nonwovens. First, we show the capability of the model to generate a wide variety of realistic nonwovens by varying the model parameters. By computing various geometric descriptors, we investigate the relationship between model parameters and microstructure morphology and, in this way, assess the range of structures which may be described by our model. In a second step, we perform virtual materials testing based on the simulation of a wide range of nonwovens. For these 3D structures, we compute geometric descriptors and perform numerical simulations to obtain values for permeability as an effective material property. We then examine and quantify the relationship between microstructure morphology and permeability by fitting parametric regression formulas to the obtained data set, including but not limited to formulas from the literature. We show that for structures which are captured by our model, predictive power may be improved by allowing for slightly more complex formulas.

A stochastic model of ant trail formation and maintenance in static and dynamic environments

AbstractColonies of ants can complete complex tasks without the need for centralised control as a result of interactions between individuals and their environment. Particularly remarkable is the process of path selection between the nest and food sources that is essential for successful foraging. We have designed a stochastic model of ant foraging in the absence of direct communication. The motion of ants is governed by two components - a random change in direction of motion that improves ability to explore the environment, and a non-random global indirect interaction component based on pheromone signalling. Our model couples individual-based off-lattice ant simulations with an on-lattice characterisation of the pheromone diffusion. Using numerical simulations we have tested three pheromone-based model alternatives: (1) a single pheromone laid on the way toward the food source and on the way back to the nest; (2) single pheromone laid on the way toward the food source and an internal imperfect compass to navigate toward the nest; (3) two different pheromones, each used for one direction. We have studied the model behaviour in different parameter regimes and tested the ability of our simulated ants to form trails and adapt to environmental changes. The simulated ants behaviour reproduced the behaviours observed experimentally. Furthermore we tested two biological hypotheses on the impact of the quality of the food source on the dynamics. We found that increasing pheromone deposition for the richer food sources has a larger impact on the dynamics than elevation of the ant recruitment level for the richer food sources.

Deep calibration with random grids

We propose a neural network-based approach to calibrating stochastic volatility models, which combines the pioneering grid approach by Horvath et al. [Deep learning volatility: A deep neural network perspective on pricing and calibration in (rough) volatility models. Quant. Finance, 2021, 21(1), 11–27]. with the pointwise two-stage calibration of Bayer and Stemper [Deep calibration of rough stochastic volatility models. Working Paper, arXiv:1810.03399, 2018] and Liu et al. [A neural network-based framework for financial model calibration. J. Math. Ind., 2019, 9(1), 1–28]. Our methodology inherits robustness from the former while not suffering from the need for interpolation/extrapolation techniques, a clear advantage ensured by the pointwise approach. The crucial point to the entire procedure is the generation of implied volatility surfaces on random grids, which one dispenses to the network in the training phase. We support the validity of our calibration technique with several empirical and Monte Carlo experiments for the rough Bergomi and Heston models under a simple but effective parametrization of the forward variance curve. The approach paves the way for valuable applications in financial engineering—for instance, pricing under local stochastic volatility models—and extensions to the fast-growing field of path-dependent volatility models.

Research on High-performance Computing Network Search System based on Computer Big Data

An efficient computing system for massively parallel systems is established. The stochastic Petri net abstracts and models the high-performance computer work scheduling system. The IB switch is modeled. Stochastic Petri nets are used for performance analysis. Finally, the proposed method is combined with InfiniBand interconnection architecture to evaluate the system's delay. The experimental results prove the feasibility of this algorithm.

Fiscal Policy for Inclusive Growth. A Case Study of Myanmar

Following decades of isolation under military rule, Myanmar initiated historic political reforms since 2011, but faces challenges translating liberalization into broad-based advancement. A dynamic stochastic general equilibrium modelling approach estimated on Myanmar data is used, with simulations quantifying potential growth and distributional impacts of strategic government expenditure reallocations towards infrastructure upgrading, agricultural investments, expanded social transfers and progressive tax reforms. Results indicate sustained infrastructure and rural productivity spending have high output multipliers while well-targeted transfers reduce income inequality. However, institutional absorptive capacities condition realization of social returns. Complementary governance enhancements like project oversight and competitiveness in public investment allocation prove vital. Regional peer benchmarking provides comparative lessons. Altogether, evidence-based fiscal reorientation towards addressing pressing human capital, rural-urban and firm competitiveness constraints can accelerate inclusive growth in Myanmar if pursued prudently. Sustainable expansion requires strengthening public financial management (PFM) systems and results monitoring.

2D Smagorinsky-Type Large Eddy Models as Limits of Stochastic PDEs

We prove that a version of Smagorinsky large eddy model for a 2D fluid in vorticity form is the scaling limit of suitable stochastic models for large scales, where the influence of small turbulent eddies is modeled by a transport-type noise.

Dynamics of a Stochastic SVEIR Epidemic Model with Nonlinear Incidence Rate

This paper delves into the analysis of a stochastic epidemic model known as the susceptible–vaccinated–exposed–infectious–recovered (SVEIR) model, where transmission dynamics are governed by a nonlinear function. In the theoretical analysis section, by suitable stochastic Lyapunov functions, we establish that when the threshold value, denoted as R0s, falls below 1, the epidemic is destined for extinction. Conversely, if the reproduction number R0 of the deterministic model surpasses 1, the model manifests an ergodic endemic stationary distribution. In the numerical simulations and data interpretation section, leveraging a graphical analysis with COVID-19 data, we illustrate that random fluctuations possess the capacity to quell disease outbreaks, underscoring the role of vaccines in curtailing the spread of diseases. This study not only contributes to the understanding of epidemic dynamics but also highlights the pivotal role of stochasticity and vaccination strategies in epidemic control and management. The inherent balance and patterns observed in epidemic spread and control strategies, reflect a symmetrical interplay between stochasticity, vaccination, and disease dynamics.

Optimization for stochastic model arisen from investment problem associated with default risk

In this paper, we study the optimal strategy arisen from the excess-of-loss reinsurance and asset allocation in defaultable markets under a general stochastic model. By developing the corresponding dynamic programming approach, we establish the optimal investment approach through two sub-problems: a pre-default case and a post-default case, respectively, characterized by the obtained Hamilton–Jacobi–Bellman (HJB) equations. We show the existence of a classical solution to the pre-default case via super-sub solution techniques and give an explicit characterization of the optimal reinsurance and investment policy that maximizes the common used utility associated with the terminal wealth. Verification theorem is further established to show the uniqueness of the corresponding solution of HJB equation that is critical to the desired optimal solution.

Borel control and efficient numerical techniques to solve the Allen–Cahn equation governed by temporal multiplicative noise

In this manuscript, we investigate a stochastic version of the Allen–Cahn equation, which is a nonlinear partial differential equation from mathematical physics. The stochastic model considers temporal multiplicative noise in the form of the derivative of the standard Wiener process. The existence and the uniqueness of solutions for this stochastic model is established rigorously using the theory of distributions. As a corollary from these analytical results, some a priori optimal estimates for the solutions of this model are constructed. In this work, we develop reliable numerical schemes which possess similar features as those of the solutions for the analytical model. Moreover, we establish mathematically the von Neumann stability and the consistency for the schemes proposed in this paper. Both the analytical and numerical results derived in this work are computationally verified through some simulations.

Spatio-temporal evolution of sports tourism industry integration: A empirical analysis of Xinjiang in China.

Since the issuance of the "Guiding Opinions on Vigorously Developing Sports Tourism" in 2016, the integration of sports and tourism has become a strategy in regional economic development. It creates new economic growth points, enhances local images, and promotes cultural communication. In the context of the "Tourism Makes Xinjiang Thrive" strategy, quantitatively investigating the integration of the sports and tourism industries helps people to better understand their interaction which can serve as the valuable input in policy-making for the comprehensive development of a region. This paper uses entropy weight method, stochastic frontier analysis and coupling coordination model to quantitatively analyze the effect of sports tourism industry integration in Xinjiang from the perspective of integration path. Meanwhile, the Dagum Gini coefficient and nuclear density estimation were used to analyze the regional differences and dynamic evolution of industrial integration quality. The result shows that (1) The sports and tourism integration quality in Xinjiang has not reached the optimal goal of complete integration. In the process of mutual industrial promotion, tourism promotes a higher degree of integration with the sports industry. (2) The industrial integration quality shows a phenomenon of "imbalance and inadequacy" among the regions. The regions with high quality of industrial integration were Urumqi, Ili, Kashgar, Altay and Changji, which have rich sports tourism resources. (3) The overall spatial difference in the quality of industrial integration presented a fluctuation downtrend. The difference between the tourism industrial belts was very significant.

Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation

The development of mathematical models for studying newly emerging and re-emerging infectious diseases has gained momentum due to global events. The gyrodactylid-fish system, like many host-parasite systems, serves as a valuable resource for ecological, evolutionary, and epidemiological investigations owing to its ease of experimental manipulation and long-term monitoring. Although this system has an existing individual-based model, it falls short in capturing information about species-specific microhabitat preferences and other biological details for different Gyrodactylus strains across diverse fish populations. This current study introduces a new individual-based stochastic simulation model that uses a hybrid τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document}-leaping algorithm to incorporate this essential data, enhancing our understanding of the complexity of the gyrodactylid-fish system. We compare the infection dynamics of three gyrodactylid strains across three host populations. A modified sequential-type approximate Bayesian computation (ABC) method, based on sequential Monte Carlo and sequential importance sampling, is developed. Additionally, we establish two penalised local-linear regression methods (based on L1 and L2 regularisations) for ABC post-processing analysis to fit our model using existing empirical data. With the support of experimental data and the fitted mathematical model, we address open biological questions for the first time and propose directions for future studies on the gyrodactylid-fish system. The adaptability of the mathematical model extends beyond the gyrodactylid-fish system to other host-parasite systems. Furthermore, the modified ABC methodologies provide efficient calibration for other multi-parameter models characterised by a large set of correlated or independent summary statistics.

Inference in the nonparametric stochastic frontier model

This article is the first in the literature to discuss in detail how to conduct various types of inference in the stochastic frontier model when it is estimated using nonparametric methods. We discuss a general and versatile inferential technique that allows for a range of practical hypotheses of interest to be tested. We also discuss several challenges that currently exist in this framework in an effort to alert researchers to potential pitfalls. Namely, it appears that when one wishes to estimate a stochastic frontier in a fully nonparametric framework, separability between inputs and determinants of inefficiency is an essential ingredient for the correct empirical size of a test. We showcase the performance of the test with a variety of Monte Carlo simulations.