Narrow Results

Majority of epidemics eradication programs work in preventive responsive way. The lack of exact information about epidemiological status of individuals makes responsive actions less efficient. Here, we demonstrate that additional tests can significantly increase the efficiency of "blind" treatment (vaccination or culling). Eradication strategy consisting of "blind" treatment in very limited local neighborhood supplemented by extra tests in a little bit larger neighborhood is able to prevent invasion of even highly infectious diseases and to achieve this at a cost lower than for the "blind" strategy. The effectiveness of the extended strategy depends on such parameters as the test efficiency and test cost.

In this work, we propose a single period stochastic inventory decision-making model that captures the trade-off between inventory policies and disruption risks for unreliable (both uncapacitated and capacitated) dual-sourcing reverse supply chain networks. Risk-management has emerged high at the corporate agenda as globalised supply chain networks are more stretched than ever due to offshoring and thus are more exposed to disruptions, while reverse logistics has been proven to constitute a profit center. In this environment, global companies have to scrutinize especially the role of major Asian economies (with large manufacturing capacities and huge markets), while conducting their strategic procurement planning. The developed model can be applied to a number of different scenario types encompassing various instances of disruptions to the collection of the end-of-life products, of the transportation system, and of the remanufacturing yield and capacity. Analytical closed-form solutions are obtained and important managerial insights on the merit of contingency strategies in managing uncertainties for reverse logistics networks are discussed.

This paper develops a queueing system model to analyze the operations of a hierarchical healthcare system consisting of general hospitals (GHs) and community healthcare centers (CHCs). GHs typically provide a higher level of health care service than CHCs, and thus are preferred choices for many patients’ healthcare service needs. Consequently, GHs are often heavily congested and patients often incur excessive waiting time. In contrast, CHCs are often idle and resources are underutilized. To help balance the utilization of resources in GHs and CHCs, a congestion-based information guidance policy is proposed in this paper to inform patients in the GH service queue about the anticipated delay. Upon being informed the delay for GH service, patients may balk, remain in queue for GH service, or switch to receive service at CHCs. This policy is thus expected to relieve the congestion at GHs and promote CHC usage. To study the effects of the proposed policy, a hierarchical healthcare system is modeled as a queueing system with strategic patients. Stationary performance measures of the system are analytically characterized using a Markov chain model. Stochastic and numerical analyses provide insights on how to design information guidance policy that would help improve overall health care service quality under different scenarios.

We discuss a recently developed embolic stroke model in relation to models of percolation. When descibing embolic stroke, it is important to know if blood flow can travel from the major arteries to the arterioles supplying the brain. In this sense, the onset of stroke bears some similarity to the central question of percolation theory. The order parameter for the percolation transition is found, and the percolation threshold is investigated. We discuss possible extensions to the model.

Experiments have shown that in embryonic stem cells, the promoters of many lineage-control genes contain “bivalent domains”, within which the nucleosomes possess both active (H3K4me3) and repressive (H3K27me3) marks. Such bivalent modifications play important roles in maintaining pluripotency in embryonic stem cells. Here, to investigate gene expression dynamics when there are regulations in bivalent histone modifications and random partition in cell divisions, we study how positive feedback to histone methylation/demethylation controls the transition dynamics of the histone modification patterns along with cell cycles. We constructed a computational model that includes dynamics of histone marks, three-stage chromatin state transitions, transcription and translation, feedbacks from protein product to enzymes to regulate the addition and removal of histone marks, and the inheritance of nucleosome state between cell cycles. The model reveals how dynamics of both nucleosome state transition and gene expression are dependent on the enzyme activities and feedback regulations. Results show that the combination of stochastic histone modification at each cell division and the deterministic feedback regulation work together to adjust the dynamics of chromatin state transition in stem cell regenerations.

We consider a stochastic model for T-cell activation proposed in Refs. [1] and [2] to compare the specificity and sensitivity of two different strategies for T-cell activation that utilize the history of phosphorylation of T-cell receptor (TCR). We compare these two strategies when the temporal signals/events that are essential for progressive T-cell activation are suppressed by blockade of CD4 receptor that may have caused by disease or therapeutic effects.^3–6 We show that under these conditions, a threshold-strategy which is capable of maintaining a threshold (for total number of phosphorylated TCRs by time $T$) for a further duration $\mathrm{\Delta }T$ performs better in discriminating agonist peptides than a single-threshold strategy (reached by time $T$) leading to T-cell activation using the Wentzell-Friedlin theory for large deviations for stochastic processes.^7,8

In this work, we use continuous-time Markov jump processes and the corresponding zero fluctuation ordinary differential equations to analyze the relation between immune response and cancerous cells. We incorporate the Allee effect into our model to show that intrinsic stochasticity and nonlinearity may interact in elimination, equilibrium, and escape mechanisms in the low-count regime. Later, we consider the effect of immunotherapy through a pulse injection term and the Tau-Leaping algorithm. We show using the model state variables and parameters that the cancer cell population at its threshold level gets into the elimination phase for high antigenicity values.

Manufacturing systems are increasingly becoming automated and complex in nature. Highly reliable and flexible manufacturing systems (FMSs) are the necessity of manufacturing industries to fulfill the increasing customized demands. Worldwide, FMSs are used in industries to attain high productivity in production environments with rapidly and continuously changing manufactured goods structures and demands. Reliability prediction plays a very significant role in system design in the manufacturing industry, and two crucial issues in the prediction of system reliability are failures of equipment and system configuration. This novel work presents a stochastic model to analyze the performance of an FMS through its reliability characteristics, in the concern of its equipment. To improve the reliability of FMS, determine the sensitivity of the reliability measures of FMS. FMS consists of many components such as machine tools like CNC, automatic handling and material storage, controller and robot for serving load. The designed system is studied by using the Markov process, supplementary variable technique, Laplace transformation, coverage factor and Gumbel–Hougaard family copula to obtain various reliability measures. For some realistic approach, particular cases and graphical illustrations are also obtained.

This paper investigates the relationship between volatility and liquidity on the German electricity futures market based on high-frequency intraday prices. We estimate volatility by the time-weighted realized variance acknowledging that empirical intraday prices are not equally spaced in time. Empirical evidence suggests that volatility of electricity futures decreases as time approaches maturity, while coincidently liquidity increases. Established continuous-time stochastic models for electricity futures prices involve a growing volatility function in time and are thus not able to capture our empirical findings a priori. In Monte Carlo simulations, we demonstrate that incorporating increasing liquidity into the established models is key to model the decreasing volatility evolution.

In this paper, we develop a stochastic model for future monthly spot prices of the most important crude oils and refined products. The model is easy to calibrate to both historical data and views of a user even in the presence of negative prices which have been observed recently. This makes it particularly useful for risk management and design of optimal hedging strategies in incomplete market situations where perfect hedging may be impossible or prohibitively expensive to implement. We illustrate the model with optimization of hedging strategies for refinery margins in illiquid markets using a portfolio of 12 most liquid derivative contracts with 12 maturities traded on New York Mercantile Exchange (NYMEX) and Intercontinental Exchange (ICE).

This paper reviews recent progress in modeling collective behaviors in myxobacteria using lattice gas cellular automata approach (LGCA). Myxobacteria are social bacteria that swarm, glide on surfaces and feed cooperatively. When starved, tens of thousands of cells change their movement pattern from outward spreading to inward concentration; they form aggregates that become fruiting bodies. Cells inside fruiting bodies differentiate into round, nonmotile, environmentally resistant spores. Traditionally, cell aggregation has been considered to imply chemotaxis, a long-range cell interaction. However, myxobacteria aggregation is the consequence of direct cell-contact interactions, not chemotaxis. In this paper, we review biological LGCA models based on local cell–cell contact signaling that have reproduced the rippling, streaming, aggregating and sporulation stages of the fruiting body formation in myxobacteria.

We review the task of aligning simple models for language dynamics with relevant empirical data, motivated by the fact that this is rarely attempted in practice despite an abundance of abstract models. We propose that one way to meet this challenge is through the careful construction of null models. We argue in particular that rejection of a null model must have important consequences for theories about language dynamics if modeling is truly to be worthwhile. Our main claim is that the stochastic process of neutral evolution (also known as genetic drift or random copying) is a viable null model for language dynamics. We survey empirical evidence in favor and against neutral evolution as a mechanism behind historical language changes, highlighting the theoretical implications in each case.

We investigate behavior of the continuous stochastic signals above some threshold, bursts, when the exponent of multiplicativity is higher than one. Earlier we have proposed a general nonlinear stochastic model applicable for the modeling of absolute return and trading activity in financial markets which can be transformed into Bessel process with known first hitting (first passage) time statistics. Using these results we derive PDF of burst duration for the proposed model. We confirm derived analytical expressions by numerical evaluation and discuss bursty behavior of return in financial markets in the framework of modeling by nonlinear SDE.

Many biological systems are genuinely hybrids consisting of interacting discrete and continuous components and processes that often operate at different time scales. It is therefore desirable to create modeling frameworks capable of combining differently structured processes and permitting their analysis over multiple time horizons. During the past 40 years, Biochemical Systems Theory (BST) has been a very successful approach to elucidating metabolic, gene regulatory, and signaling systems. However, its foundation in ordinary differential equations has precluded BST from directly addressing problems containing switches, delays, and stochastic effects. In this study, we extend BST to hybrid modeling within the framework of Hybrid Functional Petri Nets (HFPN). First, we show how the canonical GMA and S-system models in BST can be directly implemented in a standard Petri Net framework. In a second step we demonstrate how to account for different types of time delays as well as for discrete, stochastic, and switching effects. Using representative test cases, we validate the hybrid modeling approach through comparative analyses and simulations with other approaches and highlight the feasibility, quality, and efficiency of the hybrid method.

Jumps can be seen in many natural processes. Classical deterministic modeling approach explains the dynamical behavior of such systems by using impulsive differential equations. This modeling strategy assumes that the dynamical behavior of the whole system is deterministic, continuous, and it adds jumps to the state vector at certain times. Although deterministic approach is satisfactory in many cases, it is a well-known fact that stochasticity or uncertainty has crucial importance for dynamical behavior of many others. In this study, we propose to include this abrupt change in the stochastic modeling approach, beside the deterministic one. In our model, we introduce jumps to chemical master equation and use the Gillespie direct method to simulate the evolutionary system. To illustrate the idea and distinguish the differences, we present some numerically solved examples.

To correlate the mechanical properties of granular porous materials with their microstructure, typically porosity is being considered as the dominant parameter. In this work, we suggest the average coordination number, i.e., the average number of connections that each grain of the porous material has to its neighboring grains, as additional — and possibly even more fundamental — microstructural parameter. In this work, a combination of stochastic and mechanical modeling is applied to study microstructural influences on the elastic properties of porous ceramics. This is accomplished by generating quasi-two-dimensional (2D) and fully three-dimensional (3D) representative volume elements (RVEs) with tailored microstructural features by a parametric stochastic microstructure model. In the next step, the elastic properties of the RVEs are characterized by finite element analysis. The results reveal that the average coordination number exhibits a very strong correlation with the Young’s modulus of the material in both 2D and 3D RVEs. Moreover, it is seen that quasi-2D RVEs with the same average coordination number, but largely different porosities, only differ very slightly in their elastic properties such that the correlation is almost unique. This finding is substantiated and discussed in terms of the load distribution in microstructures with different porosities and average coordination numbers.

This paper presents the integrated simulation for earthquake hazard and disaster prediction. The earthquake hazard simulation takes advantage of the macro-micro analysis method which estimates strong ground motion with high spatial and temporal resolution. The earthquake disaster simulation calculates seismic responses for all structures in a target area by inputting synthesized strong ground motion to a structure analysis method which is plugged into the system; a suitable analysis method, linear or non-linear, is chosen depending on the type of the structure. The results of all simulations are visualized so that government officials and residents can share common recognition of possible earthquake hazard and disaster. Two examples of this integrated earthquake simulations are presented; one is made by plugging nonlinear structure analysis methods into the system, and the other is made for an actual city, the computer model of which is constructed with the help of available geographical information systems.

The efficient treatment of uncertainties in soil properties is essential for seismic probabilistic risk assessment. Recently, we developed a stochastic elasto-plastic model based on the first-order spectral expansion of the stochastic behavior. This new treatment is applied to elasto-plastic soil with uncertain material properties. A numerical experiment is performed to evaluate the probabilistic constitutive relation. The probabilistic stress–strain relation of soil is simulated successfully in agreement with the standard Monte-Carlo simulation. In addition, some characteristics of the probabilistic elasto-plastic soil are studied.

When a vehicle manufacturer designs a contract with a rental vehicle company, it is important for the OEM to properly understand the rental company’s economic preference. While it is usually not directly observable, the economic preference of the counter party can often be revealed indirectly through some observable market behavior. In such cases, econometric inference needs to be used. In this paper, we use the de-fleeting process of the rental vehicle company as the inferential apparatus. To this end, we first develop a model to describe the decision-making in the de-fleeting process for the rental vehicle company, based on the optimal stopping theory. We then outline an econometric procedure to estimate the model parameters. Finally, we use simulated data to illustrate how to deal with some of the technical issues that one might encounter when the procedure is applied to real data.

In wavefront sensing, an accurate likelihood model offers a reliable way to relate directly a chosen set of global parameters of interest (e.g. the actuator signals of a deformable mirror) to the fundamental data (e.g. the CCD pixel photoelectrons in the wavefront sensor), without lossy pre-processing stages like centroid calculation or wavefront reconstruction. We have shown, through numerical simulations, that at low light levels maximum likelihood estimation can offer advantages in residual wavefront error against traditional least-square estimation from centroid data. We discuss the importance of nuisance parameters in the likelihood model.