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Increased technological methods have enabled the investigation of biology at nanoscale levels. Such systems require the use of computational methods to comprehend the complex interactions that occur. The dynamics of metabolic systems have been traditionally described utilizing differential equations without fully capturing the heterogeneity of biological systems. Stochastic modeling approaches have recently emerged with the capacity to incorporate the statistical properties of such systems. However, the processing of stochastic algorithms is a computationally intensive task with intrinsic limitations. Alternatively, the queueing theory approach, historically used in the evaluation of telecommunication networks, can significantly reduce the computational power required to generate simulated results while simultaneously reducing the expansion of errors. We present here the application of queueing theory to simulate stochastic metabolic networks with high efficiency. With the use of glycolysis as a well understood biological model, we demonstrate the power of the proposed modeling methods discussed herein. Furthermore, we describe the simulation and pharmacological inhibition of glycolysis to provide an example of modeling capabilities.

This work regards the simulation of the spread of the COVID-19 disease in a community by applying the deterministic and stochastic Susceptible-Exposed-Infective-Recovered (SEIR) epidemic models. The developed computational method for the stochastic variant allows to realistically simulate the spread of COVID-19 in a medium-sized community and to study the effect of preventive measures such as quarantine and vaccination. The results of the simulations are compared with the deterministic version of the SEIR model. The comparison makes it possible to conclude that the epidemic outbreak can be prevented even though the basic reproduction number is greater than one. UNIAG, unidade de I&D financiada pela FCT, Ministerio da Ciencia, Tecnologia e Ensino Superior. Projeto n°. UIDB/04752/2020. info:eu-repo/semantics/publishedVersion

Stochastic simulation models are increasingly popular for analyzing complex stochastic systems. However, the input distributions driving the simulation models are typically unknown in practice and are usually estimated from real world data. Since the size of real world data tends to be limited, the resulting estimation of input distribution will contain errors. This estimation error is commonly known as input uncertainty. In this paper, we consider the stochastic simulation optimization problem when the input uncertainty is present and assume that both the family and parameters of the input distribution are unknown. Traditional efficient metamodel-based optimization approaches like Efficient Global Optimization (EGO) do not take the input uncertainty into account. This can lead to sub-optimal decisions when the input uncertainty level is high. Here, we adopt a nonparametric Bayesian approach to model the input uncertainty and propose an EGO-based simulation optimization algorithm that explicitly accounts for the input uncertainty.

In the traditional teaching mode and method of public physical education practice in colleges and universities, the technical teaching and skill training of students with basic techniques and tactics as the main content, focusing on the unity and standardization of teaching forms, structures, contents, methods, assessments, evaluations, etc. Given the new goals and requirements given by national sports to college sports currently, the limitations and one-sidedness of the old teaching modes and methods have emerged. In view of the absoluteness of distinguishing superiority or inferiority in traditional sports teaching evaluations, and the inconsistency problem of multiple evaluation conclusions, an advantage evaluation method highlighting own strengths is established. In the evaluation, a stochastic simulation algorithm is used to determine the advantages of evaluation objects by calculating the superiority among the evaluation objects. The validity of the proposed method is verified by numerical examples, and the evaluation conclusion with probability information is obtained.

Due to environmental conditions as well as internal processes, the lack of long-term stability of electrochemical gas sensors poses a severe problem with respect to their applications, e.g. in tracking air quality on a large scale. Thus far, the development of suitable algorithms to face these problems relies on long-term datasets obtained from sufficiently good reference devices. Since such measurements on actual sensor systems are not always available, especially in the development phase of them, simulated approaches would be a great benefit for algorithm development and the further analysis of the sensors. Those simulators, however, require proper models to capture the general principles of the functionalized materials in such sensor arrays. In this work, we propose a stochastic model that can be used for this purpose, i.e. that allows for simulating the behavior of graphene-based electrochemical gas sensors in particular. The proposed approach allows to properly map different material-related microscopic effects on the sensor surface to a signal output. Evaluations show that the proposed model is able to capture the drift dynamics of such sensors in particular when comparing the results to real measurement data.

In line with the increasing number of electric vehicles, a stochastic approach that simulates the charging stations which is managed hybridlike system of a solar array is discussed in this study. A smart charging system algorithm is designed to control the charging status of electric vehicles arriving at a parking lot with a certain vehicle capacity based on the real data. Since charging electric vehicles can become an extra burden on the power grid, a smart charging algorithm is needed for stability and security. By solar energy, electric vehicles can be charged, and the surplus can be injected back to the grid. Otherwise, electricity can be provided from the grid. A stochastic-based simulation is developed to generate the necessary big-data based on realistic assumptions. The installation parameters of the solar energy, the number of vehicles in the parking lot, the hours of arrival, the duration of stay, and the charging status are simulated in various scenarios. This research aims to meet the demand for the increasing number of electric vehicles with the smart charging algorithm in the areas where have high solar energy potential.

In real applications, decision makers usually make use of simulation models to study the real-world complex systems and make optimal decisions. Simulation optimization is a procedure to find optimal design via simulation model. When the simulation model itself is complex and time-consuming to run, it will be preferable to use fewer simulation runs to find optimal design. Under this setting, Gaussian process based optimization algorithms, including the Efficient Global optimization (EGO) and the Informational Approach to Global optimization (IAGO) have been well developed for both deterministic and stochastic simulation optimization problems. Recently, the EGO algorithm was extended to solve the stochastic simulation optimization problem when input uncertainty is considered. However, the stochastic IAGO algorithm with input uncertainty has not yet been carefully studied. This paper contributes to refining the current IAGO algorithm to make it applicable for simulation optimization when homoscedastic stochastic noise and input uncertainty are considered. Details about the model formulation and the refined IAGO algorithm are provided. Numerical comparisons between EGO with input uncertainty and IAGO with input uncertainty show that IAGO with input uncertainty can find the optimal design more accurately when budget constraints are present.

Plug-in electric vehicles (PEVs) have gained popularity in the Gulf Cooperation Council countries as they improve local air quality and are seen as a tool to diversify carbon-based economies. However, power grids in the region are low-inertia systems and often strained by heavy air-conditioning load. In this paper, we present a stochastic simulation study to assess the impacts of PEV charging on the power generation under various penetration and charging schedule scenarios in the State of Qatar. Penetration ratios are obtained from new PEV sales statistics from four different countries, namely Norway, Netherlands, France, and China. Actual 5-min electricity load profiles are used and peak-consumption day is chosen to evaluate the worst-case scenario. The results show that current power generation in Qatar has enough spare capacity to fuel up vehicles, however, uncontrolled PEV charging may require additional peak-power generators if the government goal of 10% PEVs penetration rate is reached. Finally, recommendations for solar-PEV charging is provided by analyzing the actual load and global horizontal irradiance measurements.

The aggregation of flexible resources in demand side demonstrates a great potential to address the challenges brought by wind power integration. In this paper, we presented a probabilistic method to assess the synthetic benefits of demand response (DR) programs for power system operation under wind power uncertainty. First, the price-based DR programs are described through the elaborated modeling of demand-price elasticity and consumers satisfaction. Then, a short-term stochastic simulation method is proposed, where the randomness of wind generation is captured by Latin Hypercube sampling, and a two-stage stochastic program framework is developed to simulate the consumption re-scheduling in response to dynamic electricity tariffs. Moreover, the linearization technique is introduced to convexity the consumers satisfaction constraints, so that the assessment model converts to a computationally friendly mixed integer linear program (MILP). The simulation outcomes yield the DR performance metrics in several aspects, e.g., economics, renewable energy utilization, and reserve capacity adequacy. Numerical results validate the effectiveness of the proposed method, which demonstrates the benefits of DR by significantly improving the wind power utilization with a reduced operational cost and guaranteed consumers satisfaction.

Stochastic simulation of biological systems becomes widely used, since it can intuitively account for the fluctuation of species with a few number of molecules. However, for bigger models and/or models with mixed abundance of molecules, stochastic simulation fails to produce the required results in reasonable time. Parallel simulation can offer a solution for this challenge. Nevertheless, currently available parallel software tools either provide a coarse-grained parallelization or a general-purpose fine-grained parallel simulation of the well-known stochastic simulation algorithm (SSA). The former can only take advantage of parallel processing if multiple runs have to be performed, while the latter requires extensive synchronization and communication between the different processing nodes each time a reaction is to fire. In this paper, a fine-grained parallelization approach is presented that takes advantage of the underlying model semantics to improve the simulator performance. The proposed method is applied to the yeast cell cycle regulation, which is an example of biological models that requires extensive investigation.

The Human Immune System (HIS) plays a fundamental role in the defense of the body against diseases. However, the multi-scale interactions among several of its components make the complete understanding of the mechanisms involved in the body defense a complex task. Mathematical and computational tools can help towards this goal. Lots of works use differential equations, agent-based models and cellular automaton to describe the behavior of the HIS. In this work, we use the Gillespie stochastic simulation algorithm to model the stochastic nature of the HIS response to Yellow Fever vaccine.

International audience; This paper presents a method to automatically create stochastic simulation models of clinical pathways from raw databases. We introduce an automatic procedure to convert a process model, discovered with process mining, into an actionable simulation model. The concept of state charts is used and enriched to incorporate the distinctive features of healthcare processes into the model. The clinical pathway model is used to simulate new patients' sequence of events. The resulting model is validated by comparing key performances indicators with historical data. Finally, we use the model to perform an automatically setup sensitivity analysis. The whole process is automated and can be used with any input data.

The focus of my Phd project is on the evaluation of the dependability of critical infrastructures, e.g. energy distribution and gas networks. The modeling formalism of hybrid Petri nets with random variables forms the basis of our modeling approach. We plan to rigorously evaluate dependability using discrete-event simulation and methods of Statistical Model Checking and extend existing approaches with methods of rare-event simulation.

We investigate the possibility of performing stochastic simulation of a synthetic gene circuit that includes a cell-to-cell communication system with an intracellular feedback control circuit. We propose an implementation of the CLE stochastic simulation method that makes possible to simulate gene synthetic circuits involving cell-to-cell communication. We find the minimum number of samples ensuring the statistic moments over the population do not degrade. This allows us to reduce the total simulation time and improve the efficiency of the method. Also we reveal that one realization of the population of interconnected cells, provided there is enough time to perform the time average, is useful to obtain representatives of the long-term moments of the population. Finally we show the potential of the improved approach by performing an analysis of the influence of the parameters in the LuxR promoter on the noise strength of the population.

Pore size distribution is the key factor of the nonwoven geotextiles for various functions including filtration, separation and reinforcement. The comparison of pore size distributions of spunbond-needle punched nonwoven geotextiles has been made among the theoretical, experimental and the virtual study based on a stochastic simulation. Moreover, the influence of fiber diameter, solid volume fraction, and the thickness of the nonwoven geotextiles on the pore size distribution of the nonwoven geotextiles has been investigated. It is demonstrated that the pore size distribution, which obtained using Geodict® based on a stochastic simulation, shows a good agreement with experimental data and theoretical, and it is feasible for the prediction of pore size distribution in spunbond-needle punched nonwoven geotextiles. The results show that increasing the solid volume fraction would decrease the pore size of the nonwoven geotextiles under the same filament diameter and same thickness of the webs. Increasing fiber diameter would cause the pore size decreasing, while thickness and solid volume fraction kept constant also found. The results also show that the pore size decreasing with the thickness of the nonwoven geotextiles increasing, while the constant of the solid volume fraction and filaments diameter.

In power systems with a high share of renewable feed-in, the understanding of the behavior of distributed loads and generation units and their electrical flexibility potential is of high significance. Thermal storage systems, especially in domestic heating systems, such as heat pumps (HP) or micro-CHP units, offer short- and medium-term flexibility options. The flexibility potential of individual units highly depends on the individual thermal load profile. Nevertheless, due to the high effort of measuring the thermal energy consumption of heating circuits, applicable thermal load profiles can rarely be found. As a result, this paper introduces a model for the simulation of heat demand profiles with special regard to stochastic noise. The analyzed data set consists of the measured profiles of the thermal consumption of 13 residential buildings. By means of a statistical evaluation of the heating circuits' power consumption and adaption of a stochastic differential equation, a model for the simulation of individual thermal load profiles is developed. Furthermore, the paper demonstrates the influence of stochastic thermal load profiles on the operation and the flexibility potential of CHP units.

The design of clothing supply chain network is one of the most key issues in clothing supply chain management. This article proposes a multi-echelon clothing supply chain network design model with uncertain price. The model includes suppliers of fabric, suppliers of accessory, clothing factories, distributors, and customer zones and makes managers of clothing supply chain network to maximize the total profits. A method which integrates simulation with chance-constrained programming is used to deal with the model. This method is employed to determine the configuration of clothing supply chain network. And the best value of any confidence level can be obtained using this method and CPLEX 12.6. The results of computational experiments show the proposed model is effective and pragmatic in designing clothing supply chain network under uncertainty.

This paper presents an improved stochastic simulation method for calculating current dependent energy losses in distribution networks. The method is based on power load curves and integrates the stochastic nature of the load curves with power and voltage covariance matrices. The method reduces calculation effort using the factor analysis of covariance matrices and provides a few quantities needed to calculate energy losses. The method has no limitation for network configuration and gives accurate results several times faster than other existing methods. Therefore, it is appropriate for considering losses in optimization and decision making purposes in operating and planning of distribution networks.

A stochastic algorithm for simulation of fluctuation-induced enzyme kinetics is developed. The method is generally well applicable when the reactions occur in low-dimensional and disordered media such as biological ones. We suggest a generalization of the Michaelis — Menten scheme of enzyme kinetics that is extended to simulate the quantum tunneling phenomena in the catalytic cycles of enzymatic processes. The stochastic method is suggested as a generalization of the technique developed in our recent studies [12], [13] where this method was developed to describe the annihilation of spatially separate electrons and holes in a disordered semiconductor. The stochastic technique is based on the spatially inhomogeneous, nonlinear integro-differential Smoluchowski equations with random source term. We focus in this study on the spatial distribution, and numerically investigate the segregation in the case of a source with a continuous generation in time and randomly distributed in space. The stochastic particle method presented is based on a probabilistic interpretation of the underlying process as a stochastic Markov process of interacting particle system in discrete but randomly progressed time instances. The segregation is analyzed through the correlation analysis of the vector random field of concentrations which appears to be isotropic in space and stationary in time.

Synthetic Biology aspires to design, compose and engineer biological systems that implement specified behaviour. When designing such systems, hypothesis testing via computational modelling and simulation is vital in order to reduce the need of costly wet lab experiments. As a case study, we discuss the use of computational modelling and stochastic simulation for engineered genetic circuits that implement Boolean AND and OR gates that have been reported in the literature. We present performance analysis results for nine different state-of-the-art stochastic simulation algorithms and analyse the dynamic behaviour of the proposed gates. Stochastic simulations verify the desired functioning of the proposed gate designs.\ud

Semivariograms and its parameters have a significant impact on Kriging interpolation and stochastic simulation. This paper used GS+ and ArcGIS to research the heavy metal of Yichang citrus orchard, which used spherical model, Gaussian model, exponential model and linear model to fit the experimental semivariograms and applied these models in the Sequential Gaussian Simulation. The stochastic simulation results of different models show that the average of Hg is over the national standard of soil quality, so there are some pollution. The best model is exponential model which can keep semivariograms isomorphic before and after the simulation. which is different from the model selected by the largest coefficient of determination, so during stochastic simulation, the appropriate model should be selected by the comparison of the simulation results of various model.

A new approach for simulation of delay-Doppler maps for ocean global navigation satellite system reflectometry is presented. The simulator is based on a stochastic forward scattering model for generation of realizations of the sea surface reflected signal and allows a flexible definition of system and environmental parameters. The signal scattered from a delay Doppler cell is modelled as the coherent random sum of a large number of components from specular points; the sum converges to a compound-Gaussian random variable when the mean number of points is sufficiently large. In the proposed simulation process, the discretization of the DDM arises from a natural sampling of the delay-Doppler domain rather than on sampling the ocean surface. Instantaneous DDMs are simulated and consistency with the theoretical model is verified.

Recently, hybrid models have emerged that combine microscopic and mesoscopic regimes in a single stochastic reaction-diffusion simulation. Microscopic simulations track every individual molecule and are generally more accurate. Mesoscopic simulations partition the environment into subvolumes, track when molecules move between adjacent subvolumes, and are generally more computationally efficient. In this paper, we present the foundation of a multi-scale stochastic simulator from the perspective of molecular communication, for both mesoscopic and hybrid models, where we emphasize simulation accuracy at the receiver and efficiency in regions that are far from the communication link. Our multi-scale models use subvolumes of different sizes, between which we derive the diffusion event transition rate. Simulation results compare the accuracy and efficiency of traditional approaches with that of a regular hybrid method and with those of our proposed multi-scale methods., 7 pages, 2 tables, 6 figures. Will be presented at the 2015 IEEE International Conference on Communications (ICC) in June 2015

The advancement of single-cell technologies has shown that stochasticity plays an important role in many biochemical reaction networks. However, our ability to investigate this stochasticity using mathematical models remains rather limited. The reason for this is that computing the time evolution of the probability distribution of such systems requires one to solve the chemical master equation (CME), which is generally impossible. Therefore, many approximate methods for solving the CME have been proposed. Among these one of the most prominent is the finite state projection algorithm (FSP) where a solvable system of equations is obtained by truncating the state space. The main limitation of FSP is that the size of the truncation which is required to obtain accurate approximations is often prohibitively large. Here, we propose a method for approximating the solution of the CME which is based on a combination of FSP and Gillespie's stochastic simulation algorithm. The important advantage of our approach is that the additional stochastic simulations allow us to choose state truncations of arbitrary size without sacrificing accuracy, alleviating some of the limitations of FSP.

Biochemical systems can be described by biochemical reactions. Biochemical reactions can be investigated through mathematical modeling and stochastic simulations. Deterministic and stochastic models are two basic categories of models for biochemical reactions. Due to transmembrane transportation of biochemical species and delayed degradation, time delays are ubiquitous in coupled biochemical systems. Therefore, models for biochemical reactions can be further classified into delayed and un-delayed ones. For biochemical systems without delays, researchers have established the connections between deterministic models and stochastic models directly from the deterministic ones. For delayed biochemical systems, researchers have proposed some stochastic simulation methods to cope with biochemical reactions with time delays. However, the existing delayed stochastic simulation algorithms (SSA) are all incapable of realizing the comparison between highly nonlinear deterministic delayed models and stochastic models directly from the the deterministic ones. In this paper, we proposed a delayed SSA, which can realize the comparison between deterministic models and its stochastic counterparts. Furthermore, one can also use the algorithm to investigate intrinsic noise-induced behaviors, and the effect of system volumes. Several numerical examples show the effectiveness and correctness of our algorithm.