Search

This paper proposes a numerical modeling method to construct finite element models of randomly pitted steel Circular Hollow Section (CHS) T-joints. A series of parametric analyses were conducted on joints under axial brace compression to study the effect of geometric properties, such as the brace-to-chord diameter ratio, β , and chord diameter-to-thickness ratio, 2 γ , on the ultimate strength under varying degrees of volume loss of corroded material (DOV). The synergy of key geometric properties and corrosion damage was clarified. The results indicated that the joints with a smaller β or a larger 2 γ exhibited a larger reduction in ultimate strength under the same DOV. Moreover, due to the stress concentration stemming from irregular corrosion pits, the ultimate strength of a randomly pitted joint was nearly 10% lower than that with general corrosion. In particular, a significant variation in the ultimate strength of a randomly pitted joint was observed under a certain DOV, following a normal distribution. A correction factor was proposed to account for the impact of pitting corrosion on ultimate strength. This factor was then utilized to modify the CIDECT mean strength equation, leading to high accuracy in predicting the ultimate strength, with mean relative errors of less than 5%. • Propose a novel modeling method to mimic random pitting corrosion on CHS T-joints. • Identify key geometric properties affecting the ultimate strength of randomly pitted joints. • Clarify the synergy of pitting corrosion and geometric properties on strength reduction. • Define a correction coefficient to account for the pitting damage effect on ultimate strength. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for analyzing seismic accelerograms using the evolutionary Power Spectral Density function (EPSD). The accelerogram of an earthquake can be accurately modeled and simulated from its spectrogram, based on the oscillatory stochastic processes theory. To adequately characterize a spectrogram that is consistent with the response spectra, a parametric model with 16 parameters is proposed. This model describes the square of the amplitude spectrum, an envelope of the square of the accelerogram, and a copula that constructs a time–frequency model from the time and frequency marginals. The use of copulas to model a bivariate probability distribution is a common practice in statistics, particularly when the marginal distributions are known. The periodogram can be viewed as an unnormalized probability density function, where the total energy serves as the normalization constant, since the total energy of a seismic motion is always finite. Additionally, a reduced model consisting of only 10 parameters is presented, which may be especially valuable when only shear wave effects are relevant. • Low dimensional representation of an accelerogram. • Design a framework for attenuation laws that does not rely on Boore's random vibration theory. • The framework is compatible with previous attenuation laws for amplitude spectrum and accelerogram time envelopes. [ABSTRACT FROM AUTHOR]

Performance-based wind engineering (PBWE) is gaining consensus as an alternative to current wind design procedures for its potential to allow the explicit evaluation of system performance over a full range of performance objectives including collapse. To achieve the maximum benefits from PBWE, advanced computational approaches that enable the efficient prediction of the inelastic performance, as well as probabilities of failure under extreme winds, are essential. In this paper, a stochastic simulation-based framework is proposed to efficiently estimate the reliabilities/probabilities of failure of reinforced concrete (RC) structures subject to a full range of wind intensities. The framework is based on integrating a high-fidelity nonlinear modeling environment with a wind-tunnel-informed stochastic wind load model. An optimal stratified sampling scheme is adopted to propagate structural and load uncertainties and subsequently estimate the small probabilities of failure characterizing collapse with limited computational efforts. Through the illustration on a 45-story archetype RC building with a hypothetical location in New York City, the inelastic behavior and the subsequent reliabilities associated with various collapse limit states of interest are investigated. In particular, typical collapse mechanisms for the RC structures under extreme winds are explicitly captured by evaluating the progression of the localized damage, including reinforcing bar yielding, fracture, low-cycle fatigue failure, and concrete crushing. • High-fidelity modeling of wind-excited reinforced concrete structures is presented. • Efficient estimation of small failure probabilities is enabled by stratified sampling. • Limits states ranging from component failure to system collapse are studied. • A full-scale archetype structure is used to illustrate the modeling framework. • Insight into the reliability of reinforced concrete structures subject to extreme winds is gained. [ABSTRACT FROM AUTHOR]

In a companion paper, Ojeda-Rojas et al. (2021) [1] describe a stochastic agent-based simulation (ABS) model of a cow-calf operation on a commercial farm in São Paulo, Brazil. The model's parameterization was based on data collected from two sources: a real beef cattle herd and related scientific literature. Based on the mentioned simulation model, this study aims to assess the economic outcome of 10 different reproductive scenarios: Natural mating only (ONM); one timed artificial insemination (TAI) plus natural mating (NM) (1TAI + NM); two TAI plus NM, with 24, 32, and 40 days between TAI (2TAI/24 + NM, 2TAI/32 + NM, and 2TAI/40 + NM, respectively); three TAI without NM, with 24, 32, and 40 days between TAI (3TAI/24, 3TAI/32, and 3TAI/40, respectively); and three TAI plus NM, with an interval between TAI of 24 (3TAI/24 + NM) and 32 days (3TAI/32 + NM). The simulation was performed on an animal-by-animal basis over a time horizon of 5000 days. Each scenario had 32 farms, and each farm kept up to 400 adult females. According to the scenario, a bull population was composed of 0, 7, or 15 individuals. The outcomes, represented as means ± standard deviations, were assessed after reaching a steady-state (1825 days). The model outcomes showed that the 3TAI/24 + NM scenario resulted in higher incomes (US$ 96,479.19 ± 709.81), whereas the ONM scenario had the lowest incomes (US$ 79,753.37 ± 741.87). The 3TAI/24 + NM (US$ 101.720.63 ± 79.21) and ONM (US$ 90.898.58 ± 59.17) scenarios presented the highest and lowest total operating costs (TOC), respectively. However, when TOC was evaluated per kg of the weaned calf, the highest and lowest costs were associated with the ONM (US$ 2.81 ± 0.03/kg) and 2TAI/24 + NM (US$ 2.17 ± 0,04/kg) scenarios, respectively. Our model suggests that reproductive strategies that use TAI have a better economic performance than those under NM. However, when performing three TAI with an interval of 40 days, the benefit was lower; in some cases, it was even worse than the ONM. Combining TAI with early pregnancy diagnosis resulted in better economic performance than other TAI programs and NM. The 2TAI/24 + NM scenario outperformed the others due to the contrast between its high income and moderate costs. Beef cattle production is a highly complex system. Simulations models, specifically ABS models, could make the decision-making process on complex systems straightforward and effective. Furthermore, ABS models can overcome the limitations of conventional research approaches, such as high costs and long experimentation periods. • A stochastic agent-based model was used to simulate a cow-calf operation. • We evaluate the economic performance of using natural mating alone or with TAI. • The analysis considers the number of TAIs (1, 2, or 3), days between them (24, 32, and 40), and early pregnancy diagnosis. • Combining TAI with early pregnancy diagnosis resulted in a better economic performance. • The 2TAI/24 + NM scenario outperformed the others due to its high income and moderate costs. [ABSTRACT FROM AUTHOR]

• This paper proposed a novel framework for evaluating the sustainability of urban agglomerations. • Significant regional disparities in urban agglomeration sustainability were observed from 2012 to 2021. • The 12 urban agglomerations exhibited inconsistencies of economic, social, and environmental sustainability. • The growth rate of sustainability values for the 12 urban agglomerations demonstrated a downward trajectory. The sustainable development of urban agglomerations represents a significant driving force in national and global development. This study establishes an indicator system comprising factors associated with the economy, society, and environment, in accordance with the Triple Bottom Line, to assess the sustainability of 12 urban agglomerations in China. A novel framework is proposed, including a dynamic probability weighting method based on sufficient stochastic simulations and a coordination-difference-driven aggregation approach that considers the coordination degree and differences between evaluated objects. The evaluation revealed significant regional disparities in urban agglomeration sustainability from 2012 to 2021. The eastern region's Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei region, and Shandong Peninsula exhibit above-average sustainability performance. Conversely, the western region's Guangzhong, Guangxi Beibu Gulf, Chengyu, and Ningxia Yellow River regions exhibit below-average performance. Moreover, the growth rate of sustainability values for the 12 urban agglomerations followed a downward trajectory. Furthermore, the environmental dimension is the primary driver of sustainable development in urban agglomerations, while the economic dimension represents the main obstacle. These findings offer policymakers a scientific and practical framework to guide sustainability-related decisions. [ABSTRACT FROM AUTHOR]

Surrogate modelling techniques have opened up new possibilities to overcome the limitations of computationally intensive numerical models in various areas of engineering and science. However, while fundamental in many engineering applications and decision-making, the incorporation of uncertainty quantification into meta-models remains a challenging open area of research. To address this issue, this paper presents a novel stochastic simulation approach combining sparse polynomial chaos expansion (PCE) and Stochastic Kriging (SK). Specifically, the proposed approach adopts adaptive sparse PCE as the trend model in SK, achieving both global and local prediction capabilities and maximizing the role of the stochastic term to conduct uncertainty quantification. To maximize the generalization and computational efficiency of the meta-model, the Least Angle Regression (LAR) algorithm is adopted to automatically select the optimal polynomial basis in the PCE. The computational effectiveness and accuracy of the proposed approach are appraised through a comprehensive set of case studies and different quality metrics. The presented numerical results and discussion demonstrate the superior performance of the proposed approach compared to the classical ordinary SK model, offering high flexibility for the characterization of both extrinsic and intrinsic uncertainty for a wide variety of problems. [ABSTRACT FROM AUTHOR]

• Uncertainty propagation of thermomagnetic convection in porous media with random porosity was investigated. • The input and output stochastic fields are represented by the Karhunen-Loève expansion and polynomial chaos expansion, respectively. • The decoupled governing equations of thermomagnetic convection are solved by the finite volume method. • The thermomagnetic convection of paramagnetic fluid shows a remarkable chaos effect. • The results from the intrusive polynomial chaos expansion method agree quite well with those from the Monte Carlo simulation. Understanding and quantifying uncertainty factors are crucial for accurately predicting thermomagnetic convection phenomena. This study presented a mathematical model and algorithm framework for uncertainty analysis of thermomagnetic convection in porous media with random porosity. The proposed method combined Karhunen-Loève expansion and intrusive polynomial chaos expansion to represent the input random parameters and output response, respectively. The Galerkin projection method was employed to decouple the stochastic governing equations of thermomagnetic convection into deterministic governing equations that can be efficiently solved using the finite volume method. By solving the polynomial chaos expansion of the output response and employing the stochastic projection method to solve the associated decoupled governing equations, the temporal evolution and statistical characteristics of the output response were obtained. The study revealed that porosity uncertainty in the porous medium affects the thermomagnetic convection of paramagnetic fluid, exhibiting significant chaos effects. Monte Carlo simulations were performed to validate the accuracy of the proposed approach and demonstrate its computational efficiency compared to traditional Monte Carlo methods. This research provides valuable insights into the uncertainty analysis of thermomagnetic convection and offers a promising methodology for analyzing heat transfer in various porous media applications. [ABSTRACT FROM AUTHOR]

The ability of sociotechnical systems to recover from disruptions through decentralized, bottom-up processes is a crucial element of their overall resilience, but it is also among the most overlooked in resilience research. This paper proposes a computational framework leveraging dynamic multi-agent networks to conceptualize, model, and quantify multi-agent, decentralized recovery process building upon strategic network formation methods. This innovative approach addresses limitations in conventional strategic network formation models by accounting for the fluidity of structural shifts induced by the strategic behaviors of individual agents, and by acknowledging the inherent heterogeneity within agent populations. The central objective of this study is to highlight the critical role of bottom-up recovery and present a systematic, adaptable framework for modeling these processes in diverse real-world scenarios. We achieve this through four key elements: modeling post-disruption responses, exploring their interdependencies, accommodating multiple equilibria, and generating metrics for scenario comparison and forecasting. Our dynamic strategic network formation model offers valuable insights into crucial trade-offs in decentralized recovery processes and facilitates the integration of both bottom-up and top-down resilience strategies. Furthermore, it could serve as a predictive tool informing initial network architectural decisions by estimating likely post-disruption equilibrium states based on the initial network structure. Ultimately, this study provides a robust platform for analyzing network efficiency and resilience trade-offs, setting the stage for more informed decision-making in complex sociotechnical systems. • Bottom-up recovery is crucial for resilience of sociotechnical systems. • Top-down interventions need to incorporate models of bottom-up processes. • We propose a multi-agent network framework for modeling Bottom-up recovery. • Framework includes metrics to evaluate the role of top-down interventions. [ABSTRACT FROM AUTHOR]

Incomplete luteal regression after treatment with a single dose of PGF 2α during an Ovsynch protocol decreases fertility to timed artificial insemination (AI). An additional treatment with PGF 2α 24 h after the first dose has been recommended to increase the proportion of cows with complete luteal regression and subsequent pregnancy per AI (P/AI). This is, however, associated with additional costs of labor and product. The objective was to develop a stochastic partial budget model to estimate the economic impact of an additional PGF 2α treatment on d 8 during an Ovsynch protocol in primiparous and multiparous cows. A systematic review of the literature and a meta-analytical assessment was performed to evaluate the effects of adding a second PGF 2α treatment during the Ovsynch protocol on P/AI in lactating dairy cows. Thirteen randomized controlled experiments from 11 published manuscripts including 9,735 cows were used. We were able to retrieve information regarding parity from 9 experiments from 7 manuscripts (2,367 primiparous cows and 5,356 multiparous cows). An additional dose of PGF 2α yielded a 5.60 risk difference in P/AI [95% confidence interval (CI) = 3.69–7.52]. In primiparous cows and multiparous cows, an additional dose of PGF 2α yielded a 4.24 (95% CI = 0.31–8.17) and a 5.31 risk difference in P/AI (95% CI = 2.75–7.87). Revenue was based on the associated improvement in reproductive performance because of an increase in P/AI multiplied by the value of a pregnancy (PGVAL). Median PGVAL was €252, ranging from €42 (fifth percentile) to €623 (95th percentile). Based on parity, median PGVAL was €205 (5th percentile = €43; 95th percentile = €651) and €264 (5th percentile = €88; 95th percentile = €598) for primiparous (n = 1,252) and multiparous cows (n = 3,003), respectively. Using a stochastic simulation model with 10,000 iterations, adding a second PGF 2α dose on d 8 was more profitable (€7.76/cow; 5th percentile = €0.01; 95th percentile = €29.40) compared with a single PGF 2α administration on d 7. In primiparous cows, adding a second PGF 2α treatment was more profitable (€1.99/cow; 5th percentile = −€3.08; 95th percentile = €22.52) in 67% of all simulated iterations. In multiparous cows, adding a second PGF 2α treatment was more profitable (€7.92/cow; 5th percentile = −€0.09; 95th percentile = €28.22) in 95% of all simulated iterations. In conclusion, there was a clear benefit of an additional PGF 2α treatment during the Ovsynch protocol on P/AI (+5.6 percentage units). Despite additional costs for hormones and labor, an additional treatment with PGF 2α on d 8 was more profitable in 95% of all scenarios because of the associated increase in fertility. It turned out, however, that adding a second PGF 2α treatment was more profitable in multiparous cows compared with primiparous cows. [ABSTRACT FROM AUTHOR]

The main aim of the article was to propose an expert intermittent demand forecasting system. This system is based on stochastic simulation but also has many adjustments that might be treated as expert corrections. The presented forecasting system was applied in a company that sells mostly tools and work clothes. The number of forecasted items is over 16,000. The expert system was compared with the most popular intermittent demand forecasting methods (based on exponential smoothing) such as Croston's, SBA, TSB or SES methods. For each of these methods a separate forecasting system was constructed. Smoothing factors as well as initial values for all forecasts were optimized. Forecasting accuracy measures taking into account the specificity of an intermittent demand time series were applied. Also, a new forecast error measure (scaled Absolute Mean Error) was proposed and used in the comparisons. [ABSTRACT FROM AUTHOR]

National and international across-population selection is often recommended and fairly common in the current breeding practice of dairy cattle, with the primary aims to increase genetic gain and genetic variability. The aim of this study was to test the hypothesis that the strategy of truncation selection of sires across populations [i.e., competitive gene flow strategy (CGF)] may not necessarily maximize genetic gain in the long term in the presence of genotype-by-environment interaction (G×E). Two alternative strategies used to be compared with CGF were forced gene flow (FGF) strategies, with 10 or 50% of domestic dams forced to be mated with foreign sires (FGF10%, FGF50%). Two equal-size populations (N dams = 1,000) that were selected for the same breeding goal trait (h2 = 0.3) under G×E correlation (r g) of either 0.9 or 0.8 were simulated to test these 3 different strategies. Each population first experienced either 5 or 20 differentiation generations (G d), then 15 migration generations. Discrete generations were simulated for simplicity. Each population performed a within-population conventional breeding program during differentiation generations and the 3 across-population sire selection strategies based on joint genomic prediction during migration generations. The 4 G d_ r g combinations defined 4 different levels of differentiation degree between the 2 populations at the start of migration. The true rate of inbreeding over the last 10 migration generations in each scenario was constrained at 0.01 to provide a fair basis for comparison of genetic gain across scenarios. Results showed that CGF maximized the genetic gain after 15 migration generations in 5_0.9 combination only, the case of the lowest differentiation degree, with a superiority of 0.4% (0.04 genetic SD units) over the suboptimal strategy. While in 5_0.8, 20_0.9, and 20_0.8 combinations, 2 FGF strategies had a superiority in genetic gain of 2.3 to 12.5% (0.21–1.07 genetic SD units) over CGF after 15 migration generations, especially FGF50%. The superiority of FGF strategies over CGF was that they alleviated inbreeding, introduced new genetic variance in the early migration period, and improved accuracy in the entire migration period. Therefore, we concluded that CGF does not necessarily maximize the genetic gain of across-population genomic breeding programs given moderate G×E. The across-population selection strategy remains to be optimized to maximize genetic gain. [ABSTRACT FROM AUTHOR]

Quantifying the near-fault effect and establishing a reasonable model of near-fault pulse-like ground motions are particularly important for seismic design of structures in near-fault regions. Given the pronounced randomness associated with earthquakes, this study first proposes a novel stochastic model of near-fault pulse-like ground motions by combining the improved finite-fault model (IFFM) and the multivariate copula-based velocity-pulse model (CVPM). Further, a probability density evolution method (PDEM) based stochastic simulation method is developed, by which the model parameters can be determined in a unified probability space so as to ensure the consistency of two independent models. For illustrative purposes, the observed records collected from the 1999 Chi-Chi earthquake are used to generate new stochastic ground motions set. Two ground motions sets based on classical stochastic simulation methods are also presented for comparison. Numerical results show that the proposed method for stochastic simulation of near-fault pulse-like ground motions is reliable; the statistics of peak ground accelerations and spectral characteristics of simulated samples are consistent with station records. Besides, the proposed method accommodates the noteworthy randomness and proportion consistency of components associated with near-fault pulse-like ground motions, making it suitable for the stochastic response and reliability analysis of seismic structures in near-fault regions. This superiority is challenging to classical stochastic simulation methods that lack reasonable consideration of randomness and correlation associated with model parameters. • Modeling is performed by combining improved finite-fault model and multivariate copula-based velocity-pulse model. • Probability density evolution method is employed to ensure the consistency of combined models. • Simulation results are verified by using station records collected from 1999 Chi-Chi earthquake. • The proposed method enables reasonable consideration of randomness and correlation associated with model parameters. [ABSTRACT FROM AUTHOR]

Currently, many countries are suffering from a heavy burden caused by population aging since the elderly occupy a large number of medical resources. Home health care (HHC) is a prospective approach to relieve and cope with this issue. This study addresses a multi-objective HHC routing and scheduling problem (HHCRSP) considering uncertainties and caregivers' working time balance. First, a stochastic programming method is employed to formulate the uncertain service time and travel time, and then a chance-constrained programming model is developed to minimize the total operation cost and penalty cost of violating allowable time windows. Second, a multi-objective migrating birds optimization incorporating a stochastic simulation method is proposed. An ensemble of population iteration and external archive search is designed to obtain high-quality solutions. A stochastic simulation strategy is devised to evaluate the feasibility of obtained solutions and calculate their objective values. At last, numerical experiments are implemented for solving an extending benchmark set. The comparisons and discussions verify the better performance of the proposed algorithm compared with its peers. [ABSTRACT FROM AUTHOR]

The objective of this study was to create a stochastic, agent-based simulation model of a synthetic population of beef cattle, and then use it to compare the technical performance of different reproductive strategies. The model was parameterized using data from a real beef cattle herd and from the peer-reviewed scientific literature to represent a Nelore cattle herd in the state of São Paulo, Brazil. Ten scenarios were evaluated: natural mating (NM) only (ONM); one timed artificial insemination (TAI) plus NM (1TAI + NM); two TAI plus NM, with 24, 32, and 40 days between inseminations (2TAI/24 + NM, 2TAI/32 + NM, and 2TAI/40 + NM, respectively); three TAI without NM, with 24, 32, and 40 days between TAI (3TAI/24, 3TAI/32, and 3TAI/40, respectively); and three TAI plus NM, with 24 and 32 days (3TAI/24 + NM and 3TAI/32 + NM, respectively). NM began 10 days after the last TAI and was performed until the end of the breeding season. The size of the female herd was set to contain up to 400 individuals. The bull population was established at 0, 7, or 15 bulls depending on the used scenario. Simulation was performed for 5000 days. The outcomes for each scenario are means ± S.E. assessed on 32 farms at 1-day time intervals and on an animal-by-animal basis after steady state was reached (1825 days). The 3TAI/24 + NM scenario resulted in a greater number of births (279.85 ± 0.47 births), while the ONM scenario had the least value (202.38 ± 0.43 births). The heaviest males and females at weaning belonged to 3TAI/24, with 190.85 ± 0.17 kg for males and 173.89 ± 0.13 kg for females. The ONM scenario had the lightest males (166.84 ± 0.18 kg) and females (151.75 ± 0.16 kg). The greatest and least total pregnancy rates were found in 3TAI/24 + NM (0.91 ± 0.00) and ONM (0.62 ± 0.00), respectively. The ONM scenario required 52.5 days more than scenarios that included TAI to reach 50% of pregnancy. The greatest ages at culling for cows was 3TAI/24 + NM (3658.88 ± 10.41 days). In contrast, the lowest age at culling was found in ONM (2823.93 ± 8.28 days). We concluded that the proposed model represents the main interactions of a real beef cattle herd. It has all the advantages of a physical experiment, but does not require incurring significant expenses nor altering the real system. This study offers evidence that the scenarios that present the best technical performance are those that used TAI with a 24-day interval between inseminations. • A stochastic agent-base model was created to simulate a cow-calf operation. • Performance of using natural mating alone or in combination with TAI was evaluated. • Number of TAI (1, 2 or 3) and days between them (24, 32 and 40) were evaluated. • All TAI scenarios showed better reproductive performance and weaning weights. • 3 TAI plus natural mating (24 days of interval) presented the best technical results. [ABSTRACT FROM AUTHOR]

• General decoupled method for reliability-based geotechnical design. • The method is robust to model complexity and parameter dimension. • Non-parametric and global estimation of failure probability functions. • Case study on the design optimization of deep excavation in Singapore marine clay. • Effects of statistical characteristics of soil properties on design is investigated. This paper presents a general decoupled method for reliability-based geotechnical design that takes into account the spatial variability of soil properties. In this method, reliability analyses that require a lot of computational resources are decoupled from the optimization procedure by approximating the failure probability function globally. Failure samples are iteratively generated over the entire design space so that their global distribution information can be extracted to construct the failure probability function. The method is computationally efficient, is flexible to implement, and is well suited for geotechnical problems that may involve sophisticated models. A design example of two-dimensional deep excavation against basal heave is discussed for Singapore marine clay where the density and normalized undrained shear strength of soil mass are modeled as random fields. Results demonstrate that the proposed method works well in practice and is advantageous over the coupled or locally decoupled reliability-based geotechnical design methods. [ABSTRACT FROM AUTHOR]

In the middle of the 20th century, increasing inbreeding rates were identified as a threat to livestock breeding. Consequences include reduced fertility, fitness and phenotypic expression of lethal alleles. An important step in mitigating this inbreeding was the introduction of optimum contribution selection (OCS). OCS facilitates the simultaneous management of genetic gain and inbreeding rates. However, using a standard OCS methodology for regional breeds with historical introgression for upgrading reasons could lead to reinforced selection on introgressed genetic material since those alleles improve the rate of genetic gain and reduce the average kinship in the population. Consequently, regional breeds may become genetically extinct if a standard OCS approach is used. Thus, the advanced OCS (aOCS) approach takes introgressed genetic material into account. The major goals of this study were to (i) gather key information on the feasibility of aOCS under practical conditions of the actual breeding scheme of Vorderwald cattle, (ii) identify superior strategies for implementing the actual scheme and (iii) examine whether historical breeding decisions to increase genetic gain by introgression from commercial breeds could have been avoided by using aOCS. Stochastic simulations were designed in this study to create populations from the historical gene pool by using aOCS. Simultaneously, all practical constraints of a breeding scheme were met. Thus, the simulated populations were comparable with real data. The annual genetic gain was higher in reality (1.56) than in the simulation scenarios (1.12–1.40). The introgressed genetic material increased to 61.3% in reality but was conserved at a final value of 15.3% (±0.78) across simulations. The classical rate of inbreeding and rate of native inbreeding were constrained to 0.092% on an annual basis. This value is equal to an effective population size of 100. The observed values for rates of inbreeding were 0.082–0.087% and 0.087–0.088% for classical and native kinship, respectively. The corresponding figures in reality were 0.067% and 0.184%, respectively. This study suggests that aOCS is feasible for Vorderwald cattle. Strategies for implementation are identified. Finally, we conclude that historical breeding decisions could have been avoided by using aOCS. The genetic gain would have been reduced by at least 12.2%, but the introgressed genetic material, genetic diversity and native genetic diversity would have been more desirable for a breed under conservation. [ABSTRACT FROM AUTHOR]

This paper aims to study the contracts of the Portuguese Public-Private Partnerships (PPP) of the motorway sector which represent an important part of the Annual Portuguese State Budget, as well as a crucial contribution to the Portuguese Public Debt. Once the formulae determining annual rents due to the private partners depend on stochastic variables, their simulation is binding. Otherwise, the Public Finances can suffer large losses. Our study is essential aiming to defend the Public interest and the political transparency as well as to present tools to prevent future excessive costs. In order to perform a simulation of the PP rents we started by describing the related contracts and analysing the mathematical formulae therein, following an insightful look and a descriptive analysis the historical data. Finally, a stochastic simulation study of the injury accident rates allowed us to calculate their rates as well as their renting implications. The simulated values are in accordance with our previous analysis, showing the existence of negative values for bonuses and penalties in the contracts. We discover the existence of an important trend: the bonuses increase and the penalties decrease when regarding symmetric variations in the fatal accident rates. This results in a strong and negative impact in the Portuguese Public Treasure. For the first time numerical flaws present in PPP contracts were systematically analysed by using a new statistical tool. This approach fills a gap when we are dealing with these issues allowing the partners to decide in a well informed manner. [ABSTRACT FROM AUTHOR]

• We use stochastic simulations to analyse public mass shootings. • Results provide the impact of human parameters on survival probability and shooter accuracy. • Implications of results are discussed and future research is proposed. A variety of individuals are likely to be involved in mass shooting attacks. However, the potential effect of people characteristics and response in such situations remains unclear. To address this issue, here we use a new stochastic model to identify patterns that maximize the survival probability and minimize the effectiveness of the shooter. As expected, while survival rates increase when people move fast (since they become a more difficult target to hit and their exposition time is reduced), chances of surviving decrease with non-escaping behaviours. We also found that densely occupied enclosures result in more casualties than sparsely occupied ones, however, casualties at high densities represent a smaller proportion of the individuals involved. Interestingly, even though the shooter effectiveness increases as the crowd becomes denser, so does too the survival probability overall. These findings challenge our current understanding of the impact of human parameters on mass shooting attacks. [ABSTRACT FROM AUTHOR]

• An improved version of Stochastic Subset Optimization (SSO), named iSSO, is presented. • Voronoi tessellation is implemented to partition the design space into non-overlapping subregions. • Double-sort algorithm is implemented to identify the optimal subregions. • For sampling in the design space, a weighted sampling method is proposed. • Several optimization problems are used to demonstrate effectiveness and efficiency. The Stochastic Subset Optimization (SSO) algorithm was proposed for optimal reliability problems that minimizes the probability of system failure over the admissible space for the design parameters. It is based on the simulation of samples of the design parameters from an auxiliary Probability Density Function (PDF) and exploiting the information contained in these samples to identify subregions for the optimal design parameters within the original design space. This paper presents an improved version of SSO, named iSSO to overcome the shortcomings in the SSO. In the improved version, the Voronoi tessellation is implemented to partition the design space into non-overlapping subregions using the pool of samples distributed according to the auxiliary PDF. A double-sort approach is then used to identify the subregions for the optimal design. The iSSO is presented as a generalized design optimization approach primarily tailored for the stochastic structural systems but also adaptable to deterministic systems. Several optimization problems are considered to illustrate the effectiveness and efficiency of the proposed iSSO. [ABSTRACT FROM AUTHOR]

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 prediction framework was based on the stochastic simulation method. • The value and its probability of agricultural water resources carrying capacity (AWRCC) was obtained. • The impact of indicators on AWRCC was explored. • The schemes to reach the target level of AWRCC were recommended. [ABSTRACT FROM AUTHOR]

The stochastic kinetics of biochemical reaction networks is described by a chemical master equation (CME) and the underlying laws of mass action. Assuming network-free simulations of the rule-based models of biochemical reaction networks (BRNs), this paper departs from the usual analysis of network dynamics as the time-dependent distributions of chemical species counts, and instead considers statistically evaluating the sequences of reaction events generated from the stochastic simulations. The reaction event-time series can be used for reaction clustering, identifying rare events, and recognizing the periods of increased or steady-state activity. However, the main aim of this paper is to device an effective method for identifying causally and anti-causally related sub-sequences of reaction events using their empirical probabilities. This allows discovering some of the causal dynamics of BRNs as well as uncovering their short-term deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related. Moreover, since the time-ordering of reaction events is locally irrelevant, the reaction sub-sequences can be transformed into the reaction sets or multi-sets. The distance metrics can be then used to define the equivalences among the reaction events. The proposed method for identifying the causally related reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The method was evaluated for five models of genetic networks in seven defined numerical experiments. The models were simulated in BioNetGen using the open-source network-free simulator NFsim. This simulator had to be modified first to allow recording the traces of reaction events, and it is available in the Github repository, ploskot/nfsim_1.20. The generated event time-series were analyzed with Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. This demonstrates the opportunities for substantially increasing the research productivity by creating automated data generation and processing pipelines. [Display omitted] • Biochemical reaction networks can be studied as traces of reaction events. • Causally related sub-sequences of reaction events can be identified. • Reaction sub-sequences can be represented as a query and the corresponding response patterns. • NFsim software was modified to allow recording the reaction traces. • The simulations and evaluations of biochemical reaction networks were nearly fully automated. [ABSTRACT FROM AUTHOR]

Among many decision problems, interval-valued multiplicative preference relation (IMPR) is widely utilized due to its ability to express uncertain information. A new approach to solve group decision making (GDM) with IMPRs is proposed in this paper, named stochastic group preference acceptability analysis with TODIM (SGPAA-TODIM) method, by combining TODIM method (an acronym in Portuguese of Interactive and multi-criteria Decision Making) with stochastic multi-criteria acceptability analysis (SMAA-2). It effectively circumvents information loss and considers the weight and risk preferences of experts. Firstly, the stochastic multiplicative preference relation is defined through stochastic simulation employing a certain density function, and its priority weight vector is determined using the logarithmic least squares method (LLSM). Then, the priority preference comprehensive matrix is proposed by extracting information of priority vectors. Moreover, the novel SGPAA-TODIM method is developed to analyze the stochastic parameter spaces, with the optimal rank determined based on the analysis of acceptability degrees associated with dominance rank. Finally, to demonstrate the validity and applicability of the proposed method, numerical examples are given. • The stochastic MPR is proposed through stochastic simulation. • The priority preference comprehensive matrix is defined for aggregating the priority vectors. • A novel method to solve group decision making problem. • Three examples are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Computing small failure probabilities is often of interest in the reliability analysis of engineering systems. However, this task can be computationally demanding since many evaluations of expensive high-fidelity models are often required. To address this, a multi-fidelity approach is proposed in this work within the setting of stratified sampling. The overall idea is to reduce the required number of high-fidelity model runs by integrating the information provided by different levels of model fidelity while maintaining accuracy in estimating the failure probabilities. More specifically, strata-wise multi-fidelity models are established based on Gaussian process models to efficiently predict the high-fidelity response and the system collapse from the low-fidelity response. Due to the reduced computational cost of the low-fidelity models, the multi-fidelity approach can achieve a significant speedup in estimating small failure probabilities associated with high-fidelity models. The effectiveness and efficiency of the proposed multi-fidelity stochastic simulation scheme are validated through an application to a two-story two-bay steel building under extreme winds. • A multi-fidelity scheme is presented for efficient estimation of small failure probabilities. • The scheme is based on Bayesian nonlinear regression with Gaussian process priors. • The scheme is integrated with stratified sampling for the estimation of small failure probabilities. • Gaussian process classification is used to estimate collapse from low-fidelity responses. • The scheme is applied to the failure probability estimation of a structure subject to extreme winds. [ABSTRACT FROM AUTHOR]

In the seismic reliability analysis of engineering structures, it is often necessary to adopt stochastic ground motions as inputs. However, the current methods used to generate stochastic samples, such as the spectral representation method, the spectral transfer method, the dynamic source method, the physical source method, and the comprehensive method, etc., are not very convincing since they do not contain the complete information of seismic occurrence and propagation. In this study, an improved finite-fault model for stochastic simulation of ground motions is proposed. Compared to existing stochastic finite-fault model, the improved model considers the propagation law of phase spectrum, local site effect, and the impact of wave velocity on the arrival time of seismic waves. Besides, identification and statistics of model parameters are carried out based on ground motion records. Reasonable samples of stochastic ground motions are then obtained. In addition, parameter sensitivity analysis is conducted to determine critical random variables, which reduces the complexity of stochastic simulation. Simulation results are compared with recorded accelerograms from the 1999 Chi-Chi earthquake. It is shown that the improved stochastic finite-fault model is reliable, and the acceleration response spectra of simulated samples are consistent with those of station records collected at various site classes and the design spectra. • An improved finite-fault model for stochastic simulation of ground motions is proposed. • Parameter sensitivity analysis is conducted to determine critical random variables of model. • Simulation results are compared with recorded accelerograms from the 1999 Chi-Chi earthquake. • Acceleration response spectra of simulated samples are consistent with those of station records and design spectra. [ABSTRACT FROM AUTHOR]

Characterization of complex reservoir structures by using limited observations is challenging in geosciences because it requires to reproduce geological realism. We propose a novel method to reconstruct complex structures by combining variational autoencoder and generative adversarial networks (VAE-GAN) with conditional image quilting algorithm. It improves the stability of traditional GAN-based simulation method without decreasing the quality of patterns. Firstly, we construct a VAE-GAN model to extract the high-dimensional features of facies patterns and to create abundant new patterns. The VAE-GAN-based learning method has a good ability in feature learning from training patches and accurately reproduce new facies patterns. Finally, these new patterns are spliced together to reconstruct a complex geological structure by employing the conditional image quilting algorithm, in which patterns are pasted to the simulated areas based on the calculated minimum cost path. During this process, conditioning is also considered. Since the pattern is generated by using the deep learning method rather than directly extracted from the training image, the diversity of realizations is enhanced without losing reproducibility. Based on synthetic training image, several examples are described and analyzed in detail, demonstrating the effectiveness and reliability of the present method. In addition, the new method is applied to a real training image. The complex heterogeneous structures are well reproduced by our method, indicating its practicability. • A facies conditional simulation method based on VAE-GAN and image quilting is proposed. • VAE-GAN can stably generate facies pattern with great quality. • The realizations honor to the conditioning well data. • The method learn the features and reproduce complex structures of geological bodies. [ABSTRACT FROM AUTHOR]

• The environmental risk of 2021 A Symphony collision incident is simulated. • A long-term deterministic simulation is performed to hindcast oil transport and fate. • The reliability of stochastic forecast is tested by comparing with the hindcast result. Coupled with a 3D hydrodynamic model, a well-established 3D oil spill model is used to simulate the transport and fate of oil spilled from the A Symphony oil tanker collision in the Yellow Sea on April 27, 2021. The model is first validated by airborne mini-SAR and shipborne X-band radar observation in a 30-day simulation. Subsequently, the model prediction capabilities are investigated over a longer period when hydrodynamic data may not be available. In these cases, hydrodynamic data in earlier years are used for stochastic simulation, and the reliability of a multi-year stochastic simulation is tested. To evaluate the impact of hydrodynamics and meteorological conditions, indexes including oil sweeping area for surface oil, as well as oil centroid trajectory and oil fate for both the surface and subsurface oil, are statistically analyzed for a period up to 90 days after the spill. Over time, the deviation in the stochastic simulation relative to the deterministic simulation increased in the modeled oil transport, indicating an error accumulation. However, in the modeled oil fate, the deviation showed an interesting variation of first increasing and then gradually decreasing. This phenomenon can be attributed to three contributing factors: (1) limitations in empirical equations used in the model; (2) limited environmental impact of the spill; (3) differential role of small-scale ocean processes in short- and long-term simulations. The uncertainty in stochastic members may serve as an indicator of forecast accuracy. [ABSTRACT FROM AUTHOR]

Recent years have seen a great deal of attention in energy conservation for production and manufacturing activities, particularly for energy-intensive industries. One of the useful strategies in reducing unnecessary energy consumption is to schedule these activities by considering both energy-driven and time-oriented criteria. This scheduling model can make an interaction between the energy consumption and the production cost to realize an efficient and sustainable production process. In this regard, the customers' expectation for due date is another important factor for decision-makers to control the delay in delivery. Making these decisions is extremely difficult due to uncertain circumstances to extract the accurate information of facilities and jobs in advance. Aforementioned issues in the context of urgent need for energy-conservation as well as the advent of globalized and multi-factory manufacture motivate our attempts to address a stochastic multi-objective distributed permutation flow shop scheduling problem by considering total tardiness constraint via minimizing the makespan and the total energy consumption. Due to the uncertainty of the proposed problem, a chance-constrain approach is used to describe decision-makers' awareness for the total tardiness, and accordingly, a chance-constrained programming model is utilized to formulate this problem. As a complicated optimization problem, a new multi-objective brain storm optimization algorithm incorporating stochastic simulation approach is specifically designed to better solve problem. A comparative study based on a set of benchmark test problems as well as two classical and popular algorithms is provided. The experimental results demonstrate that the proposed algorithm shows a very competitive performance in dealing with the investigated problem. [ABSTRACT FROM AUTHOR]

• Non-stationary non-Gaussian random field generator using only sparse data. • Bayesian compressive sampling reconstructs complete data and quantifies uncertainty. • KL expansion generates non-Gaussian and non-stationary random fields. • Real wind speed time series data during typhoon are used to demonstrate the proposed method. The first step to simulate random fields in practice is usually to obtain or estimate random field parameters, such as mean, standard deviation, correlation function, among others. However, it is difficult to estimate these parameters, particularly the correlation length and correlation functions, in the presence of sparse measurement data. In such cases, assumptions are often made to define the probabilistic distribution and correlation structure (e.g. Gaussian distribution and stationarity), and the sparse measurement data are only used to estimate the parameters tailored by these assumptions. However, uncertainty associated with the degree of imprecision in this estimation process is not taken into account in random field simulations. This paper aims to address the challenge of properly simulating non-stationary non-Gaussian random fields, when only sparse data are available. A novel method is proposed to simulate non-stationary and non-Gaussian random field samples directly from sparse measurement data, bypassing the difficulty in random field parameter estimation from sparse measurement data. It is based on Bayesian compressive sampling and Karhunen–Loève expansion. First, the formulation of the proposed generator is described. Then, it is illustrated through simulated examples, and tested with wind speed time series data. The results show that the proposed method is able to accurately depict the underlying spatial correlation from sparse measurement data for both non-Gaussian and non-stationary random fields. In addition, the proposed method is able to quantify the uncertainty related to random field parameter estimation from the sparse measurement data and propagate it to the generated random field. [ABSTRACT FROM AUTHOR]

Stochastic modeling of diffusion processes in various spatial domains and with boundary conditions is widely applicable especially in combination with non-linear kinetics of chemical reactions. However, the comparison with exact solution is possible in simple cases e.g. linear diffusion models. The novel testing method introduced in this article enables to study the behavior of linear stochastic diffusion models with possible low particle concentration in target sub-domains. The proposed method is demonstrated on various diffusion models with the known Green function. The novel method is recommended whenever the χ 2 goodness-of-fit test fails or the localization of critical domains is required. • General framework of rare event testing for stochastic simulation experiments. • Local testing of agreement between stochastic and deterministic models. • False Discovery Rate multiple testing instead of chi-squared goodness of fit test. • Application to diffusion process with pseudomonomolecular chemical reaction. • Detailed analysis and testing of particle–boundary interactions. [ABSTRACT FROM AUTHOR]

Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m -dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. [ABSTRACT FROM AUTHOR]

• Stochastic simulation demonstrated the benefits of importing sires for improvement of canine hip dysplasia (HD). • Importing sires with high genetic merits may improve genetic progress and genetic variability. • When countries used different scales to estimate HD breeding values, high genetic correlation was required. • Greater international consensus in HD screening protocols is recommended. Breeding against canine hip dysplasia (HD) may benefit from the importation of foreign sires. When foreign sires are evaluated on a different HD scale, this may diminish the efficacy. Using stochastic simulations, we evaluated genetic change and inbreeding levels for different scenarios of importing sires with high genetic merit for HD. Population size and genetic parameters (e.g. heritability, accuracy of selection, genetic correlation) were based on actual data for HD in Golden retrievers and Labrador retrievers in the UK and Sweden. For countries with different HD scales and an estimated breeding value (EBV) evaluation in place, the importation was useful if imported sires had EBV rankings in the top 50% and if genetic correlations between EBV systems were above 0.85. When importing sires with EBV rankings in the top 10%, moderate accuracies of EBVs (>0.40) and moderately strong genetic correlations (>0.70) were needed. Selection against HD without the importation of sires may increase inbreeding levels, while the importation of sires can decrease inbreeding levels. For national genetic evaluation and selection programmes, importing sires with high genetic merit can be an effective breeding strategy, but care is needed to estimate reliable EBVs. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Vacuum residue is represented as a continuum in molecular weight and aromaticity. • Molecular structures are formulated consistently with vacuum residue chemistry. • SARA fractions are distinguished using distinctive structural descriptors. • Island- and archipelago-type resins and asphaltenes are considered. • Visualization of detailed molecular distributions in bitumen vacuum residue is presented. Abstract Vacuum residue is the most diverse and structurally complex fraction of petroleum. In this work, we develop a representation of the molecular composition of this petroleum fraction. The underlying assumption is that vacuum residue is a continuum in molecular structure that can be described using probability distribution functions. The structural definition of SARA classes (saturates, aromatics, resins, and asphaltenes) is formulated in consistency with recent findings in petroleomics and the chemistry of thermal cracking or conversion. Island- and archipelago-type structures are considered in the representation of resins and asphaltenes. The systematic assembly of residue molecules is executed using stochastic algorithms. The model is implemented with good results in simulating a vacuum residue from oil sand bitumen. The simulation allows visualization of detailed molecular distributions in vacuum residue and its SARA fractions. The model also gives acceptable predictions for the properties of the maltene and asphaltene fractions of the residue sample. [ABSTRACT FROM AUTHOR]

Highlights • A general theory is given for the probability of resolving all peaks in chromatograms. • The theory allows for systematic variation of average peak spacing and peak width. • The theory is simplified by replacing a geometric mean by an arithmetic mean. • The theory agrees with published simulation results for isocratic chromatograms. • The theory is applied to three other systems with variable peak spacing and width. Abstract An equation is proposed for the probability that all mixture constituents are separated, when the density (i.e., average number of eluting constituents per time) and width of single-component peaks (SCPs) vary systematically. The probability Pr that m SCPs are separated is modeled as the product of the m – 1 probabilities that adjacent pairs of SCPs are separated. Pr is then expressed as the geometric mean of the probability product raised to the power of m – 1. This geometric mean is approximated by an arithmetic mean equaling the probability that adjacent SCPs are separated, as calculated from previously developed statistical overlap theory (SOT) for variable SCP density and width. The theory is tested using previously reported and current in-house simulations of isocratic chromatograms of SCPs with random differences in standard chemical potential. In such chromatograms, more SCPs elute at short times than long times, and their widths are less at short times than long times. The average difference between 179 previously reported and currently predicted values of 100 x Pr is about 0.6, when 100 x Pr > 5. The theory requires numerical computation of one integral but can be approximated by an analytic equation for SOT probabilities close to one. For SCPs having retention times exceeding twice the void time, this equation simplifies to a previous SOT expression, with the gradient peak capacity replaced by the isocratic peak capacity. The versatility of the Pr theory is tested using three other models of chromatograms, in which the density and width of SCPs vary. The Pr predictions agree with simulation for all three models. [ABSTRACT FROM AUTHOR]

Abstract Pitting corrosion can cause stress concentration and early onset of plasticity in the metallic structural components. This paper presents a comprehensive experimental and numerical study on the effect of pitting features such as pit shape and depth, pitting distribution and intensity on the structural performance of steel tubular members. The experimental investigations were carried out on tubular members with mechanically drilled pitting damage under axially loaded compression condition. The test results were applied to validate finite element (FE) models by comparing the load-shortening curves, ultimate loads and failure modes. The validated FE models were then used to simulate the random nature of pitting damage in terms of stochastic simulation. The results from the stochastic analyses show that random natures associated with pit shape, pit depth and pitting distribution can result in a great reduction and variation of ultimate strength, likely causing the transition of collapse mode. The experiments provide a modelling benchmark to validate FE models of the tubular structures with random pitting corrosion for the ultimate strength assessment. The developed FE models are shown to be capable of replicating the pitted tubular members. Highlights • An experimental programme of compressive loading tests on steel tubular members with pitting damage has been carried out. • Impacts of diverse pitting features to structural strength and deformation have been experimentally studied and compared. • Finite element models validated against experimental results have been developed to simulate random pitting damage. • Effect of random nature of pitting damage on variation of ultimate strength and structural failure mode has been studied. [ABSTRACT FROM AUTHOR]

Highlights • A highly permeable lens at the bottom of a drywell can infiltrate water at a much faster rate. • Cumulative infiltration volume increased with variance and lateral correlation length. • Cumulative infiltration volume decreased with an increasing vertical correlation length. • The average value of hydraulic conductivity played a primary role on drywell cumulative infiltration. • Constant head experiments provide higher accuracy in inversely optimized hydraulic parameters. Abstract Drywells are increasingly used to capture stormwater runoff for surface infiltration and aquifer recharge, but little research has examined the role of ubiquitous subsurface heterogeneity in hydraulic properties on drywell performance. Numerical experiments were therefore conducted using the HYDRUS (2D/3D) software to systematically study the influence of subsurface heterogeneity on drywell infiltration. Subsurface heterogeneity was described deterministically by defining soil layers or lenses, or by generating stochastic realizations of soil hydraulic properties with selected variance (σ) and horizontal (X) and vertical (Z) correlation lengths. The infiltration rate increased when a high permeability layer/lens was located at the bottom of the drywell, and had larger vertical and especially horizontal dimensions. Furthermore, the average cumulative infiltration (I) for 100 stochastic realizations of a given subsurface heterogeneity increased with σ and X , but decreased with Z. This indicates that the presence of many highly permeable, laterally extending lenses provides a larger surface area for enhanced infiltration than the presence of isolated, highly permeable lenses. The ability to inversely determine soil hydraulic properties from numerical drywell infiltration results was also investigated. The hydraulic properties and the lateral extension of a highly permeable lens could be accurately determined for certain idealized situations (e.g., simple layered profiles) using constant head tests. However, variability in soil hydraulic properties could not be accurately determined for systems that exhibited more realistic stochastic heterogeneity. In this case, the heterogeneous profile could be replaced with an equivalent homogeneous profile and values of an effective isotropic saturated conductivity (K s) and the shape parameter in the soil water retention function (α) could be inversely determined. The average value of K s for 100 stochastic realizations showed a similar dependency to I on σ , X , and Z. Whereas, the average value of α had large confidence interval for soil heterogeneity parameters and played a secondary role in drywell infiltration. This research provides valuable insight on the selection of site, design, installation, and long-term performance of a drywell. [ABSTRACT FROM AUTHOR]

Highlights • Stochastic ecological models require determining the number of Monte Carlo replicates. • Sample size calculations from experimental design can be adapted for simulation. • Estimation precision and detectable effect size must be specified. • A pilot study is required to estimate variability in model outputs. • Sample size increases with variability and precision, decreases with effect size. Abstract Ecologists often rely on computer models as virtual laboratories to evaluate alternative theories, make predictions, perform scenario analysis, and to aid in decision-making. The application of ecological models can have real-world consequences that drive ecological theory development and science-based decision and policy-making, so it is imperative that the conclusions drawn from ecological models have a strong, credible quantitative basis. In particular it is important to establish whether any predicted change in a model output has ecological and statistical significance. Ecological models may include stochastic components, using probability distributions to represent some modeled processes. An individual run of a stochastic ecological model is a random draw from an infinitely large population, requiring replicate simulations to estimate the distribution of model outcomes. An important consideration is the number of Monte Carlo replicates necessary to draw useful conclusions from the model analysis. A simple framework is presented that borrows from well-understood techniques for experimental design, including confidence interval estimation and sample size power analysis. The desired precision of interval estimates for model prediction, or the minimum desired detectable effect size between scenarios, is established by the researcher in the context of the model objectives and the ecological system. The number of replicates required to achieve that level of precision or detectable effect is computed given an estimate of the variability in the model outcomes of interest. If the number of replicates is computationally prohibitive, then the expected precision or detectable effect for that sample size should be reported. An example is given for a stochastic model of fire spread integrated with an eco-hydrological model. [ABSTRACT FROM AUTHOR]

Abstract Estimation of a reservoir's production potential depends on accurate physical and mathematical modeling of natural fracture networks. However, prediction of spatial locations and connectivity of these fracture networks is uncertain due to lack of sufficient data to model them. A stochastic simulation technique has been developed based on Multiple Point Statistics (MPS) using non-gridded training images and flexible spatial templates. The local angle of propagation of fractures are simulated based on the pattern of fractures in the vicinity of the simulation mode. The fracture is propagated in the simulated direction and a new set of simulation nodes are generated. The process is continued until the target fracture lengths are attained. A synthetic case was successfully used for demonstrating the working of the algorithm. [ABSTRACT FROM AUTHOR]

Abstract Power grids face a new and important challenge: the oncoming mass penetration of plug-in Electrical Vehicles (EVs). Nevertheless, the architectures of transmission and distribution grids are still focused on traditional design and operational rules. Consequently, it is necessary to predict the adequate solutions for the problems which are going to rise to the electrical and production grids as well as the effect on their commercial operation as a result of the gradual integration of EVs into the network. For example major congestion problems may appear in already heavily loaded grids as well as voltage profile problems mainly in radial networks, particularly if the peak load periods coincide with EV charging periods. In this paper the impacts of penetration of EVs on selected buses of a Distribution Network are assessed. The analysis takes into account driving profiles of EV owners, type of vehicle (battery capacity, energy consumption), travelling distance, road conditions etc, and approximates the hourly allocation of the energy requirements under various EV charging strategies. A steady state analysis toolbox is used to their impact to its operation. [ABSTRACT FROM AUTHOR]

Highlights • This work highlights BioSimulator.jl, an open-source master equation simulation engine implemented in the Julia language. • We highlight the comparative strengths of the exact and approximate stochastic simulation algorithms provided by our software. • Stochastic simulation finds applications across disciplines, including ecology, chemistry, and genetics. We illustrate BioSimulator.jl's workflow and utility with examples. • Our benchmarks favor a Julia implementation: BioSimulator.jl provides code readability and ease-of-use without sacrificing performance. Abstract Background and Objectives : Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl , for implementing the Gillespie algorithm, τ -leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. Methods : We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. Results : BioSimulator.jl 's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. Conclusion : The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification. [ABSTRACT FROM AUTHOR]

Abstract Epidemiological models are dominated by compartmental models, of which SIR formulations are the most commonly used. These formulations can be continuous or discrete (in either the state-variable values or time), deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SIR dynamical systems models, and we outline how they can be easily and rapidly constructed using Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using NMB network and mapping tools. [ABSTRACT FROM AUTHOR]

Multi-scale geochemical data play an important guiding role in mineral exploration and environmental assessment. However, due to the influence of certain factors such as terrain, sampling cost and time, and instrument detection limit, geochemical data obtained from a study area are often incomplete or sparse. In this study, a conditional generative adversarial network (GAN) model was developed as a stochastic simulation technique for spatial extrapolation of downscaled geochemical data by learning nonlinear relationships between two soil geochemical datasets with different sampling densities, one set called fine data and the other coarse data. Taking Qianjiang City, Hubei Province, China as an example, first the fine data and coarse data were divided separately into training area and verification area for simulation in the GAN model. Then, exploratory data analysis, error analysis and variogram analysis were used to evaluate the realizations. Finally, Ni anomalies related to soil heavy metal pollution were extracted by combined concentration–area (C–A) fractal model and uncertainty analysis, in the verification area. Our studies indicate that the proposed conditional GAN model is efficient in extrapolating downscaled geochemical data, since both the realizations and fine data maintain similar spatial autocorrelation and statistical distribution. • A conditional GAN model was proposed for spatial extrapolation of downscaled geochemical data. • The conditional GAN model can effectively learn the nonlinear relationship between the coarse data and the fine data. • The current research provides a new paradigm for solving the problem of missing high-resolution data. [ABSTRACT FROM AUTHOR]

In coastal cities such as Hong Kong, rapid reclamation using prefabricated vertical drains (PVDs) is preferred as it can accelerate land supply to meet the urgent demand for houses. A robust PVD design relies on the correct identification of permeable soil layers and accurate delineation of their stratigraphic connectivity with surrounding drainage boundaries. The current engineering practice often ignores the physical locations of minor drainage boundaries (e.g., sand lenses) in the subsurface stratigraphy and might lead to a false interpretation of potential drainage and consolidation mechanisms. In this study, a data-driven analysis framework that takes stratigraphic uncertainty into consideration is proposed to investigate the spatiotemporal consolidation of PVD-improved ground using sparse site investigation data often encountered in engineering practice. The method adaptively develops multiple geological cross-sections from limited measurements and prior knowledge that is reflected by a single training image. The resulting multiple geological realizations serve as the input for PVD analysis. The proposed data-driven framework allows for a probabilistic evaluation of soil spatiotemporal behavior in terms of the degree of consolidation and future ground settlement. More importantly, the proposed method accurately predicts the spatial distribution of stratigraphic boundaries with quantified uncertainty, which significantly influences the consolidation mechanism. [ABSTRACT FROM AUTHOR]

Highlights • Simulation is a powerful tool to evaluate the effects of compensatory growth. • Compensatory growth can be used as a strategy to reduce feeding costs. • Compensatory growth has low cost. However, it may present high financial risk. • Feed restriction >90 days may offset the cost savings of compensatory growth. Abstract The manipulation of growth can be used as a strategy to reduce feeding costs. The objective of this study was to analyze, by deterministic and stochastic simulation, compensatory growth effects on the bioeconomic results of growing-finishing beef cattle systems. Four beef cattle systems were simulated with different feed restriction periods (no restriction, CONT; 90 days of restriction, COMP90; 120 days of restriction, COMP120; and 150 days of restriction, COMP150). Risk analysis was performed using the Monte Carlo method. Animals in feed restriction systems demonstrated partial compensation and required longer periods to achieve slaughter weight than CONT steers (16, 23 and 27 more days for COMP90, COMP120 and COMP150, respectively). The CONT system was the most productive (466 kg/ha/year), followed by COMP90 (443 kg/ha/year), COMP120 (432 kg/ha/year), and COMP150 (383 kg/ha/year). The most profitable system was the COMP90 system (US$ 161/ha) followed by the CONT (US$ 144/ha). The lower net margin was observed in the COMP120 (US$ 107/ha) and COMP150 (US$ 90/ha) systems. The risk analysis showed that COMP90 presented the highest financial stability. For all simulated systems, prices of finished steers, feeder calves, and corn were the variables with the greatest effects on net margin. The use of compensatory growth can be a tool to reduce feeding costs in beef cattle systems; however, the feed restriction must not be longer than three months. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) penetration growth without energy storage system (ESS) is restricted due to its poor dispatchability. Compressed air energy storage system (CAES) is one of the promising ESS technologies. But, low energy density and high mechanical inertia confined its application as a bulk ESS. In this paper, a novel hybrid thermal-compressed supercritical carbon dioxide energy storage (T-CCES) system is proposed to address CAES shortcomings. In this system, a stratified water thermal energy storage (TES) is employed to store both released heat of compression process and high-frequency reluctant power of a wind farm (which cannot be followed by CCES) using electrical heaters. A dynamic simulation is performed to evaluate system characteristics in steady wind generation condition and in a real wind generation condition. Stochastic simulation of the proposed system integrated with a real wind farm is also fulfilled to assess its characteristics and performance by considering uncertainties using sparse polynomial chaos expansion. Additionally, global sensitivity analysis via Sobol’ indices is conducted to identify the most important parameters of the system. Results show that the system can store wind power during off-peak hours and smoothly releases it into the grid with 57.55% round-trip efficiency (RTE) and 84.1 kWh/m 3 energy density. Uncertainty quantification results indicate that the system can properly work under uncertainties with standard deviation of 55.72–58.16% RTE and 83.7–86.5 kWh/m 3 energy density. Moreover, results show that the random production of wind turbine has a negligible effect on performance characteristics of the T-CCES which makes it a promising solution for integration with wind farms. [ABSTRACT FROM AUTHOR]

Pitting corrosion poses a threat to plated steel structures serving in aggressive corrosion environments. This paper involves numerical studies on the structural behaviour and ultimate strength reduction of plated steel structures due to random pitting damage. Stochastic simulations were used to model the random nature of the pitting corrosion varying pitting shape, depth and distribution. A series of nonlinear analyses were performed on unstiffened plates and stiffened panels to understand the mechanisms of structural collapse due to random pitting damage. Empirical formulae were derived respectively for the prediction of ultimate strength reductions of unstiffened plates and stiffened panels in terms of regression analysis from the numerical results. Random pitting corrosion induces a variation and reduction in ultimate strength, and can lead to a transition in failure mode. The collapse of pitted structures under uniaxial compression has a feature that the onset of plasticity initiates in the areas close to the unloaded edge of the structure, and propagates into a continuous plasticity region linking the pits with highly concentrated stress. The pitted area with intensively stress-concentrated pits undergoes a locally amplified deformation that determines the failure mode, leading to structural failure. [ABSTRACT FROM AUTHOR]

This work addresses the challenge of efficiently determining the representative volume size in computational homogenization by exploiting the requirement that mechanical response quantities are statistically homogeneous and ergodic random fields. The proposed computational homogenization approach focuses on empirically determining the autocorrelation functions of output response quantities through post processing finite element analyses. Once the autocorrelation functions are known, only trivial computations are needed to determine the variance of apparent properties as a function of domain size without any additional finite element computation by utilizing a simple formula relating the autocorrelation function to the variance of spatially averaged quantities. This approach improves upon the current established approach by circumventing the need to analyze numerous successively larger domains in order to determine convergence of apparent properties, which can be computationally prohibitive. Furthermore, similar previous analytical expressions for the variance of apparent properties are asymptotic with respect to domain size while the expression in the proposed approach is exact. After presenting the formulation, the method is first demonstrated for a one-dimensional bar model where analytical expressions can be derived. Then the approach is demonstrated on two numerical examples: (1) a plane strain, linear elastic domain with a lognormal random field describing its compliance, and (2) a stochastic polycrystalline microstructure of a Nickel super alloy having a crystal plasticity model for its constitutive law. [ABSTRACT FROM AUTHOR]

Spontaneous Ca 2+ oscillation is a special kind of astrocyte signal that is crucial in several brain functions. However, the underlying mechanism of its initiation is still unclear. Experimental investigations reveal quite different results of the contribution of Ca 2+ influxes from the extracellular space. We developed a new biophysical model, which includes two kinds of extracellular Ca 2+ influx: through voltage gated Ca 2+ channels and through capacitative Ca 2+ entry. Besides, two stochastic models are constructed to discuss the influence of channel noise in voltage gated influx and stochastic IP 3 receptor dynamics on the contribution of Ca 2+ fluxes. Simulation results reveal that the contribution of a given flux can be considerably strengthened or weakened by special cellular states, such as the change of membrane potential, inter-flux feedback; the special cellular condition (e.g. endoplasmic reticulum depletion, other flux inhibition). In stochastic environment, the contribution of fluxes may be strongly altered and the respond of different fluxes varies qualitatively. Our results may help to understand the different results obtained in experiments. [ABSTRACT FROM AUTHOR]