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The drive in computational methods and more intuitive software has seen a rise in the number of publications in this area in recent years. Computational simulations can be found in many areas of science from computational biology and chemistry to fundamental physics. These may help synthetic chemists in their drug discovery endeavors and cosmologists predicting astronomical events. This paper is designed to equip chemists with a basic understanding of how loops and conditional statements can be used within both the MATLAB syntax and Excel spreadsheets to simulate dynamic processes. Many commercial software packages require little to no programming and often do not explicitly display the underlying calculations. While these programs are often very efficient for solving a specific problem, they are somewhat limited. Principally, this practical uses a stochastic simulation algorithm to generate the kinetic data of a reversible association reaction. The kinetic data is analyzed using the standard rate laws to highlight to students the effectiveness of this method. Ultimately, the skills developed in this practical will help students in future computational projects, where bespoke coding is necessary.

Abstract This study proposes an innovative stochastic simulation‐based optimization (SSO) model for arterial traffic signal coordination based on the ring‐and‐barrier diagram. The SSO model takes the network traffic equity and the arterial traffic efficiency as the two objectives, which are evaluated by the relative Gini index and the relative vehicular throughout, respectively. Then a surrogate‐based promising area search (SPAS) algorithm is developed to solve the SSO model. The advantage of the SPAS algorithm is that the built‐in shrinking ball method and the promising area search technique help reduce the computational efforts while appropriately capturing the stochasticity. Then some numerical experiments are conducted based on a real‐world urban arterial road with four signalized T‐intersections and field data in Nanchang, China. The numerical results show that the SSO model for arterial traffic signal coordination can be well solved by the proposed SPAS algorithm and that the relationship between the arterial traffic efficiency and the network traffic equity is not completely competitive. Specifically, increasing the weight of arterial traffic efficiency from 0.5 can slightly improve the network traffic equity while significantly improving the arterial traffic efficiency, providing significant guidance for real‐world arterial signal coordination.

Relatively large deformations of the steel arch support in underground coal mines in the Republic of Serbia present one of the main problems for achieving the planned production of coal. Monitoring of the critical sections of the steel arch support in the underground roadways is necessary to gather quality data for the development of a forecasting model. With a new generation of 3D laser scanners that can be used in potentially explosive environments (ATEX), deformation monitoring is facilitated, while the process of collecting precise data is much shorter. In this paper, we used a combination of grey and stochastic system theory combined with an autoregressive process for processing collected data and the development of a forecasting model of the deformations of the steel arch support. Forecasted data accuracy based on the positions of the markers placed along the internal rim of support construction shows high accuracy with MAPE of 0.2143%. The proposed model can successfully be used by mining engineers in underground coal mines for steel arch support deformations prediction, consequentially optimizing the maintenance plan of the underground roadways and achieving planned production.

Disease spreading simulations are traditionally performed using coupled differential equations. However, in the setting of metapopulations, most of the solutions provided by this method do not account for the dynamic topography of subpopulations. Conversely, the alternative approach of individual-based modeling (IBM) may add computational cost and complexity. Hybrid models allow for the study of disease spreading because they combine both aforementioned approaches by separating them across different scales: a local scale that addresses subpopulation dynamics using coupled differential equations and a global scale that addresses the contact between these subpopulations using IBM. We present a simple way of simulating the spread of disease in dynamic networks using the high-level statistical computational language R and the hybridModels package. We built four examples using disease spread models at the local scale in several different networks: an animal movement network; a three-node network, whose model solution using a stochastic simulation algorithm is compared with the ordinary differential equations approach; the commuting of individuals between patches, which we compare with the permanent migration of individuals; and the commuting of individuals within the metropolitan area of São Paulo.

Hydrological applications such as storm-water management usually deal with region-specific reference rainfall regulations based on intensity–duration–frequency (IDF) curves. Such curves are usually obtained via frequency analysis of rainfall and exceedance probability estimation of rain intensity for different durations. It is also common for reference rainfall to be expressed in terms of precipitation P, accumulated in a duration D, with a return period T. Meteorological modules of hydro-meteorological models used for the aforementioned applications therefore need to be capable of simulating such reference rainfall scenarios. This paper aims to address three research gaps: (i) the discrepancy between standard methods for defining reference precipitation and the strong multi-scale intermittency of precipitation, (ii) a lack of procedures to adapt multi-fractal precipitation modelling to specified partial statistical references, and (iii) scarcity of proper multi-scale tools to quantitatively estimate the effectiveness of such simulation procedures. Therefore, it proposes (i) a procedure based on extreme non-Gaussian statistics in two scaling regimes due to earth's finite size to tackle multi-scale intermittency head on, (ii) a renormalization technique to make simulations comply with the aforementioned partial statistical references, and (iii) multi-scale metrics to compare simulated rainfall time series with those observed. While the first two proposals are utilized to simulate reference rainfall scenarios for three regions (Paris, Nantes, and Aix-en-Provence) in France that are characterized by different climates, the last one is used to validate them. The scope of this paper is that the baseline precipitation scenarios simulated here can be used as realistic inputs into hydrological models for applications such as the optimal design of storm-water management infrastructure, especially green roofs. Although only purely temporal simulations are considered, this approach could possibly be generalized to space–time as well.

Owing to the intrinsic heterogeneity and anisotropy of karst systems, traditional hydrological exploration methods face significant challenges when investigating karst conduit networks. This study employs pyKasso for the stochastic simulation of karst conduit networks in Panzhou City, focusing on uncertainty analysis through local sensitivity analysis and Monte Carlo methods. The simulation process incorporates geological, topographic, and fracture data to create a realistic representation of the karst network. We found that the spatial configuration and characteristics of the karst network are significantly influenced by various input parameters such as fracture parameters, inlets, outlets, and cost ratios. We highlight the minimal influence of fracture densities and the substantial impact of the count of inlets and outlets on crucial network metrics. The results demonstrate the critical role of parameter sensitivity and variability in modeling the intricate karst systems, providing valuable insights for hydrogeological studies and the management of karst water.

The cassava (Manihot esculenta Crantz) breeding program at the International Institute of Tropical Agriculture (IITA) has adopted genomic selection to accelerate genetic gain. The program continues to develop varieties broadly adapted across Nigeria's diverse agroclimatic zones. However, for this purpose, genotype‐by‐environment interaction (GEI) presents a challenge. To decide whether broad adaptation breeding is a good strategy, we evaluated broad versus narrow adaptation strategies using stochastic simulation, assessing genetic gain, genetic variance, heritability, and selection accuracy at 0 versus realistic levels of GEI variance. To parameterize the models, we analyzed historical data from four phenotypic evaluation stages of the IITA breeding program to estimate genetic and error variances, and genetic correlations across environments. Based on these observed parameters, the genomic‐enabled breeding programs exhibited higher genetic gain than the conventional program for both GEI variances. At realistic GEI variance, the narrow adaptation program showed higher genetic gain than the broad adaptation program. Across all programs, the genetic variance declined over time, though the genomic‐enabled programs showed higher initial variance due to the selection of parents at earlier stages. At realistic GEI variance, an increase in genetic variance was observed in the narrow adaptation program due to its conversion of GEI between mega‐environments into main genetic variance within mega‐environments. This higher genetic variance led to higher heritabilities and selection accuracies. This study highlights the potential of genomic selection in accelerating genetic gain and suggests that dividing the IITA cassava breeding program to target more than one mega‐environment should be considered. Core Ideas: Simulation is a cost‐effective approach to assess different breeding strategies toward making informed decisions.Genomic selection outperformed a conventional breeding program in terms of rates of genetic gain, irrespective of genotype‐by‐environment interaction variance.Breeding for narrow adaptation by splitting Nigerian locations into two mega‐environments outperformed breeding for broad adaptation.Not all biological complexities of genetic effects and real‐world breeding programs can be captured in simulation. [ABSTRACT FROM AUTHOR]

We present the validation of the hybrid sSSA-SDPD method for advection-diffusion-reaction problems coupled to discrete biochemical systems, as presented in the publication "A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for advection-diffusion-reaction problems" (Drawert et al., 2019). We validate 1D diffusion, and 2D diffusion cases against their analytical solutions. We present graphs and tables of data showing the error in the simulation method.

ABSTRACT: Butana is one of the local dairy cattle breeds of Sudan commonly kept by smallholder producers. This breed has been strongly promoted to advance the dairy production sector in the country. The main problem, however, is the lack of a systematic breeding program that involves smallholder producers. The aim of the current study was to identify the most promising design for a breeding program to improve the milk yield performance of Butana cattle under smallholder production conditions. In total, 3 breeding scenarios, including (1) the use of farm bulls, (2) the use of village bulls, and (3) the rotational use of village bulls within village groups, were simulated using a stochastic simulation approach. For each breeding scenario, 3 selection methods for bulls were considered, namely random mating, phenotypic selection, and selection based on estimated breeding value (EBV). The results showed that no genetic gain was realized with random mating in all breeding scenarios. In the farm bull breeding scenario, annual genetic gain (standard deviation units) ranged from 0.01 to 0.19 (phenotypic selection) and from 0.01 to 0.39 (selection based on EBV). In the village bull breeding scenarios, the annual genetic gain ranged from 0.01 to 0.21 (phenotypic selection) and 0.01 to 0.45 (selection based on EBV). The lowest genetic gain was realized for the rotational use of village bulls among villages within groups. Through the rotational use of village bulls, however, a higher genetic variance was maintained than in the farm and village bull breeding scenarios. We concluded that a village bull breeding program with selection based on EBV of young bulls was the most promising breeding design for achieving the breeding goal. Further studies are needed to assess the organizational feasibility of such a breeding program to ensure the participation of smallholder producers and its sustainability.

The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy.

The traditional method of installing underground utilities (e.g., water, sewer, gas pipes, electrical cables) by burying them under roads has been used for decades. However, the repeated excavations related to this method cause problems, such as traffic congestion and business disruption, which can significantly increase financial and social costs. Multi-purpose Utility Tunnels (MUTs) are a good alternative for buried utilities. Although the initial cost of MUTs is higher than that of the traditional method, social cost savings make them more feasible, especially in dense urban areas. Different factors, such as the specifications of utilities, the location of the MUTs, and the construction method, should be investigated to determine if MUTs can be an economical and practical alternative. The construction method is one of the most important factors to assess to have a successful MUT project and reduce its impact on the surrounding area. Simulation can be used to investigate the different construction methods of MUTs. In this paper, two Stochastic Discrete Event Simulation models depicting two MUT construction methods (i.e., microtunneling and cut-and-cover) are developed to analyze the duration and cost of the MUT projects. Also, 4D simulation models of these methods are developed for constructability assessment of these projects.