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In accidents involving cars with pedestrians, the impact of the head on structural parts of the vehicle presents a significant risk of injury. If the head hits the windshield, the injury is highly influenced by glass fracture. In pedestrian protection tests, a head form impactor is shot on the windshield while the resultant acceleration at the centre of gravity of the head is measured. To assess the risk of fatal or serious injury, a head injury criterion (HIC) as an explicit function of the measured acceleration can be determined. The braking strength of glass, which has a major impact on the head acceleration, however, is not deterministic but depends on production-related microcracks on the glass surface as well as on the loading rate. The aim of the present paper is to show a pragmatic method for how to include the stochastic failure of glass in crash and impact simulations. The methodology includes a fracture mechanical model for the strain rate-dependent failure of glass, an experimental determination of the glass strength for the different areas of a windshield (surface, edge, and screen-printing area), a statistical evaluation of the experimental data, and a computation of an HIC probability distribution by stochastic simulation.

Social structure is based on the availability of the Water-Energy-Food Nexus. To cover these needs of society, several solutions of different scales of infrastructures coexist. The construction of infrastructure is capital-intensive; therefore, investment risk is always a consideration. In this paper, we try to evaluate the investment risk by interest rates (IR). We show that IR is a key indicator, which includes multiple parameters of prosperity expressing the political and economic status quo of the society. The selection of a particular scale influences the required capital and is thus one of the most critical decisions. Water supply infrastructure is one of the foundations of society, and the selection of the optimal scale of such infrastructure is often a great challenge in civil engineering. As a case study, we analyse three different scales of water supply infrastructures for the area of West Mani (Greece), i.e., dam, water ponds, and seawater desalination. We evaluate each case by the capital intensity by performing stochastic simulations of interest rates and identify the optimal solution as the one with the smallest median unit cost, in this case, the water ponds. In order to assess the impact of the development level of a country on the resulting unit cost stochastic properties we apply the optimal solution to various countries with different development levels and IR. We show that IR in the least developed countries, being generally higher and more variable, increases the unit cost, including its variability, which ultimately indicates higher investment risk.

This paper describes an adaptive noise generator circuit suitable for on-chip simulations of stochastic chemical kinetics. The circuit uses amplified BJT white noise and adaptive low-pass filtering to emulate the power spectrum and auto-correlation of random telegraph signals (RTS) with Poisson-distributed level transitions. A current-mode implementation in the IHP 0.25 µm BiCMOS process shows excellent agreement with theoretical results from the Gillespie stochastic simulation algorithm over a 60 dB range in mean current levels (modeling molecule count numbers). The circuit has an estimated layout area of 0.01 mm2 and typically consumes 100 µA, which are 10× and 8× better, respectively, than prior implementations.

Microalgae are multi-purpose microbial agents due to their capability to efficiently sequester carbon dioxide and produce valuable biomass such as protein and single-cell oils. Formulation and tuning of microalgae kinetics models can significantly contribute to the successful design and operation of microalgae reactors. This work aimed to demonstrate the capability of self-organizing map (SOM) algorithm to elucidate the patterns of parameter rankings in microalgae models subject to stochastic variations of input forcing functions–bioprocess influent component concentration levels. These stochastic variations were implemented on a modeled chemostat with a deterministic microalgae kinetic model consists of ten time-dependent variables and eighteen model parameters. The methodology consists of two major stages: (1) global sensitivity analysis (GSA) on the importance of model parameters with stochastic sampling of bioreactor influent component concentrations, and (2) training of self-organizing maps on the datasets of model parameter rankings derived from the GSA indices. Results reveal that functional principal components analysis can project at least 99% of the time-dependent dynamic patterns of the model variables on B-splines basis functions. The component planes for hexagonal lattice SOMs reveal that the sensitivity rankings some parameters in the algae model tested can be stable over a wide range of variations in the levels of influent component concentrations. Therefore, SOM can be used to reveal the trends in multi-dimensional data arrays arising from the implementation of GSA of kinetic models under stochastic perturbation of input forcing functions.

Complex physical models are the most advanced tools available for producing realistic simulations of the climate system. However, such levels of realism imply high computational cost and restrictions on their use for policymaking and risk assessment. Two central characteristics of climate change are uncertainty and that it is a dynamic problem in which international actions can significantly alter climate projections and information needs, including partial and full compliance of global climate goals. Here we present AIRCC-Clim, a simple climate model emulator that produces regional probabilistic climate change projections of monthly and annual temperature and precipitation, as well as risk measures, based both on standard and user-defined emissions scenarios for six greenhouse gases. AIRCC-Clim emulates 37 atm-ocean coupled general circulation models with low computational and technical requirements for the user. This standalone, user-friendly software is designed for a variety of applications including impact assessments, climate policy evaluation and integrated assessment modelling.

Hydroclimatic processes such as precipitation, temperature, wind speed and dew point are usually considered to be independent of each other. In this study, the cross–correlations between key hydrological-cycle processes are examined, initially by conducting statistical tests, then adding the impact of long-range dependence, which is shown to govern all these processes. Subsequently, an innovative stochastic test that can validate the significance of the cross–correlation among these processes is introduced based on Monte-Carlo simulations. The test works as follows: observations obtained from numerous global-scale timeseries were used for application to, and a comparison of, the traditional methods of validation of statistical significance, such as the t-test, after filtering the data based on length and quality, and then by estimating the cross–correlations on an annual-scale. The proposed method has two main benefits: it negates the need of the pre-whitening data series which could disrupt the stochastic properties of hydroclimatic processes, and indicates tighter limits for upper and lower boundaries of statistical significance when analyzing cross–correlations of processes that exhibit long-range dependence, compared to classical statistical tests. The results of this analysis highlight the need to acquire cross–correlations between processes, which may be significant in the case of long-range dependence behavior.

Olive pruning residues are a by-product that can be applied to soil or used for energy production in a circular economy model. Its benefits depend on the amount of pruning, which varies greatly within farms. This study aimed to investigate the spatial variability of shreddable olive pruning in a traditional olive grove in Córdoba (Spain) with an area of 15 ha and trees distanced 12.5 m from each other. To model the spatial variability of shreddable olive pruning, geostatistical methods of stochastic simulation were applied to three correlated variables measured on sampled trees: the crown projected area (n = 928 trees), the crown volume (n = 167) and the amount of shreddable pruning (n = 59). Pearson’s correlation between pairs of variables varied from 0.71 to 0.76. The amount of pruning showed great variability, ranging from 7.6 to 76 kg tree−1, with a mean value of 37 kg tree−1. Using exponential and spherical variogram models, the spatial continuity of the variables under study was established. Shreddable dry pruning weight values showed spatial autocorrelation up to 180 m. The spatial uncertainty of the estimation was obtained using sequential simulation algorithms. Stochastic simulation algorithms provided 150 possible images of the amount of shreddable pruning on the farm, using tree projected area and crown volume as secondary information. The interquartile range and 90% prediction interval were used as indicators of the uncertainty around the mean value. Uncertainty validation was performed using accuracy plots and the associated G-statistic. Results indicate with high confidence (i.e., low uncertainty) that shreddable dry pruning weight in the mid-western area of the farm will be much lower than the rest of the farm. In the same way, results show with high confidence that dry pruning weight will be much higher in a small area in the middle east of the farm. The values of the G-statistic ranged between 0.89 and 0.90 in the tests performed. The joint use of crown volume and projected areas is valuable in estimating the spatial variability of the amount of pruning. The study shows that the use of prediction intervals enables the evaluation of farm areas and informed management decisions with a low level of risk. The methodology proposed in this work can be extrapolated to other 3D crops without requiring modifications. On a larger scale, it can be useful for predicting optimal locations for biomass plants, areas with high potential as carbon sinks or areas requiring special soil protection measures.

The values of the physical–mechanical properties of any soil are affected by uncertainties both due to experimental measurements and the impossibility of knowing them, in detail, at every point of the spatial domain. Accordingly, this work focuses on uncertainty in shear wave velocity (Vs) and its impact on the seismic response. The Monte Carlo method, based on pseudo-random number generation, was selected. To understand which random distributions could identify the site’s real conditions, the Fourier spectrum frequencies were calculated for each realization and were compared with the predominant natural site frequency. The experimental range data were used to calculate the spectral average acceleration and the horizontal amplification factors. The simulations were performed and interpreted by a modified version of VisualQ4M software based on 2D Quad4M, including the generation of pseudo-random numbers and pre- and post-data processing. A site at a small scale, in the territory of the city of L’Aquila (Italy), was selected as the test case. This paper demonstrates, from a numerical point of view, that both a simple local topographic modification due to excavation and the uncertainties of the numerical values, even of the shear wave velocity alone, can have an important impact on the local seismic amplification.

Finite-fault stochastic method was applied to simulate the Mw 6.9, 2008 Iwate-Miyagi earthquake at 44 selected Kik-net sites using EXSIM computer code. To investigate the effects of source characte...

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45° and 65°. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

A numerically efficient globally averaged number density approach is used to simulate a reaction-diffusion system using a particle-based stochastic simulation algorithm called reactive multiparticle collision (RMPC) dynamics. Constant diffusivity of the particles is achieved through a time-varying rotation angle (also called collision angle). Variation in the diffusion coefficient between two different chemical species is achieved in one of two ways: (ⅰ) using a different kBT/m value for one species compared to the other, or (ⅱ) using the same kBT/m value for both species, but using a different probability to free-stream for one species compared to another. For smaller diffusivities and larger spatial inhomogeneities, bath particles were necessary for the model to agree with the PDE solution. The latter approach was further used without a bath, and shown to be capable of producing Turing patterns after long simulation times. The significance of our work is that RMPC can serve as a feasible simulation tool for both short and long-term simulations, can handle spatial inhomogeneities, can model a fairly large range of diffusivities in a reaction-diffusion scenario, and is capable of producing Turing patterns. An advantage of this method includes more detailed system information in feasible simulation times.

In Northern European countries, a great variety of Red cattle populations exists which can be broadly categorized in two groups: specialized dairy and dual-purpose breeds. Collaboration between these breeds (i.e. the exchange of sires across breeds) can be beneficial but is limited so far. The aim of this study was to demonstrate and evaluate consequences of collaboration between Red breeds using stochastic simulations. Two breeding lines (dairy type and dual purpose) were simulated. As a special aspect of this study, differences in genetic levels of breeding traits (milk production, beef production, mastitis resistance, fertility, feed efficiency) have been taken into account. Various scenarios were investigated where across-breed selection was either restricted or allowed and with different correlations between breeding goals in the two lines. The results of this study were influenced by the different genetic levels in breeding traits only in the first years of simulation. In the long run, the breed differences did not affect the degree of collaboration between lines. When the correlation between breeding goals was close to unity, the selection of external bulls was highly beneficial in terms of genetic gain and total monetary gain. Additionally, the lowest rate of inbreeding was found in that case. With decreasing correlations between environments, degree of cooperation between lines rapidly terminated and lines operated individually. In last years of simulation, cooperation was only found when the correlation between breeding goals was close to unity. From a long-term perspective, the exchange of breeding sires across lines also caused negative effects. In the dual-purpose line, deterioration of genetic gain in mastitis resistance and fertility was observed. Additionally, breeding lines genetically converged, which decreased genetic diversity. Collectively, short-term benefits and long-term negative effects have to be reconciled if collaboration between Red breeds in Northern Europe is to be pursued.

Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products' exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance.

Nowadays, many manufacturing and service industries prefer to share resources such as facilities and workers to cooperatively perform tasks, which can efficiently improve resource utilization and customer satisfaction. Generally, the decision-makers need to pay more for resource usage, leading to an urgent demand to decrease operational costs. This article proposes a stochastic bi-objective two-stage open shop scheduling problem that models a vehicle maintenance process where tasks are appointed to be completed by multiple third-party companies with professional equipment. We formulate this optimization problem by minimizing the total tardiness and processing cost subject to various resource constraints. A hybrid multiobjective migrating birds optimization combined with a genetic operation and a discrete event system is designed by considering problem characteristics to solve the problem. In this method, the migrating birds optimization with some particular strategies aims at searching candidate solutions from the entire solution domain. Simultaneously, the discrete event system, by using stochastic simulation and discrete event-based simulation approaches, focuses on evaluating the performance of searched solutions. Simulation experiments are performed, and state-of-the-art algorithms are used as competitive approaches. The results confirm that this approach has an excellent performance in handling our considered problem.

With few exceptions, simulation output analysis has focused on static characterizations, to determine a property of the steady-state distribution of a performance metric such as a mean, a quantile, or the distribution itself. Analyses often seek to overcome difficulties induced by autocorrelation of the output stream. But sample paths generated by stochastic simulation exhibit dynamic behaviour that is characteristic of system structure and associated distributions. In this paper, we explore these dynamic characteristics, as captured by the Fourier transform of a dynamic steady-state simulation trajectory. We find that Fourier coefficient magnitudes can have greater discriminatory power than the usual test statistics when two systems have different utilisations and/or dynamic behaviour, and with simpler analysis resulting from the statistical independence of coefficient estimates at different frequencies.

Surface temperature has increased due to the impact of climate change, and the related weather events, such as heat waves and urban heat island, are occurring more frequently than before. Local governments and planners consider these impacts of climate change and try to avoid them. One of the mainly used structural tools is building summer shelters. A critical issue for urban planners is to test how many shelters should be added and how to distribute the shelters to cope with the impact of climate change. Stochastic simulation models can be a good option to randomize locations of shelters and to see how beneficial for living the shelters can be. Therefore, a novel stochastic simulation model is proposed for distributing summer shelters for coping with the climate change impact. This study proposes a stochastic spatial binary simulation with multivariate normal distribution (SSBM) which contains two major procedures consisting of (1) simulation-based derivation of the empirical function and (2) stochastic simulation of spatial binary data with multivariate normal distribution and the derived empirical function. The proposed model is applied to a case study in Jinju City, South Korea, for the umbrella-shape summer shelters (USS). Results concluded that the proposed SSBM reproduced the statistical characteristics of USS and can be a good alternative to model the locations of USS, including the impact of climate change and investigating the evolution of the USS in the future.

Distinct localisation of macromolecular structures relative to cell shape is a common feature across the domains of life. One mechanism for achieving spatiotemporal intracellular organisation is the Turing reaction-diffusion system (e.g. Min system in the bacterium Escherichia coli controlling in cell division). In this thesis, I explore potential Turing systems in archaea and eukaryotes as well as the effects of subdiffusion. Recently, a MinD homologue, MinD4, in the archaeon Haloferax volcanii was found to form a dynamic spatiotemporal pattern that is distinct from E. coli in its localisation and function. I investigate all four archaeal Min paralogue systems in H. volcanii by identifying four putative MinD activator proteins based on their genomic location and show that they alter motility but do not control MinD4 patterning. Additionally, one of these proteins shows remarkably fast dynamic motion with speeds comparable to eukaryotic molecular motors, while its function appears to be to control motility via interaction with the archaellum. In metazoa, neurons are highly specialised cells whose functions rely on the proper segregation of proteins to the axonal and somatodendritic compartments. These compartments are bounded by a structure called the axon initial segment (AIS) which is precisely positioned in the proximal axonal region during early neuronal development. How neurons control these self-organised localisations is poorly understood. Using a top-down analysis of developing neurons in vitro, I show that the AIS lies at the nodal plane of the first non-homogeneous spatial harmonic of the neuron shape while a key axonal protein, Tau, is distributed with a concentration that matches the same harmonic. These results are consistent with an underlying Turing patterning system which remains to be identified. The complex intracellular environment often gives rise to the subdiffusive dynamics of molecules that may affect patterning. To simulate the subdiffusive transport of biopolymers, I develop a stochastic simulation algorithm based on the continuous time random walk framework, which is then applied to a model of a dimeric molecular motor. This provides insight into the effects of subdiffusion on motor dynamics, where subdiffusion reduces motor speed while increasing the stall force. Overall, this thesis makes progress towards understanding intracellular patterning systems in different organisms, across the domains of life.

In environmental transmission, pathogens transfer from one individual to another via the environment. It is a common transmission mechanism in a wide range of host-pathogen systems. Incorporating environmental transmission in dynamic transmission models is crucial for gauging the effect of interventions, as extrapolating model results to new situations is only valid when the mechanisms are modelled correctly. The challenge in environmental transmission models lies in not jointly identifiable parameters for pathogen shedding, decay, and transmission dynamics. To solve this unidentifiability issue, we present a stochastic environmental transmission model with a novel scaling method for shedding rate parameter and a novel estimation method that distinguishes transmission rate and decay rate parameters. The core of our scaling and estimation method is calculating exposure and relating exposure to infection risks. By scaling shedding rate parameter, we standardize exposure to pathogens contributed by one infectious individual present during one time interval to one. The standardized exposure leads to a standard definition of transmission rate parameter applicable to scenarios with different decay rate parameters. Hence, we unify direct transmission (large decay rate) and environmental transmission in a continuous manner. More importantly, our exposure-based estimation method can correctly estimate back the transmission rate and the decay rate parameters, while the commonly used trajectory-based method failed. The reason is that exposure-based method gives the correct weight to infection data from previous observation periods. The correct estimation from exposure-based method will lead to more reliable predictions of intervention impact. Using the effect of disinfection as an example, we show how incorrectly estimated parameters may lead to incorrect conclusions about the effectiveness of interventions. This illustrates the importance of correct estimation of transmission rate and decay rate parameters for extrapolating environmental transmission models and predicting intervention effects.

In digital soil mapping (DSM), soil maps are usually produced in a univariate manner, that is, each soil map is produced independently and therefore, when multiple soil properties are mapped the underlying dependence structure between these soil properties is ignored. This may lead to inconsistent predictions and simulations. For example, soil organic carbon (SOC) and total nitrogen (TN) maps produced independently may show unrealistic carbon–nitrogen (C:N) ratios. In the last decade the production of soil maps with machine learning models has become increasingly popular as these models are able to capture complex non-linear relationships between soil properties and environmental covariates. However, producing soil maps with multivariate machine learning models is still lacking and requires much investigation in DSM. In this paper we present the combined modelling of multiple soil properties with a multivariate random forest (MRF) model. We applied this model to mapping SOC and TN, and we compared it with results of two separate univariate random forest (RF) models. The comparison was done by means of stochastic simulations determined by sampling from the conditional distributions of the soil properties, given the covariates, as estimated by quantile regression forest. The results show that the MRF model is superior in terms of maintaining the dependence structure between SOC and TN, and consequently, is also able to produce more realistic C:N ratios. The models were also compared on the basis of prediction accuracy using commonly used accuracy metrics such as the root mean square error (RMSE). We found that the accuracy of the MRF model (RMSE-SOC=40.04, RMSE-TN=2.26, RMSE-CN=3.58) is comparable to that of the univariate RF models (RMSE-SOC=39.76, RMSE-TN=2.26, RMSE-CN=3.65). We performed the same comparisons between a regression co-kriging model and two separate regression kriging models, and made similar conclusions.

Pedometricians have spent a lot of effort on mapping soil types and basic soil properties. However, end-users typically need a more elaborate soil quality index for land management. Soil quality indices are typically derived from multiple individual soil properties by evaluating whether specific criteria are met. If this is based on individually mapped soil properties, then an important consequence is that cross-correlations between soil properties are ignored. This makes it impossible to quantify the uncertainties associated with the mapped indices. The objective of this study was to map a soil potential multifunctionality index for agriculture (Agri-SPMI) over a 12,125 km2 study region located along the French Mediterranean coast to help urban planners preserve soils of the highest quality. The index considered the ability of soils to fulfil four functions under five land use scenarios. A binary map represented each soil function fulfilment for a given scenario. The final soil quality index map was the sum of the 20 binary maps. A regression cokriging model was developed to map the basic soil properties first individually from legacy soil data and spatial soil covariates using a random forest algorithm, and next, interpolate the residuals using cokriging and the linear model of coregionalisation. The mapping uncertainties of soil properties were propagated by calculating the soil quality index over 300 stochastic simulations of soil properties derived from the linear models of coregionalisation. Results showed a poor prediction accuracy of the quality index, mainly because some soil properties were poorly predicted (notably available water capacity and coarse fragments) and used in combination with extreme thresholds that defined land suitability. Overall, the uncertainty was correctly quantified because the stochastic simulations reproduced the width of the observed distribution well, but the shapes of the distributions differed considerably from those of the observations. We envisage some ways for improvement, such as creating probability maps instead of the mean from simulations, and changing the prediction support from point to area. Highlights: The study mapped a soil quality index (Agri-SPMI) to help preserve soils of highest quality along the French Mediterranean coast. The Agri-SPMI considered the ability of soils to fulfill four functions under five land use scenarios, derived from multiple individual soil properties. A regression cokriging model was developed to map the basic soil properties, followed by interpolating the residuals using cokriging and the linear model of coregionalisation. The study accurately quantified mapping uncertainties using stochastic simulations, but the soil quality index prediction accuracy was poor, with suggestions for improvement.

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterize their metabolism at the genome scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modeling formalism of metabolic network modeling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded flux-balance analysis models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.

The aim of this study was to develop a stochastic simulation model to estimate implementation costs of porcine cysticercosis (CP) control programmes. Three scenarios were proposed: (1) use of a commercial recombinant vaccine (RV), (2) mixed use of RV plus oxfendazole (OFZ), and (3) dosing with OFZ. To establish the efficacy of the interventions, the probabilities of elimination of cysticercosis were obtained through the “cystiSim” agent-based model. The cost model was developed in R with 10000 iterations. It was found that the mixed scenario offered a probability of 0.987 eliminating cysticercosis in 6.5 years and with an annual cost of S/.335,208 (95% CI: S/ 309,922 – 368,698) (1 US$ = S/. 3.75). In contrast, the dosing strategy with OFZ proved to be less expensive with S/.260,518 (95% CI: S/ 237,559 – 293,704) per year and a probability of elimination of 0.951 in the same evaluation period. In scenarios 1 and 2, the drug cost category is the highest, representing about 50% of the final cost, while in scenario 3, the highest cost is personnel, followed by drugs. The work concludes that the dosing strategy with OFZ is the least expensive alternative of the proposed scenarios., El objetivo del estudio fue desarrollar un modelo de simulación estocástica para estimar costos de implementación de programas de control de cisticercosis porcina (CP). Se plantearon tres escenarios: (1) utilización de una vacuna recombinante (VR) comercial, (2) uso mixto de la VR más oxfendazol (OFZ), y (3) dosificación con OFZ. Para establecer la eficacia de las intervenciones se obtuvieron las probabilidades de eliminación de la cisticercosis a través del modelo basado en agentes “cystiSim”. El modelo de costo fue desarrollado en R con 10000 iteraciones. Se encontró que el escenario mixto, con probabilidad de eliminación del 0.987 en 6.5 años, tuvo con costo anual de S/.335,208 (95% IC: S/ 309,922 – 368,698) (1 US$ = S/. 3.75). En contraparte, la estrategia de dosificación con OFZ mostró ser menos costosa con S/. 260,518 (95% IC: S/ 237,559 – 293,704) por año y una probabilidad de eliminación de 0.951 en el mismo periodo de evaluación. En los escenarios 1 y 2 la categoría costo de fármacos es la mayor, representando cerca del 50% del costo final, mientras que en el escenario 3 el mayor costo es el personal seguido de los fármacos. El trabajo concluye que la estrategia de dosificación con OFZ es la alternativa menos costosa de los escenarios planteados.

Hydrological applications such as storm-water management or flood design usually deal with and are driven by region-specific reference rainfall regulations or guidelines based on Intensity-Duration-Frequency (IDF) curves. IDF curves are usually obtained via frequency analysis of rainfall data using which the exceedance probability of rain intensity for different durations are determined. It is also rather common for reference rainfall to be expressed in terms of precipitation P, accumulated in a duration D (related to rainfall intensity ), with a return period T (inverse of exceedance probability). Meteorological modules of hydro-meteorological models used for the aforementioned applications therefore need to be capable of simulating such reference rainfall scenarios. The multifractal cascade framework, since it incorporates physically realistic properties of rainfall processes (non-homogeneity or intermittency, scale invariance and extremal statistics) seems to suit this purpose. Here we propose a discrete-in-scale universal multifractal (UM) cascade based approach. Daily, Hourly and six-minute rainfall time series datasets (with lengths ranging from 100 to 15 years) over three regions (Paris, Nantes, and Aix-en-Provence) in France that are characterized by different climates are analyzed to identify scaling regimes and estimate corresponding UM parameters (α, C1) required by the UM cascade model. Suitable renormalization constants that correspond to the P, D, T values of reference rainfall are used to simulate an ensemble of reference rainfall scenarios, and the simulations are finally compared with datasets. Although only purely temporal simulations are considered here, this approach could possibly be generalized to higher spatial dimensions as well.

Compared to owning a private jet, Fractional Aircraft Ownership (FAO)concept is a cheaper alternative for very mobile business persons who want totravel in comfort. The aircraft is owned by a number of customers (referred to as“owners”) and the flight hours of its operation are shared based on each owner’sportion. In this research, we do the simulation of an FAO company with very largedemands with 27 cities of destination, which are commonly visited by businesspeople in Indonesia. We derive flight demands stochastically from the ownersand create optimal flying schedules based on the demands. Using the calculationof fixed and variable costs, we can determine the optimal flight pairings thatminimized the operational cost. Eventually, we can determine the number ofaircraft needed to be owned by FAO so the business will profit.

Coupling genetic and mechanistic models is appealing to explore the impact of energy trade-offs on the expression of feed efficiency traits in dairy cattle and predict selection response. The objective of this study was to evaluate the sensitivity of genetic (co)variances among milk production and feed efficiency (FE) traits simulated with a mechanistic dairy cow model depending on the genetic variability assumed for input parameters. The cow model was calibrated for a grass-based production and included a genetic module. Four genetically driven input parameters described the energy acquisition and allocation to different biological functions of cows. In each simulation, a population of 20 000 cows from 200 unrelated sires was simulated. The nutritional environment was an input of the model and was tailored by modulating feed offer and quality. A non-limiting nutritional environment was simulated to mimic a situation of ad libitum feeding and was used as a reference. Two other scenarios were simulated by imposing a moderate and a high DM intake restriction on simulated cows. Five phenotypes related to milk production and FE were considered: milk production, BW at calving, DM intake, lactation efficiency and body reserves during early lactation. These traits were estimated both in first and third lactations. A baseline scenario was defined considering a heritability of 0.35 and a phenotypic CV of 10% for acquisition and allocation parameters (AAPs). Different scenarios were explored by reducing the heritability to 0.15 or increasing CV to 20 and 30% or both. Heritabilities and genetic correlations between simulated traits were estimated using animal linear mixed models. Each scenario was replicated 20 times. Simulated performance and genetic parameters for these traits were compared across scenarios using an ANOVA. The heritability of AAPs only influenced the heritability of simulated traits. The phenotypic CV of AAPs mainly influenced the variability of simulated traits. However, increasing the CV also affected the number of cows reaching first and third lactation, due to the early culling of females with extreme AAPs profiles. Compared to other input parameters, the nutritional environment had the largest effect on both performance and genetic correlations between traits. Using a heritability value of 0.35 and a CV of 10% for all four AAPs enabled the simulation of milk production and FE performance with a realistic mean, variance and genetic correlations among traits in the three considered environments.

We construct a spatially-compartmental individual-based model of the spread of Covid-19 in indoor spaces. The model can be used to predict the infection rates in a variety of locations when various non-pharmaceutical interventions (NPIs) are introduced. Tasked by the Welsh Government, we apply the model to secondary schools and Further and Higher Education environments. Specifically, we consider student populations mixing in a classroom and in halls of residence. We focus on assessing the potential efficacy of Lateral Flow Devices (LFDs) when used in broad-based screens for asymptomatic infection or in ‘test-to-release’ scenarios in which individuals who have been exposed to infection are released from isolation given a negative result. LFDs are also compared to other NPIs; we find that, although LFD testing can be used to mitigate the spread of Covid-19, it is more effective to invest in personal protective equipment, e.g. masks, and in increasing ventilation quality. In addition, we provide an open-access and user-friendly online applet that simulates the individual-based model, complete with user tutorials to encourage the use of the model to aid educational policy decisions as input infection data evolves (https://bit.ly/CV19_INTER_IBM).

CO2 + O2 in-situ leaching (ISL) of sandstone-type uranium ore represents the third generation of solution mining in China. In this study, reactive transport modeling of the interaction between hydrodynamic and geochemical reactions is performed to enable better prediction and regulation of the CO2 + O2 in-situ leaching process of uranium. Geochemical reactions between mining solutions and rock, and the kinetic uranium dissolution controlled by O2 (aq) and bicarbonate ( $${\text{ HCO}}_{3}^{ - }$$ ) are considered in the CO2 + O2 ISL reactive transport model of a typical sandstone-hosted uranium ore deposit in northern China. The reactive leaching of uranium is most sensitive to the spatial distribution of the mineralogical properties of the uranium deposit. Stochastic geostatistical models are used to represent the uncertainty on the spatial distribution of mineral grades. A Monte Carlo analysis was also performed to simulate the uranium production variability over an entire set of geostatistical realizations. The ISL stochastic simulation performed with the selected geostatistical realizations approximates the uranium production variability well. The simulation results of the ISL reactive transport model show that the extent of the uranium plume is highly dependent on mineralogical heterogeneity. The uncertainty analysis suggests the effect of uranium grade heterogeneity was found to be important to improve the accurate capture of the uncertainty. This study provides guidance for the accurate simulation and dynamic regulation of the CO2 + O2 leaching process of uranium at the scale of large mining areas.

We develop adaptive replicated designs for Gaussian process metamodels of stochastic experiments. Adaptive batching is a natural extension of sequential design heuristics with the benefit of replication growing as response features are learned, inputs concentrate, and the metamodeling overhead rises. Motivated by the problem of learning the level set of the mean simulator response we develop four novel schemes: Multi-Level Batching (MLB), Ratchet Batching (RB), Adaptive Batched Stepwise Uncertainty Reduction (ABSUR), Adaptive Design with Stepwise Allocation (ADSA) and Deterministic Design with Stepwise Allocation (DDSA). Our algorithms simultaneously (MLB, RB and ABSUR) or sequentially (ADSA and DDSA) determine the sequential design inputs and the respective number of replicates. Illustrations using synthetic examples and an application in quantitative finance (Bermudan option pricing via Regression Monte Carlo) show that adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.

In synthetic biology, combinational circuits are used to program cells for various new applications like biosensors, drug delivery systems, and biofuels. Similar to asynchronous electronic circuits, some combinational genetic circuits may show unwanted switching variations (glitches) caused by multiple input changes. Depending on the biological circuit, glitches can cause irreversible effects and jeopardize the circuit's functionality. This paper presents a stochastic analysis to predict glitch propensities for three implementations of a genetic circuit with known glitching behavior. The analysis uses STochastic Approximate Model-checker for INfinite-state Analysis (STAMINA), a tool for stochastic verification. The STAMINA results were validated by comparison to stochastic simulation in iBioSim resulting in further improvements of STAMINA. This paper demonstrates that stochastic verification can be utilized by genetic designers to evaluate design choices and input restrictions to achieve a desired reliability of operation.

Currently, manufacturing enterprises attach great importance to improving processing quality and customer satisfaction. Hybrid flow shops have widespread applications in real-world manufacturing systems such as steel production and chemical industry. In a practical production process, uncertainty commonly arises due to the difficulty of knowing exact information of facilities and jobs beforehand. In order to improve processing quality and customer satisfaction of manufacturing systems in uncertain environments, this article proposes a stochastic multiobjective hybrid flow shop scheduling problem aiming at maximizing processing quality and minimizing total tardiness, where the processing time of jobs obeys a known random distribution. To describe jobs’ processing quality mathematically, a quality-based cost function is presented, and further a chance-constrained programming approach is used to formulate this problem. Then, a multiobjective artificial bee colony algorithm incorporating a stochastic simulation approach is designed by considering its characteristics. Simulation experiments are performed on a set of instances and several state-of-the-art multiobjective optimization algorithms are chosen as peer approaches. Experiment results confirm that the proposed algorithm has an excellent performance in handling this problem.

Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel implementations, inadequate. This paper introduces a new generation of the STochastic Engine for Pathway Simulation (STEPS) project (http://steps.sourceforge.net/), denominated STEPS 4.0, and its core components which have been designed for improved scalability, performance, and memory efficiency. STEPS 4.0 aims to enable novel scientific studies of macroscopic systems such as whole cells while capturing their nanoscale details. This class of models is out of reach for serial solvers due to the vast quantity of computation in such detailed models, and also out of reach for naive parallel solvers due to the large memory footprint. Based on a distributed mesh solution, we introduce a new parallel stochastic reaction-diffusion solver and a deterministic membrane potential solver in STEPS 4.0. The distributed mesh, together with improved data layout and algorithm designs, significantly reduces the memory footprint of parallel simulations in STEPS 4.0. This enables massively parallel simulations on modern HPC clusters and overcomes the limitations of the previous parallel STEPS implementation. Current and future improvements to the solver are not sustainable without following proper software engineering principles. For this reason, we also give an overview of how the STEPS codebase and the development environment have been updated to follow modern software development practices. We benchmark performance improvement and memory footprint on three published models with different complexities, from a simple spatial stochastic reaction-diffusion model, to a more complex one that is coupled to a deterministic membrane potential solver to simulate the calcium burst activity of a Purkinje neuron. Simulation results of these models suggest that the new solution dramatically reduces the per-core memory consumption by more than a factor of 30, while maintaining similar or better performance and scalability.

Izrađen je računalni kod, koji računa tortuozitet, tj. koeficijent zakrivljenosti pora iz digitalnog uzorka segmentirane i binarizirane CT slike stijene, za koju su, na temelju prethodno izrađenih modula (in-house kód RGN fakulteta), određeni povezani putevi kroz porni sustav. Algoritam se služi stohastičkim pristupom tj. koristi modificiranu metodu slučajnog hoda te se za skretanje putanje unutar pornih kanala koristi normalna raspodjela za koju je moguće definirati različite standardne devijacije. Uz standardnu devijaciju veću od 1,0 ustanovljeno je da rezultati postaju sličniji i da se poklapaju s nekim algoritmima drugih autora, koji su također testirani na istom digitalnom uzorku., Tortuosity, or the curvature coefficient of pores, was computed using a digital sample of a segmented and binarized CT image of the rock and interconnected pathways across the pore system, which were found using previously created modules (in-house code of the RGN Faculty). The program employs a stochastic approach, i.e., a modified random walk technique with a normal distribution, for which different standard deviations can be chosen to simulate the drift from the shortest path inside the pore channels. When the standard deviation exceeds 1.0, the outputs become more comparable and correspond with several algorithms developed by other authors that have been evaluated using the same digital sample.

Penalty function with memory (PFM) in Park and Kim [2015] is proposed for discrete optimization via simulation problems with multiple stochastic constraints where performance measures of both an objective and constraints can be estimated only by stochastic simulation. The original PFM is shown to perform well, finding a true best feasible solution with a higher probability than other competitors even when constraints are tight or near-tight. However, PFM applies simple budget allocation rules (e.g., assigning an equal number of additional observations) to solutions sampled at each search iteration and uses a rather complicated penalty sequence with several user-specified parameters. In this article, we propose an improved version of PFM, namely IPFM, which can combine the PFM with any simulation budget allocation procedure that satisfies some conditions within a general DOvS framework. We present a version of a simulation budget allocation procedure useful for IPFM and introduce a new penalty sequence, namely PS 2 + , which is simpler than the original penalty sequence yet holds convergence properties within IPFM with better finite-sample performances. Asymptotic convergence properties of IPFM with PS 2 + are proved. Our numerical results show that the proposed method greatly improves both efficiency and accuracy compared to the original PFM.

Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.

The research, through a bidirectional approach, aims to develop the university management of the degree programs in order to attract and maintain the potential students, as well as to estimate and manage the costs of the educational services offered during their life cycle. The authors bring into the centre of the study the importance of the simulation process in predicting a possible future and reduce the probable risks and costs from time. The results confirm that the simulation methods used help the universities to determine the future risks and to predict when and what relationship marketing programs to use in order to attract, maintain and grow the number of valuable students and also to ensure cost-effective management of university programs.

We study the transition of an epidemic from growth phase to decay of the active infections in a population when lockdown measures are introduced to reduce the probability of disease transmission. While in the case of uniform lockdown a simple compartmental model would indicate instantaneous transition to decay of the epidemic, this is not the case when partially isolated active clusters remain with the potential to create a series of small outbreaks. We model this using a connected set of stochastic susceptible-infected-removed/recovered (SIR) models representing the locked-down majority population (where the reproduction number is less than one) weakly coupled to a large set of small clusters where the infection may propagate. We find that the presence of such active clusters can lead to slower than expected decay of the epidemic and significantly delayed onset of the decay phase. We study the relative contributions of these changes to the additional total infections caused by the active clusters within the locked-down population. We also demonstrate that limiting the size of the inevitable active clusters can be efficient in reducing their impact on the overall size of the epidemic outbreak.Statement of SignificanceRestricting movement and interaction of individuals has been widely used in trying to limit the spread of COVID-19, however, there is limited understanding of the efficiency of these measures as it is difficult to predict how and when they lead to the decay of an epidemic. In this article, we develop a mathematical framework to investigate the transition to the decay phase of the epidemic taking into account that after lockdown a large number of active groups remain with the potential to produce localised outbreaks affecting the overall decay of infections in the population. Better understanding of the mechanism of transition to the decay of the epidemic can contribute to improving the implementation of public health control strategies.

The increase of sulfide (S2−) during the water flooding process has been regarded as an essential and potential risk for oilfield development and safety. Kriging and stochastic simulations are common methods for assessing the element distribution. However, these traditional simulation methods are not able to predict the continuous changes of underground S2− distribution in the time domain by limited known information directly. This study is a kind of attempt to combine stochastic simulation and the modified probabilistic neural network (modified PNN) for simulating short-term changes of S2− concentration. The proposed modified PNN constructs the connection between multiple indirect datasets and S2− concentration at sampling points. These connections, which are treated as indirect data in the stochastic simulation processes, is able to provide extra supports for changing the probability density function (PDF) and enhancing the stability of the simulation. In addition, the simulation process can be controlled by multiple constraints due to which the simulating target has been changed into the increment distribution of S2−. The actual data test provides S2− distributions in an oil field with good continuity and accuracy, which demonstrate the outstanding capability of this novel method.

As an essential part of the hydrological cycle, precipitation directly contributes to surface runoff and river runoff formation. Simulation on precipitation variables can effectively solve the adverse effects on hydrological assessment in some areas with insufficient or even no runoff observation. With the widespread use of various weather generators, the traditional stochastic hydrological simulation methods tend to be gradually replaced. To compare these two approaches mentioned above, this paper utilizes the precipitation records from 1958 to 2011 at nine meteorological stations within Huaihe River System in Henan Province to evaluate a stochastic hydrological simulation method, SARIMA model, and two types of weather generators, WeaGETS and LARS-WG, through the comparison of statistical characteristics regarding precipitation variables, such as mean, mean square error, extreme value and coefficient of variation. The results show that (1) on the annual scale, SARIMA has a better performance to reproduce the mean and mean square error as well as the extreme precipitation events than weather generators; (2) regarding the monthly-scale precipitation simulation, SARIMA is good at reproducing the statistical properties of monthly precipitation at the average level, while WeaGETS and LARS-WG work better in simulating monthly precipitation extremes; (3) compared with weather generators, SARIMA is highly constrained by the observed records, and among these two weather generators, WeaGETS scores higher on monthly precipitation simulation under the same sample length conditions. In conclusion, the traditional hydrological simulation method, SARIMA, and weather generators, WeaGETS and LARS-WG, have both benefits and drawbacks. The appropriate choice depends on different research backgrounds and purposes.

The developed method for forecasting the survivability indicators of the executive element of a special-purpose system based on analytical-stochastic simulation of a conflict situation is presented. The purpose of the method was to solve the problem of preserving and rational use of the resource of the executive elements to achieve the desired effect of the functioning of special-purpose systems. The method is sensitive to the description of the patterns of changes in the survivability and efficiency indicators of the system. It is supposed to compare the predicted value of the survivability indicator with its criterion value and forecast the time when the system loses the ability to effectively perform tasks. The survivability indicator is the mathematical expectation of the number of executive elements of a special-purpose system, which retained their ability to perform tasks as intended during a conflict situation. Based on the results of the study, the values of the time characteristics of a conflict situation were obtained, in particular, the duration of the corresponding states of the executive element: preparation, waiting, implementation. Graph-analytical simulation provides a solution to the problem of forecasting the time when the loss of executive elements leads to the system’s inability to effectively perform tasks. Checking of the adequacy of the method showed that the confidence interval of the discrepancy between the calculation results of other methods with a confidence level of 0.9 does not exceed 0.095, and no contradictions between the methods were found. The proposed method provides an increase in the efficiency of determining the corresponding indicators within 8–11% and reliability by 22%. The possibility to determine the required reserve and the time for introducing executive elements into the system can provide a justification for how they are used to maintain the required level of efficiency of a special-purpose system.

Noise can drive the dynamics of stochastic systems to different important states. Delay is another significant parameter that may impart non-Markovian behavior in the system dynamics. The interplay of noise and delay can exhibit interesting, complex behaviors in stochastic systems. In this work, we considered the stochastic gene expression model and studied this interplay of noise and delay in describing the functioning of a gene via transcription and translation processes. The calculated probability distributions of mRNA and protein, both in non-delay and delay, are found to obey certain universal classes, namely Poisson distribution at $$u,N\rightarrow large$$ limit, and Normal distribution at $$u,\langle u\rangle ,N\rightarrow large$$ limit. Analytical result of noise, measured by the Fano factor, indicates that, both in delay and non-delay cases, the gene expression system follows sub-Poissonian processes when the values of parameters are far from asymptotic values and that it becomes Poissonian at asymptotic values of the system parameters. We provided a detailed study of the noise using the Fano Factor with respect to different parameters such as mean, initial population, and time delay for the gene expression process. Again, the stochastic simulation results of the model indicate the transition of mRNA states (low and high transcription and translation) driven by the translation rate.

Because of the scarcity of recorded seismic ground motions from Vrancea intermediate-depth source that can be used in designing a structure, engineers are bound to use scaled, artificial, or simulated accelerograms. Out of these options, the first two might incompletely account for the phenomena that may appear. Although complicated, simulated accelerograms provide one of the best options for defining the seismic demand in engineering design, but further research is needed to adapt the simulation methods to the source, path, and site-specific characteristics. As some parameters used in simulations were not addressed yet specifically for the Vrancea-intermediate seismic source, the specific path duration is investigated in this paper. Using a database with the recorded ground motions from March 4, 1977, August 30, 1986, May 30 and May 31, 1990, October 27, 2004 Vrancea earthquakes (the only five recorded earthquakes with moment magnitudes at least equal to 6), the path and magnitude dependent duration specific to Vrancea intermediate-depth seismic source to be used in the stochastic simulation is developed.

Metamodels are an important tool in simulation analysis as they can provide insight about the behavior of the simulation response. Modeling the response with low-degree polynomial segments allows the identification of different behavior zones and the parameters still have relation with the physical world. The purpose of this paper is to extend the use of segmented polynomial functions for simulation metamodeling, where the segments have at most identical value and slope at the breaks. Our approach is to build segmented polynomials metamodels where the hypothesis of degree and continuity of splines are less exigent, allowing more flexibility of the approximation. When breaks are known, constrained least squares are used for metamodel estimation, taking into account the linear formulation of the problem. If breaks have to be estimated, the unconstrained nonlinear regression theory is used, when it can be applied. Otherwise, the estimation is performed using an iterative algorithm which is applied repeatedly in a cyclic manner for estimating the breaks, and jackknifing yields the confidence intervals.

Discrete-time stochastic systems are an essential modelling tool for many engineering systems. We consider stochastic control systems that are evolving over continuous spaces. For this class of models, methods for the formal verification and synthesis of control strategies are computationally hard and generally rely on the use of approximate abstractions. Building on approximate abstractions, we compute control strategies with lower- and upper-bounds for satisfying unbounded temporal logic specifications. Firstly, robust dynamic programming mappings over the abstract system are introduced to solve the control synthesis and verification problem. These mappings yield a control strategy and a unique lower bound on the satisfaction probability for temporal logic specifications that is robust to the incurred approximation errors. Secondly, upper-bounds on the satisfaction probability are quantified, and properties of the mappings are analysed and discussed. Finally, we show the implications of these results for linear stochastic dynamic systems with a continuous state space. This abstraction-based synthesis framework is shown to be able to handle infinite-horizon properties. Approximation errors expressed as deviations in the outputs of the models and as deviations in the probabilistic transitions are allowed and are quantified using approximate stochastic simulation relations.

Soybean rust (SBR) in Brazil is controlled with fungicides, which have shown variable, eventually declining, efficacy. A Monte Carlo simulation framework was proposed to approximate profitability depending on the fungicide program's efficacy and total cost. Probability distributions were fitted to slopes and intercepts of the disease-yield relationship and severity in the untreated plots reported in the literature, as well as historical records of soybean price. Simulations of disease reduction conditioned to predefined control efficacy and total application costs were split into scenarios that combined two categories of severity (high and low) and two attainable yield classes (high and low). These categories were defined based on the median of severity (57.8%) and median of the intercept (yield when severity is zero, 2995.1 kg/ha). Probability matrices were constructed relating fungicide efficacy and costs. A higher frequency of break-even events occurred in scenarios of high disease pressure and higher yield. Yearly simulations, starting with 79.4% efficacy, assuming two rates of decline determined for tebuconazole (high decline), showed that the program may remain profitable during the first 5 to 7 years of use. Contrasting to cyproconazole, a fungicide that would be profitable during the entire decade. These simulations can be useful to aid in decision-making when planning fungicide programs. This approach can be adapted to other diseases of soybean and other crops as long as damage functions are available. An interactive web app was developed to perform the simulations accessible at alvesks.shinyapps.io/rusty-profits/.

Featured Application Potential applications of this work regard the parameter estimation and the resulting analyses of complex biochemical systems characterized by a stochastic behavior, which allows for elucidating the unknown underlying mechanisms. The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the biochemical species appearing in the system can be low, making biological noise a non-negligible phenomenon and mandating the use of stochastic simulation. Finally, the values of the kinetic parameters typically follow a log-uniform distribution; thus, the optimal solutions are situated in the lowest orders of magnitude of the search space. In this work, we further elaborate on a novel approach to address the PE problem based on a combination of adaptive swarm intelligence and dilation functions (DFs). DFs require prior knowledge of the characteristics of the fitness landscape; therefore, we leverage an alternative solution to evolve optimal DFs. On top of this approach, we introduce surrogate Fourier modeling to simplify the PE, by producing a smoother version of the fitness landscape that excludes the high frequency components of the fitness function. Our results show that the PE exploiting evolved DFs has a performance comparable with that of the PE run with a custom DF. Moreover, surrogate Fourier modeling allows for improving the convergence speed. Finally, we discuss some open problems related to the scalability of our methodology.

Various gas pipeline networks used for the transit of energy sources are some of the most important infrastructures. However, carrying gas from one point to another is not the only concern when planning the construction of a new network or expanding an already existing one. The reliability and environmental impact of the system are crucial when evaluating the network and risks posed by potential gas leaks, fires, explosions, etc. Even though everyone admits that reliability is a key aspect of any system, its constraints will still be most likely neglected in the assessment of the pipeline project. How much energy is wasted by pushing an additional amount of gas through the pipeline network, which will eventually gush out of the pipeline because of one crack or another? Moreover, if this additional power or fuel consumption and related environmental impact are significant, how could it be reduced? In this paper, an approach is introduced for the simulation and quantification of how much more power would be required if the pipelines are regarded as unreliable (i.e., by leaking, rupturing, or even exploding). By employing stochastic simulations and time-dependent topology (topology determined by the value of a variable representing time) of the pipeline network as a case study for the selected representative gas transmission network, the amount of additional power consumption in gas compressor stations due to uncertain cracking and the leaking rate was evaluated. Although the analysis of power consumption was performed for a hypothetical network, the estimates of the cracking rates, detection effectiveness, and leaking rates used were as close to the real cases as possible.