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A spectral criterion involving the model parameters is given for the existence and uniqueness of a periodically correlated and seasonal non‐negative integer‐valued autoregressive process. The structure of the mean and covariance functions of the periodically stationary distribution of the model is derived using its implicit state‐space representation. Two infinite series representations for the process, the moving average, and the immigrant generation, are established. Based on the latter representation, a novel and parallelizable simulation method is proposed to generate the process. [ABSTRACT FROM AUTHOR]

Three major sweet potato pest species, sweet potato weevil Cylas formicarius (Fabricius) (Coleoptera: Brentidae), West Indian sweet potato weevil Euscepes postfasciatus (Fairmaire) (Coleoptera: Curculionidae) and sweet potato vine borer, Omphisa anastomosalis (Guenée) (Lepidoptera: Crambidae), were examined for their utilization patterns and spatial distribution in the wild host plant, blue morning glory, Ipomoea indica ((Burm.) Merr.) (Solanales: Convolvulaceae) in Okinawa, Japan. We investigated 18,720 samples of I. indica and showed that 649 individuals of C. formicarius were found at an infestation rate per 1 m of the vine of 2.44%, which was significantly higher at height levels 0 m than the other levels (0–1 m, 1–2 m and 2–3 m), and tended to be higher at height level >3 m. In the case of E. postfasciatus, 131 individuals were found, and its infestation rate (0.37% on average) was significantly higher at height levels 0–1 m than the other level (2–3 m), and tended to be higher at height level >3 m. In the case of O. anastomosalis, 65 individuals were found and its infestation rate (0.32%) was significantly higher at the 0–3 m height level than at the other level (0 m). The results of analysis of the spatio-temporal dynamics of populations within and across species showed that the pests are randomly distributed across the study area throughout the year. Stochastic simulations showed that E. postfasciatus and the other two species tend to infest the same vines, but C. formicarius and O. anastomosalis tend to infest different vines. We provide valuable information on the distribution patterns of these pests that can their effectively control. [ABSTRACT FROM AUTHOR]

Accurate damage assessment after an earthquake is crucial for effective emergency response. Using ground motion information enables rapid building damage assessment when detailed damage data are unavailable. While uncertainty in earthquake parameters plays a significant role in the accuracy of rapid estimations, it is usually treated as a constant parameter rather than as a dynamic parameter that considers the amount of ground motion data collected that evolve over time. This work investigates the impact of incorporating evolving ground motion uncertainty in ground motion estimations from US Geological Survey's (USGS) ShakeMap on post-disaster damage assessments from two methodologies: the revised Thiel–Zsutty (TZR) model and Federal Emergency Management Agency's (FEMA) Hazus. Using data from the 2020 Indios earthquake in Puerto Rico and the 2014 Napa earthquake, we find that changes in uncertainty in estimates of peak ground acceleration reach 65% between early and late versions of the ShakeMap. We propose a process to integrate this evolution with the two damage assessment methodologies through a Monte Carlo simulation-based approach, demonstrating that it is critical to introduce dynamic ground motion uncertainty in the damage assessment process to avoid propagating unreliable measures. Both methodologies show that resulting damage estimates can be characterized by narrower distributions, indicative of reduced uncertainty and increased precision in damage estimates. For the TZR model, an improved estimate of post-disaster loss is achieved with narrower bounds in distributions of expected high scenario loss. For Hazus, the results show potential changes in the most probable damage state with an average change of 13% in the most probable damage state. The described methodology also demonstrates how uncertainty in the resulting damage state distributions can be reduced compared with the use of the current Hazus methodology. [ABSTRACT FROM AUTHOR]

Background: Stochastic modelling plays a crucial role in comprehending the dynamics of intracellular events in various biochemical systems, including gene-expression models. Cell-to-cell variability arises from the stochasticity or noise in the levels of gene products such as messenger RNA (mRNA) and protein. The sources of noise can stem from different factors, including structural elements. Recent studies have revealed that the mRNA structure can be more intricate than previously assumed. Results: Here, we focus on the formation of stem-loops and present a reinterpretation of previous data, offering new insights. Our analysis demonstrates that stem-loops that restrict translation have the potential to reduce noise. Conclusions: In conclusion, we investigate a structured/generalised version of a stochastic gene-expression model, wherein mRNA molecules can be found in one of their finite number of different states and transition between them. By characterising and deriving non-trivial analytical expressions for the steady-state protein distribution, we provide two specific examples which can be readily obtained from the structured/generalised model, showcasing the model's practical applicability. [ABSTRACT FROM AUTHOR]

Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies and assist in decision-making. In addition to simulations, visualization of a Pareto frontier for multiple competing breeding objectives can assist breeders in decision-making. This paper introduces Python Breeding Optimizer and Simulator (PyBrOpS), a Python package capable of performing multi-objective optimization of breeding objectives and stochastic simulations of breeding pipelines. PyBrOpS is unique among other simulation platforms in that it can perform multi-objective optimizations and incorporate these results into breeding simulations. PyBrOpS is built to be highly modular and has a script-based philosophy, making it highly extensible and customizable. In this paper, we describe some of the main features of PyBrOpS and demonstrate its ability to map Pareto frontiers for breeding possibilities and perform multi-objective selection in a simulated breeding pipeline. [ABSTRACT FROM AUTHOR]

Assessing the risk of landslides as a result of intense seismic vibrations is an urgent problem in engineering geology. In this article, to assess slope stability, a method is proposed for normalizing the internal deformation of a slope, based on a probabilistic-stochastic approach. The cumulative Newmark displacement is considered as a normalized value, for which empirical relationships have been selected among the accumulated displacement, the level of seismic impact, and the critical acceleration specified by the material parameters of the slope. The method takes into account the uncertainties in the position of possible earthquake sources in the next 50 years in the vicinity of the studied slope, the magnitude of the event(s), and the level of seismic impact. The uncertainties in the physicomechanical parameters of the slope are also taken into account. Normative biases are estimated using reference exceedance probabilities (10 and 5%). The obtained values are compared with the threshold characteristics, and based on this, a decision is made on the slope stability to seismic loads. The value of 10 cm was taken as the lower threshold at which the slope can be considered stable. The technique was successfully tested on a well-studied area of the western slope of Mt. Bolshevik (the south of Sakhalin Island). The balanced estimate of the normative displacement for a 5% exceedance probability was slightly less than 10 cm. The study also provides recommendations for further improvement of the methodology. [ABSTRACT FROM AUTHOR]

Predicting ground motions due to induced seismicity is a challenging task owing to the scarcity of data and heterogeneity of the uppermost crust. Dealing with this requires a thorough understanding of the underlying physics and consideration of inter-site variability. The most common ground motion model used in practice is the parametric ground motion prediction equation (GMPE), of which hundreds exist in the literature. However, relatively few are developed with a focus on induced seismicity. Developing GMPEs that are specific to an appropriate magnitude-distance range ( R < 30 km; 2 ≤ M ≤ 6 ) is important for induced seismicity applications. This paper proposes a framework for the development of physically-based GMPEs to provide more accurate and reliable estimates of the potential induced-seismicity ground motion hazard, allowing for better risk assessment and management strategies. To demonstrate this approach, a new set of GMPEs for the 2018-2019 induced seismicity sequence at the Preston New Road (PNR) shale gas site near Blackpool, United Kingdom, is presented. The physically-based GMPE was developed based on a pseudo-finite-fault stochastic ground motion simulation, calibrated with parameters derived from the spectral analysis of weak-motion records from induced seismic events. An optimization-based calibration technique using the area metric (AM) was subsequently performed to calibrate optimal parameters for simulating ground motion at the PNR site. Finally, using a suite of forward simulations for events with 1 ≤ M ≤ 6 recorded at distances up to 30 km, combined with empirical data, a location-specific GMPE was derived through adjustment of an existing model. [ABSTRACT FROM AUTHOR]

Infectious diseases are a major threat to Asian shrimp aquaculture, as they proliferate at system level rather than only the individual level. We assess the impact of various forms of cooperation among Vietnamese farmers on yield risks caused by white spot disease and acute hepatopancreatic necrosis disease. Using a stochastic simulation model, we simulate shrimp farming yield risks based on input from two expert workshops. The results provide a relative comparison of expected yield losses caused by both diseases comparing a baseline scenario (no cooperation) and three scenarios with varying degrees of synchronization and information sharing across farms. Results show lower expected yield losses in all three cooperation scenarios in comparison with the farm-based scenario, highlighting the value of synchronization and information sharing practices to mitigate yield losses. We discuss the potential this has to reduce systemic risks in aquaculture, thereby potentially incentivizing the reintroduction of risk-sharing mechanisms. [ABSTRACT FROM AUTHOR]

The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program. [ABSTRACT FROM AUTHOR]

During cell division, the mitotic spindle moves dynamically through the cell to position the chromosomes and determine the ultimate spatial position of the two daughter cells. These movements have been attributed to the action of cortical force generators which pull on the astral microtubules to position the spindle, as well as pushing events by these same microtubules against the cell cortex and plasma membrane. Attachment and detachment of cortical force generators working antagonistically against centring forces of microtubules have been modelled previously (Grill et al. in Phys Rev Lett 94:108104, 2005) via stochastic simulations and mean-field Fokker–Planck equations (describing random motion of force generators) to predict oscillations of a spindle pole in one spatial dimension. Using systematic asymptotic methods, we reduce the Fokker–Planck system to a set of ordinary differential equations (ODEs), consistent with a set proposed by Grill et al., which can provide accurate predictions of the conditions for the Fokker–Planck system to exhibit oscillations. In the limit of small restoring forces, we derive an algebraic prediction of the amplitude of spindle-pole oscillations and demonstrate the relaxation structure of nonlinear oscillations. We also show how noise-induced oscillations can arise in stochastic simulations for conditions in which the mean-field Fokker–Planck system predicts stability, but for which the period can be estimated directly by the ODE model and the amplitude by a related stochastic differential equation that incorporates random binding kinetics. [ABSTRACT FROM AUTHOR]

Abstract Background Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model’s dynamics may take a prohibitively long time. Results We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system’s dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently. Conclusion We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems.

Abstract Background Stochastic modelling plays a crucial role in comprehending the dynamics of intracellular events in various biochemical systems, including gene-expression models. Cell-to-cell variability arises from the stochasticity or noise in the levels of gene products such as messenger RNA (mRNA) and protein. The sources of noise can stem from different factors, including structural elements. Recent studies have revealed that the mRNA structure can be more intricate than previously assumed. Results Here, we focus on the formation of stem-loops and present a reinterpretation of previous data, offering new insights. Our analysis demonstrates that stem-loops that restrict translation have the potential to reduce noise. Conclusions In conclusion, we investigate a structured/generalised version of a stochastic gene-expression model, wherein mRNA molecules can be found in one of their finite number of different states and transition between them. By characterising and deriving non-trivial analytical expressions for the steady-state protein distribution, we provide two specific examples which can be readily obtained from the structured/generalised model, showcasing the model’s practical applicability.

The present-day road traffic with the persistent change in the type and volume of vehicles needs to be specifically investigated for effective safety management of aging highway infrastructures. Actual traffic data can be implemented in refined procedures for stochastic simulation of road infrastructure performance, structural health monitoring (SHM), definition of weight limits on highways, and traffic-informed structural safety checks. While weigh-in-motion (WIM) systems had been widely used in many countries, their installation on Italian highways was mostly discussed and carried out only after the catastrophic collapse of the Polcevera bridge in 2018. This study presents a statistical data analysis, probabilistic models, and a simulation procedure for highway traffic, based on measurements of two WIM systems located along European route E45 close to Naples, Italy. Different limitations to maximum gross vehicle weight (GVW) were enforced at the locations of the two WIM systems, according to the Italian road code and the Italian guidelines for risk classification, safety assessment and monitoring of existing bridges, respectively. WIM data sets were filtered to exclude erroneous traffic data and vehicle classes defined according to the number of axles and axle distance were statistically characterised, allowing the derivation of probabilistic models for all traffic parameters of interest. A simulation methodology to generate random traffic load from the WIM data is also presented for its possible use in probabilistic performance assessment and traffic informed SHM of road infrastructures such as bridges. [ABSTRACT FROM AUTHOR]

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements. [ABSTRACT FROM AUTHOR]

Aiming at the design problem of aviation swarm combat course of action (COA), considering the influence of stochastic parameters in the causal relationship model and optimization problem model, according to the dynamic influence net (DIN) theory, stochastic simulation technique, feedforward neural network (FNN) function approximation technique and multi-objective artificial fish school algorithm (MOAFSA), this paper proposed a COA optimized method based on DIN and multi-objective stochastic chance constraint optimization for aviation swarm combat. First, on the basis of establishing the overall framework of the model and defining the elements of causal relationship modeling, the static and dynamic causal relationship modeling and optimization problem modeling were carried out respectively. Second, the probability propagation mechanism of DIN was established, which mainly included two aspects, i.e., the overall process and the specific algorithm. Then, input and output data were generated based on stochastic simulation. According to these data, FNN was adopted for function approximation, and MOAFSA was adopted for iterative optimization. Finally, the rationality of the model, and the effectiveness and superiority of the algorithm were verified through multiple sets of simulation cases. [ABSTRACT FROM AUTHOR]

Evaluation of urban environmental sustainability is an important prerequisite for scientific decision-making of urban sustainability. This paper proposed an evaluation method of urban environmental sustainability based on the integration of multi-improvement demands. Considering the interaction between the economic and social systems on the environmental system, this paper constructed an urban environmental sustainability evaluation indicator system from three dimensions: environmental foundation, environmental pollution, and environmental protection. Combined with the actual improvement demands of each city, a corresponding weighting method was constructed, and the weights of each city's individualized improvement demands were obtained through the analysis of indicator data. Further, in order to consider the fairness and guidance of the evaluation, the corresponding random simulation algorithm was matched for the individualized weights, and the evaluation value and the superiority probability in a stable state after multiple simulations were sought. Based on this, this paper conducted an empirical evaluation of urban environmental sustainability of the 14 cities in Liaoning Province, China. Through the demand-driven evaluation model, cities can obtain improvement suggestions that meet their demands while understanding their environmental sustainability level. It is convenient for top-down policy guidance and bottom-up practical exploration. [ABSTRACT FROM AUTHOR]

Great deviations in building energy consumption simulation are attributed to the simplified settings of occupants' air conditioning (AC) usage schedules. This study was designed to develop a method to quantify the uncertainty and randomness of AC usage behavior and incorporate the model into simulations, in order to improve the prediction performance of AC energy consumption. Based on long-term onsite monitoring of household thermal environments and AC usage patterns, two stochastic models were built using unsupervised cluster and statistical methods. Based on the Monte Carlo method, the AC operation schedule was generated through AC opening duration, setpoints, and other relevant parameters, and was further incorporated into EnergyPlus. The results show that the ideally deterministic AC operation settings from the standard significantly overestimate the cooling energy consumption, where the value based on the fixed mode was 6.35 times higher. The distribution of daily AC energy consumption based on the stochastic modeling was highly consistent with the actual situation, thanks to the accurate prediction of the randomness and dynamics of residents' AC usage patterns. The total cooling energy consumption based on two stochastic models was found to be much closer to the actual values. The work proposes a method of embedding stochastic AC usage models to EnergyPlus 22.1 benefits for an improvement in building energy consumption simulation and the energy efficiency evaluation regarding occupant behavior in the future. [ABSTRACT FROM AUTHOR]

Background: Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model’s dynamics may take a prohibitively long time. Results: We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system’s dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently. Conclusion: We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems. [ABSTRACT FROM AUTHOR]

In the first range-wide population viability model for the northwestern and southwestern pond turtles (Actinemys marmorata and Actinemys pallida, respectively), a stage-based population projection matrix was assembled with 3 life stages: hatchling, juvenile, and adult. Vital rates were defined using biologically appropriate statistical distributions, with additional parametric uncertainty included for the adult survival parameter. A triple-loop stochastic simulation model was built around a population viability analysis to project pond turtle populations into the future. Initial abundance was calculated using available historical presence data and remotely sensed landscape condition metrics. A negative binomial regression was used to predict the relationship between abundance, habitat area, and human modification. Populations of pond turtles are dominated by adult individuals, so we applied a nonstable stage distribution to initial abundance values. Initial abundances of analysis units were variable across the species' ranges, but all populations declined precipitously in the population projections. By the end of the century, the mean range-wide probability of extinction was 44.3% for the northwestern species and 57.8% for the southwestern species. Consistent with other long-lived chelonian species, population growth rate was most sensitive to adult survival, indicating that where possible, conservation efforts focusing on increasing or maintaining adult survival would benefit the species. Elasticity analysis indicated a bet-hedging life history strategy where long-term reproductive output is maximized through longevity, small clutches, and frequent reproductive bouts in the face of highly variable juvenile survival. The population dynamics presented here indicate that efforts to bolster adult survival would be most beneficial in terms of long-term population viability, which can inform targeted research and management. The feasibility of such efforts is an important consideration in conservation management for these long-lived species. [ABSTRACT FROM AUTHOR]

This article describes the R package CorrToolBox, which is designed for modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The practical utility and functionality of the package are demonstrated by several illustrative examples. In addition, the package's feasibility and performance are evaluated via simulation studies using synthetic mixed data with a range of marginal distributions. [ABSTRACT FROM AUTHOR]

The paper presents an approximate algorithm for modeling a stationary discrete random process with marginal and bivariate distributions of its consecutive components in the form of a mixture of two Gaussian distributions. The algorithm is based on a combination of the conditional distribution method and the rejection method. An example of application of the proposed algorithm for simulating time series of daily maximum air temperatures is given. [ABSTRACT FROM AUTHOR]

Simulation of random fields is widely used in Earth sciences for modeling and uncertainty quantification. The spatial features of these fields may have a strong impact on the forecasts made using these fields. For instance, in flow and transport problems the connectivity of the permeability fields is a crucial aspect. Multi-Gaussian random fields are the most common tools to analyze and model continuous fields. Their spatial correlation structure is described by a covariance or variogram model. However, these types of spatial models are unable to represent highly or poorly connected structures even if a broad range of covariance models can be employed. With this type of model, the regions with values close to the mean are always well connected whereas the regions of low or high values are isolated. Substitution random functions (SRFs) belong to another broad class of random functions that are more flexible. SRFs are constructed by composing ( Z = Y ∘ T ) two stochastic processes: the directing function T (latent field) and the coding process Y (modifying the latent field in a stochastic manner). In this paper, we study the properties of SRFs obtained by combining stationary multi-Gaussian random fields for both T and Y with bounded variograms. The resulting SRFs Z are stationary, but as T has a finite variance Z is not ergodic for the mean and the covariance. This means that single realizations behave differently from each other. We propose a simple technique to control which values (low, intermediate, or high) are connected. It consists of adding a control point on the process Y to guide every single realization. The conditioning to local values is obtained using a Gibbs sampler. [ABSTRACT FROM AUTHOR]

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The development of embryonic stem (ES) cells to extraembryonic trophectoderm and primitive endoderm lineages manifests distinct steady‐state expression patterns of two key transcription factors—Oct4 and Nanog. How dynamically such kind of steady‐state expressions are maintained remains elusive. Herein, we demonstrate that steady‐state dynamics involving two bistable switches which are interlinked via a stepwise (Oct4) and a mushroom‐like (Nanog) manner orchestrate the fate specification of ES cells. Our hypothesis qualitatively reconciles various experimental observations and elucidates how different feedback and feedforward motifs orchestrate the extraembryonic development and stemness maintenance of ES cells. Importantly, the model predicts strategies to optimize the dynamics of self‐renewal and differentiation of embryonic stem cells that may have therapeutic relevance in the future. [ABSTRACT FROM AUTHOR]

p53 kinetics plays a key role in regulating cell fate. Based on the p53 gene regulatory network composed by the core regulatory factors ATM, Mdm2, Wip1, and PIDD, the effect of the delays in the process of transcription and translation of Mdm2 and Wip1 on the dynamics of p53 is studied theoretically and numerically. The results show that these two time delays can affect the stability of the positive equilibrium. With the increase of delays, the dynamics of p53 presents an oscillating state. Further, we also study the effects of PIDD and chemotherapeutic drug etoposide on the kinetics of p53. The model indicates that (i) PIDD low-level expression does not significantly affect p53 oscillatory behavior, but high-level expression could induce two-phase kinetics of p53; (ii) Too high and too low concentration of etoposide is not conducive to p53 oscillation. These results are in good agreement with experimental findings. Finally, we consider the influence of internal noise on the system through Binomial τ -leap algorithm. Stochastic simulations reveal that high-intensity noise completely destroys p53 dynamics in the deterministic model, whereas low-intensity noise does not alter p53 dynamics. Interestingly, for the stable focus, the internal noise with appropriate intensity can induce quasi-limit cycle oscillations of the system. Our work may provide the useful insights for the development of anticancer therapy. [ABSTRACT FROM AUTHOR]

With the premise that social interactions are described by power-law distributions, we study the stochastic dynamics of SIR (Susceptible-Infected-Removed) compartmental models on static scale-free random networks generated via the configuration model. We compare simulations of our model to analytical results, providing a closed formula and a lower bound for the probability of having a minor epidemic of the disease. We explore the variability in disease spread by stochastic simulations. In particular, we demonstrate how important epidemic indices change as a function of the contagiousness of the disease and the connectivity of the network. Our results quantify the role of the starting node's degree in determining these indices, commonly used to describe epidemic spread. Our results and implementation set a baseline for studying epidemic spread on networks, showing how analytical methods can help in the interpretation of stochastic simulations. [ABSTRACT FROM AUTHOR]

In this work, we explore information geometry theoretic measures for characterizing neural information processing from EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer's disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal's instantaneous influence on another signal's information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they open or close their eyes. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. Our results show that information rate and causal information rate are superior to their more traditional or established information-theoretic counterparts, i.e., differential entropy and transfer entropy, respectively. Since these novel, information geometry theoretic measures can be applied to experimental EEG signals in a model-free manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and the diagnosis of neurological disorders, such as Alzheimer's disease as presented in this work. [ABSTRACT FROM AUTHOR]

Paratemporal methods based on tree expansion have proven to be effective in efficiently generating the trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper, we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving the desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal's triangle. Finally, we discuss the application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real-world use cases. [ABSTRACT FROM AUTHOR]

Aquifer properties, such as hydraulic transmissivity T and its spatial variability, are fundamental for sustainable groundwater exploitation in arid regions. Especially in karst aquifers, spatial variability can be considerable, and the application of geostatistical methods allows for spatial interpolation and mapping based on observations combined with the quantification of uncertainties. Moreover, direct measurements of T are typically scarce, while those of specific capacity Sc are more frequent. In this study, we establish the linear regression relationship between the logarithms of T and Sc measured in 51 wells in a semi-arid karst region in Northeastern Brazil. This relationship is used to estimate empirical values logTemp based on measurements of logSc at 269 wells. LogTemp values are found to be normally distributed with an isotropic variogram of a significant nugget effect (attributed to local-scale karst features) and approximately 10 km range (attributed to larger-scale gradual changes in karst feature density). Ordinary kriging cross-validation indicates an optimum number of 25 neighboring wells for interpolation, which is used in a conditional sequential Gaussian simulation (SGSIM) to generate 500 realizations of logTemp with respective maps of standard deviations and probabilities of (not) exceeding threshold values. High-transmissivity areas mostly coincide with karstified river valleys, while low-transmissivity areas occur toward the edges where aquifer thickness decreases. The resulting transmissivity maps are relevant for optimizing regional water management strategies, which includes stochastic approaches where transmissivity realizations can be used to parameterize multiple runs of numerical groundwater models. [ABSTRACT FROM AUTHOR]

Lying and truth-telling are conflicting behavioral strategies that pervade much of the lives of social animals and, as such, have always been topics of interest to both biology and philosophy. This age-old conflict is linked to one of the most serious threats facing society today, viz., the collapse of trustworthy sources of information. Here, we revisit this problem in the context of the two-choice sender–receiver game: the sender tosses a coin and reports the supposed outcome to the receiver, who must guess the true outcome of the toss. For the sender, the options are to lie or tell the truth, while for the receiver, the options are to believe or disbelieve the sender's account. We assume that social learning determines the strategy used by players and, in particular, that players tend to imitate successful individuals and thus change their strategies. Using the replicator equation formulation for infinite populations and stochastic simulations for finite populations, we find that when the sender benefits from the receiver's failure, the outcome of the game dynamics depends strongly on the choice of initial strategies. This sensitivity to the initial conditions may reflect the unpredictability of social systems whose members have antagonistic interests. [ABSTRACT FROM AUTHOR]

For the problem of group evaluation in complex uncertain environments, an interval rough number is used to characterize experts’ preferences, and a stochastic simulation integrated algorithm is proposed based on the G1 method combined with Monte Carlo simulation techniques. Firstly, the weighting coefficients are simulated by random sampling and the weighting of experts are determined based on ordinal correlation and interval rough number closeness. Secondly, the final weights of indicators can be obtained by combining all the experts’ opinions, and they are linearly aggregated with the pre-processed indicator values to obtain the comprehensive evaluation value of one simulation, which can be used to assess the advantages and disadvantages of the evaluated objects. Through full simulation, the preference ratio matrix is calculated, and the probability ranking with the probability of superiority is derived from it, which makes up for the deficiency of absolute ranking in the uncertain information environment. Finally, the effectiveness of the method is illustrated by an example and the advantages of the method are described in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

Ranking and selection (R&S) procedures in simulation optimization simulate every feasible solution to provide global statistical error control, often selecting a single solution in finite time that is optimal or near-optimal with high probability. By exploiting parallel computing advancements, large-scale problems with hundreds of thousands and even millions of feasible solutions are suitable for R&S. Naively parallelizing existing R&S methods originally designed for a serial computing setting is generally ineffective, however, as many of these conventional methods uphold family-wise error guarantees that suffer from multiplicity and require pairwise comparisons that present a computational bottleneck. Parallel adaptive survivor selection (PASS) is a new framework specifically designed for large-scale parallel R&S. By comparing systems to an adaptive "standard" that is learned as the algorithm progresses, PASS eliminates inferior solutions with false elimination rate control and with computationally efficient aggregate comparisons rather than pairwise comparisons. PASS satisfies desirable theoretical properties and performs effectively on realistic problems. We reconsider the ranking and selection (R&S) problem in stochastic simulation optimization in light of high-performance, parallel computing, where we take "R&S" to mean any procedure that simulates all systems (feasible solutions) to provide some statistical guarantee on the selected systems. We argue that when the number of systems is very large, and the parallel processing capability is also substantial, then neither the standard statistical guarantees such as probability of correct selection nor the usual observation-saving methods such as elimination via paired comparisons or complex budget allocation serve the experimenter well. As an alternative, we propose a guarantee on the expected false elimination rate that avoids the curse of multiplicity and a method to achieve it that is designed to scale computationally with problem size and parallel computing capacity. To facilitate this approach, we present a new mathematical representation, prove small-sample and asymptotic properties, evaluate variations of the method, and demonstrate a specific implementation on a problem with over 1 , 100 , 000 systems using only 21 parallel processors. Although we focus on inference about the best system here, our parallel adaptive survivor selection framework can be generalized to many other useful definitions of "good" systems. Funding: This work was supported by the National Science Foundation [Grants CMMI-1537060 and CMMI-1554144]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2343. [ABSTRACT FROM AUTHOR]

Python has become an essential programming language for scientific computing and data analysis and processing. Various multiple-point statistics (MPS) algorithms are used to characterize complex heterogeneous structures and phenomena in earth sciences. However, there is currently no Python library that integrates mainstream MPS methods for simulation and computation in geosciences. Aiming to establish a stable MPS tool, we developed an open-source Python library of commonly used MPS methods, named pyMPSLib. pyMPSLib consists of ENESIM, SNESIM, and DS algorithms and provides a flexible and convenient API interface. To ensure the maintainability of pyMPSLib, the Python objects and toolkits of MPS algorithms are defined and implemented. To improve the compatibility and extensibility of the presented library, uniform coding standard is adopted in pyMPSLib. We performed the parameter sensitivity analysis under multiple configurations to validate the performance of the library. This open-source library also provides optional tools to quantitatively evaluate the realizations of the integrated MPS methods. [ABSTRACT FROM AUTHOR]

The Mexico City Metrobus is one of the most popular forms of public transportation inside the city, and since its opening in 2005, it has become a vital piece of infrastructure for the city; this is why the optimal functioning of the system is of key importance to Mexico City, as it plays a crucial role in moving millions of passengers every day. This paper presents a model to simulate Line 1 of the Mexico City Metrobus, which can be adapted to simulate other bus rapid transit (BRT) systems. We give a detailed description of the model development so that the reader can replicate our model. We developed various response variables in order to evaluate the system’s performance, which focused on passenger satisfaction and measured the maximum occupancy that a passenger experiences inside the buses, as well as the time that he spends in the queues at the stations. The results of the experiments show that it is possible to increase passenger satisfaction by considering different combinations of routes while maintaining the same fuel consumption. It was shown that, by considering an appropriate combination of routes, the average passenger satisfaction could surpass the satisfaction levels obtained by a 10% increase in total fuel consumption.

BackgroundAn understanding of epidemiological dynamics, once confined to mathematical epidemiologists and applied mathematicians, can be disseminated to a non-mathematical community of health care professionals and applied biologists through simple-to-use simulation applications. We used Numerus Model Builder RAMP Ⓡ (Runtime Alterable Model Platform) technology, to construct deterministic and stochastic versions of compartmental SIR (Susceptible, Infectious, Recovered with immunity) models as simple-to-use, freely available, epidemic simulation application programs.ResultsWe take the reader through simulations used to demonstrate the following concepts: 1) disease prevalence curves of unmitigated outbreaks have a single peak and result in epidemics that 'burn' through the population to become extinguished when the proportion of the susceptible population drops below a critical level; 2) if immunity in recovered individuals wanes sufficiently fast then the disease persists indefinitely as an endemic state, with possible dampening oscillations following the initial outbreak phase; 3) the steepness and initial peak of the prevalence curve are influenced by the basic reproductive value R0, which must exceed 1 for an epidemic to occur; 4) the probability that a single infectious individual in a closed population (i.e. no migration) gives rise to an epidemic increases with the value of R0>1; 5) behavior that adaptively decreases the contact rate among individuals with increasing prevalence has major effects on the prevalence curve including dramatic flattening of the prevalence curve along with the generation of multiple prevalence peaks; 6) the impacts of treatment are complicated to model because they effect multiple processes including transmission, recovery and mortality; 7) the impacts of vaccination policies, constrained by a fixed number of vaccination regimens and by the rate and timing of delivery, are crucially important to maximizing the ability of vaccination programs to reduce mortality.ConclusionOur presentation makes transparent the key assumptions underlying SIR epidemic models. Our RAMP simulators are meant to augment rather than replace classroom material when teaching epidemiological dynamics. They are sufficiently versatile to be used by students to address a range of research questions for term papers and even dissertations.

Great deviations in building energy consumption simulation are attributed to the simplified settings of occupants’ air conditioning (AC) usage schedules. This study was designed to develop a method to quantify the uncertainty and randomness of AC usage behavior and incorporate the model into simulations, in order to improve the prediction performance of AC energy consumption. Based on long-term onsite monitoring of household thermal environments and AC usage patterns, two stochastic models were built using unsupervised cluster and statistical methods. Based on the Monte Carlo method, the AC operation schedule was generated through AC opening duration, setpoints, and other relevant parameters, and was further incorporated into EnergyPlus. The results show that the ideally deterministic AC operation settings from the standard significantly overestimate the cooling energy consumption, where the value based on the fixed mode was 6.35 times higher. The distribution of daily AC energy consumption based on the stochastic modeling was highly consistent with the actual situation, thanks to the accurate prediction of the randomness and dynamics of residents’ AC usage patterns. The total cooling energy consumption based on two stochastic models was found to be much closer to the actual values. The work proposes a method of embedding stochastic AC usage models to EnergyPlus 22.1 benefits for an improvement in building energy consumption simulation and the energy efficiency evaluation regarding occupant behavior in the future.

Human mobility contributes to the fast spatiotemporal propagation of infectious diseases. During an outbreak, monitoring the infection on either side of an international border is crucial as cross-border migration increases the risk of disease importation. Due to the unavailability of cross-border mobility data, mainly during pandemics, it becomes difficult to propose reliable, model-based strategies. In this study, we propose a method for estimating commuting-type cross-border mobility flux between any pair of regions that share an international border from the observed difference in their infection peak timings. Assuming the underlying disease dynamics are governed by a Susceptible–Infected–Recovered (SIR) model, we employ stochastic simulations to obtain the maximum likelihood cross-border mobility estimate for any pair of regions. We then investigate how the estimate of cross-border mobility flux varies depending on the transmission rate. We further show that the uncertainty in the estimates decreases for higher transmission rates and larger observed differences in peak timing. Finally, as a case study, we apply the method to some selected regions along the Poland–Germany border that are directly connected through multiple modes of transportation and quantify the cross-border fluxes from the COVID-19 cases data from 20 February to 20 June 2021.

When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm n observations are generated for the parameters, and in the inner level m observations of the simulation model are generated with the values of parameters fixed at the values generated in the outer level. In this article, we consider the case in which the observations at both levels of the algorithm are independent and show how the variance of the observations can be decomposed into the sum of a parametric variance and a stochastic variance. Next, we derive central limit theorems that allow us to compute asymptotic confidence intervals to assess the accuracy of the simulation-based estimators for the point forecast and the variance components. Under this framework, we derive analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand, and we use these expressions to illustrate the validity of our theoretical results by performing simulation experiments with this forecast model. We found that, given a fixed number of total observations nm, the choice of only one replication in the inner level (m=1) is recommended to obtain a more accurate estimator for the expectation of a performance variable.

Abstract The use of precast prestressed concrete bridge piers is rapidly evolving and widely applied. Nevertheless, the probabilistic behavior of the bending performance of precast prestressed concrete bridge piers has often been overlooked. This study aims to address this issue by utilizing actual precast bridge piers as the engineering context. Through the implementation of the Monte-Carlo simulation and Gradient Boosted Regression Trees (GBRT) algorithm, the stochastic distribution of the bending performance and their critical factors are identified. The results show that the normal distribution is the most suitable for the random distribution of bending performance indicators. The variability of the elastic modulus of ordinary steel bars, initial strain of prestressed steel hinge wires, and constant load axial force has little effect on the bending moment performance, while the yield stress of ordinary steel bars, elastic modulus of concrete, compressive strength of unrestrained concrete, and elastic modulus of prestressed steel hinge wires have a greater impact on the bending performance. Additionally, the compressive strength of unrestrained concrete has a significant influence on the equivalent bending moment of the cross-section that concerns designers.

Numerical simulation is an important method to determine the leaching range in the process of in-situ uranium mining, and the permeability coefficient is an important factor affecting the accurate calculation of leaching range by numerical simulation methods. However, the numerical model is difficult to completely describe the actual spatial distribution of permeability coefficients due to the complexity of the distribution of aquifer permeability. In order to analyze the influence of permeability coefficient uncertainty on leaching agent migration process and leaching range, according to the field condition test data of sandstone-type uranium deposits in Inner Mongolia, 200 random fields of permeability coefficient in accordance with the specific distribution were obtained using the Python geostatistical software package GSTools. All permeability coefficient random fields were applied to solute transport simulation, and Monte Carlo method was used to convert the parameter uncertainty of permeability coefficient into the uncertainty of model results. The results of simulation under homogeneous and heterogeneous permeability conditions were compared. The leaching agent in homogeneous conditions could not migrate outward and the leaching range is a regular circular shape. The direction of leaching agent migration is affected by the regional permeability between pumping and injection wells under heterogeneous conditions of permeability coefficients. When the permeability coefficient of the region between the pumping and injection wells is relatively higher, the leaching agent migrates only to the pumping wells and not outward. Conversely, a part of the leaching agent would spread along the preferential flow channels to the high-permeability region outside the pumping and injection wells region, resulting in a non-regular shape of contamination plume. Based on the analysis of the actual geological conditions, the complexity of its spatial distribution is ignored when the permeability coefficient is extended to the average value, and the influence of the random distribution of the permeability coefficient should be considered in the model. However, the leaching range obtained from different permeability coefficient random fields has obvious differences, indicating that the uncertainty of permeability coefficient leads to the uncertainty of simulation results, which can be quantified and analyzed using relevant statistical methods. The results of the quantitative analysis show that influence of permeability coefficient uncertainty on the expansion rate of the leaching range is distinct at different time. At the initial stage of the simulation (1-20 d), the influence of the uncertainty in permeability coefficient is low, and the leaching range expanded rapidly at an average rate of 350 m2/d. With the increasing of simulation time, the influence of permeability coefficient uncertainty is significant, and the expansion rate of leaching range is 18-54 m2/d. The expansion rate decreases but the uncertainty of leaching range shows a linear increasing trend. At the end of the simulation, the distribution characteristics of leaching range of 200 groups are counted, and it is found that the leaching range is concentrated in 20 000-30 000 m2 with a high degree of uncertainty. When the confidence levels are 95%, 85%, 75% and 65%, the confidence intervals of the leaching range are 34 072.0-17 699.8, 31 900.2-19 871.6, 30 688.9-21 082.8 and 29 770.1-22 001.7 m2, respectively. The higher the confidence level, the larger the confidence interval of the leaching range, indicating the higher uncertainty, and different confidence levels can be selected to determine the dissolved leaching range according to the demand in practical applications. The research results obtained by considering the uncertainty of permeability coefficients above are suitable for actual situation, therefore the results can provide reference for the determination of leaching range in uranium mining by in-situ leaching.

The prediction of the agricultural water resources carrying capacity (AWRCC) is important for evaluating the ecological security of water resources and ensure healthy and sustainable socio-economic development. However, the AWRCC is influenced by many important factors such as water resources volume, socio-economic structure and technology level. The existing prediction methods were mainly based on the policies and economic activities under predefined scenarios. It is difficult to fully reflect the future AWRCC and generate regulation schemes based on AWRCC target requirements. The paper proposed a stochastic simulation method to simulate the evaluation indicators, which were selected based on principal component analysis (PCA) and iterative strategy, and structured a support vector machine model to solve the problem of complicated calculations to realize the probability distribution prediction of the AWRCC. Furthermore, the important indicators affecting the improvement of the AWRCC were explored, and finally a regulation schemes that met the AWRCC target were generated. The results of the calculations for Zhangjiakou, Hebei Province, indicated that there is a 97.5% probability that the AWRCC evaluation value will be Level III in 2025 and there is a 2.5% probability that it will be Level II. The probabilities of achieving close to saturated, transition, and close to weakly carriable states for the corresponding Level III are 27.5%, 61%, and 9%, respectively, and the root mean square error of the prediction is 0.0057. In addition, by adjusting the important indicators, the probability of the AWRCC reaching Level IV is 72.5%, and AWRCC improves from Level III to Level IV. The proposed method can achieve higher efficient and accurate AWRCC evaluation, which can better meet the requirements of modern water resources management, effectively reduce the misjudgment risk and provide support for water resources regulation.

The serial nature of reactions involved in the RNA life-cycle motivates the incorporation of delays in models of transcriptional dynamics. The models couple a transcriptional process to a fairly general set of delayed monomolecular reactions with no feedback. We provide numerical strategies for calculating the RNA copy number distributions induced by these models, and solve several systems with splicing, degradation, and catalysis. An analysis of single-cell and single-nucleus RNA sequencing data using these models reveals that the kinetics of nuclear export do not appear to require invocation of a non-Markovian waiting time. [ABSTRACT FROM AUTHOR]