Stochastic Model Research Articles

Small populations of Palaeolithic humans in Cyprus hunted endemic megafauna to extinction.

The hypothesized main drivers of megafauna extinctions in the late Quaternary have wavered between over-exploitation by humans and environmental change, with recent investigations demonstrating more nuanced synergies between these drivers depending on taxon, spatial scale, and region. However, most studies still rely on comparing archaeologically based chronologies of timing of initial human arrival into naïve ecosystems and palaeontologically inferred dates of megafauna extinctions. Conclusions arising from comparing chronologies also depend on the reliability of dated evidence, dating uncertainties, and correcting for the low probability of preservation (Signor-Lipps effect). While some models have been developed to test the susceptibility of megafauna to theoretical offtake rates, none has explicitly linked human energetic needs, prey choice, and hunting efficiency to examine the plausibility of human-driven extinctions. Using the island of Cyprus in the terminal Pleistocene as an ideal test case because of its late human settlement (~14.2-13.2 ka), small area (~11 000 km2), and low megafauna diversity (2 species), we developed stochastic models of megafauna population dynamics, with offtake dictated by human energetic requirements, prey choice, and hunting-efficiency functions to test whether the human population at the end of the Pleistocene could have caused the extinction of dwarf hippopotamus (Phanourios minor) and dwarf elephants (Palaeoloxodon cypriotes). Our models reveal not only that the estimated human population sizes (n = 3000-7000) in Late Pleistocene Cyprus could have easily driven both species to extinction within < 1000 years, the model predictions match the observed, Signor-Lipps-corrected chronological sequence of megafauna extinctions inferred from the palaeontological record (P. minor at ~12-11.1 ka, followed by P. cypriotes at ~10.3-9.1 ka).

DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

ELSNC: A Semi-supervised Community Detection Method with Integration of Embedding-Enhanced Links and Node Content in Attributed Networks

In complex network analysis, detecting communities is becoming increasingly important. However, it is difficult to fuse multiple types of information to enhance the community-detection performance in real-world applications. Besides the nodes and the edges, a network also contains the structure of communities, its networking topological structure, and the network embeddings. Note that existing works on community detection have limited usage of all these information types in combination. In this work, we designed a novel unified model called embedding-enhanced link-based semi-supervised community detection with node content (ELSNC). ELSNC integrates the structure of the topology, the priori information, the network embeddings, and the node content. First, we employ two non-negative matrix factorization (NMF)–based stochastic models to characterize the node-community membership and the content-community membership (by performing similarity detection between a topic model and the NMF). Second, we introduce the nodes’ and networking embeddings’ topological similarity into the model as topological information. To model the topological similarity, we introduce a strong constraint (i.e., the priori information) and apply matrix completion to identify the community membership with the network embeddings’ representation ability. Finally, we present a semi-supervised community-detection method based on NMF that combines the network topology, content information, and the network embeddings. Our work’s innovation can be captured in two points: 1) As a type of semi-supervised community detection method, we extend the theory of semi-supervised methods on attributed networks and propose a unified model that integrates multiple information types. 2) The community membership obtained by the unified model simultaneously contains different information, including the topological, content, priori, and embedding information, which can more robustly be explored in the community structure in real-world scenarios. Furthermore, we performed a comprehensive evaluation of our proposed approach compared with state-of-the-art methods on both synthetic and real-world networks. The results show that our proposed method significantly outperformed the baseline methods.

Unraveling the Dance of Phosphoproteins at Adhesion Planes: Modeling YAP Phosphorylation by a Particle-Based Stochastic Model

Unraveling the Dance of Phosphoproteins at Adhesion Planes: Modeling YAP Phosphorylation by a Particle-Based Stochastic Model

A Semi-Markov Stochastic Model for Operational Reliability Assessment of Hybrid AC and LCC-VSC-Based DC System With Remote Wind Farms

A Semi-Markov Stochastic Model for Operational Reliability Assessment of Hybrid AC and LCC-VSC-Based DC System With Remote Wind Farms

Competitive exclusion and coexistence in a general two-species stochastic chemostat model with Markov switching

In this paper, a stochastic framework with a general nonmonotonic response function is formulated to investigate the competition dynamics between two species in a chemostat environment. The model incorporates both white noise and telegraph noise, the latter being described by Markov process. The existence of a unique global positive solution for the stochastic chemostat model is established. Subsequently, by using the ergodic theory of Markov process and utilizing techniques of stochastic analysis, the critical value differentiating between persistence in mean and extinction for the microorganism species is explored. Moreover, the existence of a unique stationary distribution is proved by using stochastic Lyapunov analysis. Finally, numerical simulations are introduced to support the obtained results.

An unconditional boundary and dynamics preserving scheme for the stochastic epidemic model

An unconditional boundary and dynamics preserving scheme for the stochastic epidemic model

Deterministic and stochastic computational models for the analysis of Nuclear Power Plant (NPP) start-up operations

The aim of this paper is to present an overview of both the deterministic and stochastic aspects of nuclear power plant (NPP) start-up and start-up accident scenarios. The neutron kinetics and corresponding nuclear reactor control mechanisms associated with deterministic NPP start-up are explained. The stochastic behaviour of neutron kinetics is explained and the procedures required to safely start-up an NPP due to these stochastic phenomena are outlined. Specific attention is given to the stochastic behaviour of neutrons in a low neutron source regime and the corresponding mathematical methods required for modelling such phenomena during NPP start-up accidents. The Pál-Bell equations are compared against the forward generating function equations utilising the gamma approximation in their ability to model NPP start-up accidents. The Pál-Bell equations are shown to be necessary to accurately calculate the maturity time for systems with either a very low neutron source strength or a very rapid reactivity insertion profile. Both the Hurwitz calculation route and MacMillan calculation route for low neutron source systems are explained. Full deterministic and stochastic calculations are performed for multiple nuclear reactor systems to illustrate the necessary analyses required for low neutron source NPP start-up accident scenarios.

Investigation of P. vivax elimination via mass drug administration: A simulation study

Plasmodium vivax is the most geographically widespread malaria parasite. P. vivax has the ability to remain dormant (as a hypnozoite) in the human liver and subsequently reactivate, which makes control efforts more difficult. Given the majority of P. vivax infections are due to hypnozoite reactivation, targeting the hypnozoite reservoir with a radical cure is crucial for achieving P. vivax elimination. Stochastic effects can strongly influence dynamics when disease prevalence is low or when the population size is small. Hence, it is important to account for this when modelling malaria elimination. We use a stochastic multiscale model of P. vivax transmission to study the impacts of multiple rounds of mass drug administration (MDA) with a radical cure, accounting for superinfection and hypnozoite dynamics. Our results indicate multiple rounds of MDA with a high-efficacy drug are needed to achieve a substantial probability of elimination. This work has the potential to help guide P. vivax elimination strategies by quantifying elimination probabilities for an MDA approach.

Global insights into a stochastic SIRS epidemic model with Beddington–DeAngelis incidence rate

Global insights into a stochastic SIRS epidemic model with Beddington–DeAngelis incidence rate

Emergency Supply Alternatives for a Storage Facility of a Repairable Multi-Component System

This paper studies a continuous-review stochastic replenishment model for a multi-component system with regular and emergency orders. The system consists of N parallel and independent components, each of which has a finite life span. In addition, there is a warehouse with a limited stock of new components. Each broken component is replaced by a new component from the stock. When no component is available, an emergency supply is ordered. The stock is managed according to an ((s,S),(0,Qe)) policy, which is a combination of an (s,S) policy for the regular order and a (0,Qe) policy for the emergency order. The regular order is delivered after an exponentially distributed lead time, whereas the emergency order is delivered immediately. We study three sub-policies for emergency orders, which differ from each other in size and in relation to the regular order. Applying the results from queueing theory and phase-type properties, we derive the optimal thresholds for each sub-policy and then compare the economic benefit of each one.

Minimum-energy switching geometric filter on lie groups for differential-drive wheeled mobile robots

Accurate state estimation plays a critical role in various applications, such as tracking, regulation, and fault detection in robotic and mechanical systems. Typically, the Kalman–Bucy filter is used as a linear state observer for this purpose. However, real-world robots often exhibit complex behavior, characterized by a combination of dynamics, making it essential to employ hybrid filters. In this context, the Switching Kalman filter stands out as a well-established solution. In this article we aim to generalize the Brownian-Markov Stochastic Model, a hybrid dynamic model for differential-drive wheeled mobile robots, to the case of a mobile robot whose center of mass is not aligned to the wheels axle middle point, and to design a geometric hybrid state estimator by exploiting the Lie groups theory. The Brownian-Markov Stochastic Model features two modes: “grip” and “slip”. These modes correspond to ideal grip and lateral slippage, with transitions governed by a state-dependent Markov chain. To validate the proposed switching filter, we conduct MATLAB® simulations of the robot’s motion in a scenario prone to lateral grip loss, comparing the state estimates produced by the switching geometric filter with those obtained using the switching Kalman filter.

Geometric modelling of corrosion inhibitor pigments in active protective coatings based on SR-nano-CT images

This paper reports on an essential step towards accelerating the development of new, environmentally friendly active protective coatings by structure optimization. The complex microstructure of the pigment particles within a coating have been observed non-destructively in 3D by nano-computed tomography using synchrotron radiation. For the first time, a stochastic geometry model is fitted based on spatial geometric features of the particles observed in the 3D images. The typical cell of a random Gibbs-Laguerre tessellation is used to model the particles' polyhedral shapes as well as the observed joint size and aspect ratio distributions.

Stochastic optimal control model for COVID-19: mask wearing and active screening/testing

Stochastic optimal control model for COVID-19: mask wearing and active screening/testing

Statistical Mobility of Multicellular Colonies of Flagellated Swimming Cells.

We study the stochastic hydrodynamics of colonies of flagellated swimming cells, typified by multicellular choanoflagellates, which can form both rosette and chainlike shapes. The objective is to link cell-scale dynamics to colony-scale dynamics for various colonial morphologies. Via autoregressive stochastic models for the cycle-averaged flagellar force dynamics and statistical models for demographic cell-to-cell variability in flagellar properties and placement, we derive effective transport properties of the colonies, including cell-to-cell variability. We provide the most quantitative detail on disclike geometries to model rosettes, but also present formulas for the dynamics of general planar colony morphologies, which includes planar chain-like configurations.

Structural Effects of Economic Shocks on the Macroeconomic Economy–Electricity–Emissions Nexus in India via Long-Term Cointegration Approach

For developing nations to achieve net-zero targets, macroeconomic linkages impacting the decoupling of emissions from economic growth must account for non-linear business cycles and economic shocks. This study aims to delineate decarbonization policy pathways for the Indian electricity sector in the aftermath of COVID-19 by analysing the long-term evolution of the economy–electricity–emissions (3E) nexus during the 2008 financial crisis and during COVID-19, covering the period of 1996Q2 to 2020Q3. Upon testing multiple theoretical 3E systems, it was found that a model internalizing trade, inflation, and stochasticity was able to minimize the reproduction errors from growth to recession phases, as well as predict the rebound effect from an economic crisis. This was revealed to be due to more information within the coefficients in a trade stochastic model. Our results confirm the existence of electricity-associated emission decoupling with capital formation in the long-run, post-crisis, while economic growth and inflation increase CO2 emissions. The main finding highlights the negative feedback loop of inflation-&gt;trade-&gt;emissions, which shows that GDP and emissions are not directly causal. This long-run macroeconomic dynamic death spiral causes decoupling to be inhibited, where fossil fuel imports should not be subsidized for economic shock rebound, and the risk hedging of energy transition investments should occur in the post-COVID-19 era.

Stochastic Modeling of Crack Growth and Maintenance Optimization for Metallic Components Subjected to Fatigue-Induced Failure

Abstract The degradation of metallic systems under cyclic loading is subject to significant uncertainty, which affects the reliability of residual lifetime predictions and subsequent decisions on optimum maintenance schedules. This paper focuses two main challenges in developing a reliable framework for the lifecycle management of fatigue-critical components: constructing a stochastic model that captures uncertainties in crack growth histories, and presenting a computationally efficient strategy for solving the stochastic optimization associated with maintenance scheduling. Polynomial chaos (PC) representation is proposed to propagate uncertainty in the fatigue-induced crack growth process, using a database from constant amplitude loading experiments. Additionally, an optimization strategy is implemented based on Gaussian process surrogate modeling to solve the stochastic optimization problem under maximum probability of failure constraints. The sensitivity of the optimum solution to different probability of failure thresholds is examined. The proposed framework offers a decision support tool for informed decision-making under uncertainty, aiming to mitigate fatigue failure.

Statistical uncertainty principle in Markov kinetics

A reciprocality between the statistical variance of observables of a thermodynamic state and that of their conjugate variables, as entropic forces, originates from the thermodynamic conjugacy with respect to an entropy function. This thermodynamic uncertainty principle in equilibrium can be derived from the Maximum Entropy principle and is independent upon underlying mechanistic details. We present, based on the Maximum Caliber principle as the dynamic generalization of Maximum Entropy, the formalism of the uncertainty principle in kinetics in time homogeneous Markov processes between transitional observables and their conjugate path entropic forces. A stochastic biophysical model for molecular motors is used as an illustrating example. The present work generalizes the phenomenological thermodynamics of uncertainties/fluctuations and is applicable to data ad infinitum.

Dynamics of a Stochastic Vector-Borne Model with Plant Virus Disease Resistance and Nonlinear Incidence

Symmetry in mathematical models often refers to invariance under certain transformations. In stochastic models, symmetry considerations must also account for the probabilistic nature of inter- actions and events. In this paper, a stochastic vector-borne model with plant virus disease resistance and nonlinear incidence is investigated. By constructing suitable stochastic Lyapunov functions, we show that if the related threshold R0s&lt;1, then the disease will be extinct. By using the reproduction number R0, we establish sufficient conditions for the existence of ergodic stationary distribution to the stochastic model. Furthermore, we explore the results graphically in numerical section and find that random fluctuations introduced in the stochastic model can suppress the spread of the disease, except for increasing plant virus disease resistance and decreasing the contact rate between infected plants and susceptible vectors. The results reveal the correlation between symmetry and stochastic vector-borne models and can provide deeper insights into the dynamics of disease spread and control, potentially leading to more effective and efficient management strategies.

Neural network empowered liquidity pricing in a two-price economy under conic finance settings

In the article at hand neural networks are used to model liquidity in financial markets, under conic finance settings, in two different contexts. That is, on the one hand this paper illustrates how the use of neural networks within a two-price economy allows to obtain accurate pricing and Greeks of financial derivatives, enhancing computational performances compared to classical approaches such as (conic) Monte Carlo. The methodology proposed for this purpose is agnostic of the underlying valuation model, and it easily adapts to all models suitable for pricing in conic financial markets. On the other hand, this article also investigates the possibility of valuing contingent claims under conic assumptions, using local stochastic volatility models, where the local volatility is approximated by means of a (combination of) neural network(s). Moreover, we also show how it is possible to generate hybrid families of distortion functions to better fit the implied liquidity of the market, as well as we introduce a conic version of the SABR model, based on the Wang transform, that still allows for analytical bid and ask pricing formulae.