Search

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

The proposed thesis complements generalized risk assessment frameworks for structural engineering tackling stochastic dynamic systems, by addressing multiple uncertainties, non-linearities, and full-time-dependent analyses. Two major complementary work packages have been pursued. Firstly, a novel point selection procedure for the Probability Density Evolution Method (PDEM) has been developed to enhance efficiency while retaining the full dynamic response of non-linear dynamic systems. This method aims to estimate time-dependent reliability and failure probabilities in dynamic systems under first-passage failure conditions. Leveraging features of the Subset simulation procedure, the Subset supported Point Selection (S-PS) method adaptively generates dependent sample sets, enhancing accuracy by incorporating performance function assessments as weighting factors. The proposed approach effectively identifies samples in the failure region, particularly benefiting dynamic systems under stochastic excitation. It offers a computationally efficient structural reliability estimation procedure by analyzing full-time-history responses, providing deeper insights into rare failure events and mechanisms through visualization of intermediate results. Secondly, to address the challenge of identifying patterns and underlying characteristics in natural or engineering time-varying phenomena, a data-driven stochastic representation of Evolutionary Power Spectral Density (EPSD) functions is introduced. The Relaxed Evolutionary Power Spectral Density (REPSD) function is derived from multiple similar data, accounting for uncertainties and providing a realistic representation of time data such as seismic ground motions or wind speed records in the time-frequency domain. Truncated normal distributions and kernel density estimates are used to determine a probability density function for each time-frequency component. The REPSD function enables the sampling of individual EPSD functions, facili, Die vorliegende Arbeit ergänzt Risikobewertungen für das Bauingenieurwesen, die sich mit stochastischen dynamischen Systemen befassen, indem sie sich mit mehreren Unsicherheiten, Nichtlinearitäten und vollzeitabhängigen Analysen befasst. Es wurden zwei große komplementäre Arbeitspakete verfolgt. Erstens wurde ein neuartiges Punktauswahlverfahren für die „Probability Density Evolution Method (PDEM)“ entwickelt, um die Effizienz zu steigern und gleichzeitig die volle dynamische Antwort nichtlinearer dynamischer Systeme beizubehalten. Diese Methode zielt darauf ab, zeitabhängige Zuverlässigkeit und Ausfallwahrscheinlichkeiten in dynamischen Systemen unter Bedingungen der ersten Durchgangszeit für kritische Systemantworten abzuschätzen. Unter Nutzung des Subset-Simulationsverfahrens generiert die vorgeschlagene Subset-unterstützte Punktauswahlmethode (S-PS) adaptiv abhängige Stichprobensätze und erhöht die Genauigkeit durch die Einbeziehung von Gewichtungsfaktoren. Der vorgeschlagene Ansatz identifiziert effektiv Proben im Fehlerbereich, was insbesondere dynamischen Systemen unter stochastischer Anregung zugute kommt. Es bietet ein rechnerisch effizientes Verfahren zur Schätzung der strukturellen Zuverlässigkeit durch die Analyse von Antworten über die Zeit und bietet durch die Visualisierung von Zwischenergebnissen tiefere Einblicke in seltene Fehlerereignisse und -mechanismen. Zweitens wird eine datengesteuerte stochastische Darstellung von EPSD-Funktionen (Evolutionary Power Spectral Density) eingeführt, um Muster und zugrunde liegende Merkmale in natürlichen oder technischen zeitlich variierenden Phänomenen zu identifizieren. Die „Relaxed Evolutionary Power Spectral Density“ (REPSD) Funktion wird aus mehreren ähnlichen Daten abgeleitet, berücksichtigt Unsicherheiten und bietet eine realistische Darstellung von Zeitdaten. Diese Daten können sich aus seismischen Bodenbewegungen oder Windgeschwindigkeitsaufzeichnungen im Zeit-Frequenz-Bereich zusammensetzen. Gestutzte

Mining is a high-risk industry, so efficiency and safety are key priorities. As mines continue to go deeper and exploit low-grade deposits, bulk mining methods, such as sublevel caving (SLC), have become increasingly important. SLC is suitable for massive steeply dipping ore bodies and is known for its high degree of mechanisation, productivity, and low operational cost. Moreover, technological developments and mechanisation have allowed these methods to be applied at greater depths. In modern mechanised mines Load haul dump (LHD) machines are central to achieving the desired productivity. Therefore, automation of LHDs and their increasing use in mines make it crucial to understand the performance of these machines in actual mining environments. The aim of this research was to understand the differences in the productivity of semiautonomous and manual LHDs and identify how external factors impact the performance of these machines in SLC operations. The research also investigated how LHD operator training could improve the loading efficiency. Performance data for semi-autonomous and manual LHDs were collected from LKAB’s Kiirunavaara mine’s central database, GIRON. These data were used to compare cycle times and payloads of semi-autonomous and manual LHDs. The data were filtered and sorted so that only data where both machine types were operating in the same area (crosscut, ring, and ore pass) were used. To understand the impact of external factors, data on the occurrence of boulders were collected from LKAB’s Malmberget mine by recording videos of LHD buckets, while the data on operator training were obtained by performing baseline mapping and conducting a questionnaire study with the LHD operators at LKAB’s Kiirunavaara mine. The results of the comparative analysis of manual and semi-autonomous LHDs showed the mean payload was 0.34 tonnes higher for manual LHD machines. However, the differences were not consistent across different areas of the mine. Similarly, when

Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel implementations, inadequate. This paper introduces a new generation of the STochastic Engine for Pathway Simulation (STEPS) project (http://steps.sourceforge.net/), denominated STEPS 4.0, and its core components which have been designed for improved scalability, performance, and memory efficiency. STEPS 4.0 aims to enable novel scientific studies of macroscopic systems such as whole cells while capturing their nanoscale details. This class of models is out of reach for serial solvers due to the vast quantity of computation in such detailed models, and also out of reach for naive parallel solvers due to the large memory footprint. Based on a distributed mesh solution, we introduce a new parallel stochastic reaction-diffusion solver and a deterministic membrane potential solver in STEPS 4.0. The distributed mesh, together with improved data layout and algorithm designs, significantly reduces the memory footprint of parallel simulations in STEPS 4.0. This enables massively parallel simulations on modern HPC clusters and overcomes the limitations of the previous parallel STEPS implementation. Current and future improvements to the solver are not sustainable without following proper software engineering principles. For this reason, we also give an overview of how the STEPS codebase and the development environment have been updated to follow modern software development practices. We benchmark performance improvement and memory footprint on three published models with different complexities, from a simple spatial stochastic reaction-diffusion model, to a more complex one that is coupled to a deterministic membrane potential solver to simulate the calcium burst activity of a Purkinje neuron. Simulation results of these models, source:https://www.frontiersin.org/articles/10.3389/fninf.2022.883742/full

Human development situated at the foot of mountains faces sediment-laden flood hazards characterized by high-velocity, erosive flows carrying mud and debris. In the western United States, sediment-laden flood hazards are increasing due to more frequent and severe wildfires, more intense precipitation, and the expansion of development towards mountain wildlands. Considerable work has focused on developing models of post-fire peak flows and sediment yields at the outlet of mountain canyons, but the risk to communities downstream of protective flood infrastructure common throughout the southwestern U.S. is largely unknown. We present an original modeling framework that captures the interactions between wildfires, storms, and flood infrastructure to estimate sediment-laden flood hazards. Stochastic modeling with a continuous simulation approach is used to quantify uncertainty and explicitly consider antecedent conditions. This work shows that compound post-fire flood hazards may be up to 6 times larger than suggested by the marginal hazard posed by extreme precipitation, and that future increases in wildfire severity and intensity could increase flood hazards by up to a factor of 11. Furthermore, modeling of compound post-fire flood hazards across the Santa Ana Mountains in Riverside County, California reveals hot spots of risk below catchments that last burned over 45 years ago. As urban expansion into mountainous regions continues to increase around the world, an improved understanding of the hazards facing communities “protected” by infrastructure is important to better characterize the spatial distribution of risks across populations, increase risk awareness, and inform sustainable adaptation and resilient land development practices.

© 2011 American Institute of Physics. J.V.S. and J.O.Z. acknowledge support from The Research Council of Norway, under Grant No. 167336/V30 "Transport on a nano-scale; at surfaces and contact lines." J.O.Z. acknowledges financial support from the Spanish Ministry of Science and Innovation (MICINN) through Grant No. FIS2008-03801. I. P. acknowledges financial support from MICINN through Grant No. FIS2008-04386, and from DURSI under Project No. SGR2009-634. I. P. and J.O.Z. further acknowledge joint support from MICINN under Grant No. FIS2008-04403-E., In this paper, we consider a simple reaction-diffusion system, namely, a binary fluid mixture with an association-dissociation reaction between two species. We study fluctuations at hydrodynamic spatiotemporal scales when this mixture is driven out of equilibrium by the presence of a temperature gradient, while still being far away from any chemical instability. This study extends the analysis in our first paper on the subject [J. M. Ortiz de Zarate, J. V. Sengers, D. Bedeaux, and S. Kjelstrup, J. Chem. Phys. 127, 034501 (2007)], where we considered fluctuations in a non-isothermal reaction-diffusion system but still close to equilibrium. The present extension is based on mesoscopic non-equilibrium thermodynamics that we recently developed [D. Bedeaux, I. Pagonabarraga, J. M. Ortiz de Zarate, J. V. Sengers, and S. Kjelstrup, Phys. Chem. Chem. Phys. 12, 12780 (2010)] to derive the law of mass action and fluctuation-dissipation theorems for the random contributions to the dissipative fluxes in the nonlinear macroscopic description. Just as for non-equilibrium fluctuations close to equilibrium, we again find an enhancement of the intensity of the concentration fluctuations in the presence of a temperature gradient. The non-equilibrium concentration fluctuations are in both cases spatially long ranged, with an intensity depending on the wave number q. The intensity exhibits a crossover from a proportional to q(-4) to a proportional to q(-2) behavior depending on whether the corresponding wavelength is smaller or larger than the penetration depth of the reacting mixture. This opens a possibility to distinguish between diffusion-or activation-controlled regimes of the reaction experimentally. The important conclusion overall is that non-equilibrium fluctuations in non-isothermal reaction-diffusion systems are always long ranged., Research Council of Norway, Spanish Ministry of Science and Innovation (MICINN), DURSI, Depto. de Estructura de la Materia, Física Térmica y Electrónica, Fac. de Ciencias Físicas, TRUE, pub

Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel implementations, inadequate. This paper introduces a new generation of the STochastic Engine for Pathway Simulation (STEPS) project (http://steps.sourceforge.net/), denominated STEPS 4.0, and its core components which have been designed for improved scalability, performance, and memory efficiency. STEPS 4.0 aims to enable novel scientific studies of macroscopic systems such as whole cells while capturing their nanoscale details. This class of models is out of reach for serial solvers due to the vast quantity of computation in such detailed models, and also out of reach for naive parallel solvers due to the large memory footprint. Based on a distributed mesh solution, we introduce a new parallel stochastic reaction-diffusion solver and a deterministic membrane potential solver in STEPS 4.0. The distributed mesh, together with improved data layout and algorithm designs, significantly reduces the memory footprint of parallel simulations in STEPS 4.0. This enables massively parallel simulations on modern HPC clusters and overcomes the limitations of the previous parallel STEPS implementation. Current and future improvements to the solver are not sustainable without following proper software engineering principles. For this reason, we also give an overview of how the STEPS codebase and the development environment have been updated to follow modern software development practices. We benchmark performance improvement and memory footprint on three published models with different complexities, from a simple spatial stochastic reaction-diffusion model, to a more complex one that is coupled to a deterministic membrane potential solver to simulate the calcium burst activity of a Purkinje neuron. Simulation results of these models, source:https://www.frontiersin.org/articles/10.3389/fninf.2022.883742/full

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

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

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

This article presents the effect of Industry 4.0 (I4.0) combined with asset management (AM) in improving the life cycle of complex systems in electrical energy distribution (EED). The boom in smart networks leaves companies in this sector no choice but to adhere to I4.0. The contribution of I4.0 to the progress of AM in maintenance in EED will therefore be demonstrated by a case study using simulation. The case study will concern the benefits of using advanced metering infrastructure (AMI), the heart of smart grids, at Hydro-Québec Distribution (HQD), the primary supply authority in Quebec. The HQD network includes 4.3 million clients, on a territory of approximately 250,000 km2 and 680,000 overhead transformers. The results are conclusive: the number of outages will drop by 7% annually and maintenance costs will fall by at least 5% per year.

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

Plant cell walls are versatile materials that can adopt a wide range of mechanical properties through controlled deposition of cellulose fibrils. Wall integrity requires a sufficiently homogeneous fibril distribution to cope effectively with wall stresses. Additionally, specific conditions, such as the negative pressure in water transporting xylem vessels, may require more complex wall patterns, e.g., bands in protoxylem. The orientation and patterning of cellulose fibrils are guided by dynamic cortical microtubules. New microtubules are predominantly nucleated from parent microtubules causing positive feedback on local microtubule density with the potential to yield highly inhomogeneous patterns. Inhomogeneity indeed appears in all current cortical array simulations that include microtubule-based nucleation, suggesting that plant cells must possess an as-yet unknown balancing mechanism to prevent it. Here, in a combined simulation and experimental approach, we show that a limited local recruitment of nucleation complexes to microtubules can counter the positive feedback, whereas local tubulin depletion cannot. We observe that nucleation complexes preferentially appear at the plasma membrane near microtubules. By incorporating our experimental findings in stochastic simulations, we find that the spatial behavior of nucleation complexes delicately balances the positive feedback, such that differences in local microtubule dynamics—as in developing protoxylem—can quickly turn a homogeneous array into a banded one. Our results provide insight into how the plant cytoskeleton has evolved to meet diverse mechanical requirements and greatly increase the predictive power of computational cell biology studies.

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

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

The breadth of theoretical results on efficient Markov Chain Monte Carlo (MCMC) sampling schemes on discrete spaces is slim when compared to the available theory for MCMC sampling schemes on continuous spaces. Nonetheless, in [Zan17] a simple framework to design Metropolis-Hastings (MH) proposal kernels that incorporate local information about the target is presented. The class of functions for which the resulting MH kernels are Peskun optimal in high-dimensional regimes is characterized. We will refer to these functions as \textit{balancing functions} and to the class of resulting MH proposal kernels as \textit{pointwise informed proposals}. In [PG19], the class of balancing functions is used to construct Markov Jump Processes (MJP) on discrete state spaces. As a result, the Zanella process is constructed. In the absence of a theoretical result on the optimal balancing function to choose from the class of balancing functions, a heuristic approach is proposed using the Zanella process. To further encourage the mixing behaviour of the simulated chain, the algebraic structure of the state space is exploited to achieve non-reversible Markov chains on short to medium timescales. Simulations are performed for all the considered MCMC sampling schemes by studying the Bayesian record linkage problem., Applied Mathematics

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

Human mobility contributes to the fast spatio-temporal propagation of infectious diseases. During an outbreak, monitoring the infection situation on either side of an international border is very crucial as there is always a higher risk of disease importation associated with cross-border migration. Mechanistic models are effective tools to investigate the consequences of cross-border mobility on disease dynamics and help in designing effective control strategies. However, in practice, due to the unavailability of cross-border mobility data, it becomes difficult to propose reliable, model-based strategies. In this study, we propose a method for estimating cross-border mobility flux between any pair of regions that share an international border from the observed difference in the timing of the infection peak in each region. Assuming the underlying disease dynamics is governed by a Susceptible-Infected-Recovered (SIR) model, we employ stochastic simulations to obtain the maximum likelihood cross-border mobility estimate for any pair of regions where the difference in peak time can be measured. We then investigate how the estimate of cross-border mobility flux varies depending on the disease transmission rate, which is a key epidemiological parameter. We further show that the uncertainty in mobility flux estimates decreases for higher disease transmission rates and larger observed differences in peak timing. Finally, as a case study, we apply the method to some selected regions along the Poland-Germany border which are directly connected through multiple modes of transportation and quantify the cross-border fluxes from the COVID-19 cases data during the period $20^{\rm th}$ February $2021$ to $20^{\rm th}$ June $2021$.

Résumé Le drainage minier acide (DMA), suivant l’oxydation des sulfures déclenchée par l’oxygène atmosphérique et l’eau libre, occasionne une détérioration de la qualité de l’eau en terme d’acidité et de concentration des métaux et oxyanions. Le DMA constitue un danger environnemental d’ampleur mondiale vu son effet néfaste sur les systèmes aquatiques et les formes de vies fauniques et floristiques. Les contributions scientifiques menées par le gouvernement, l’industrie minière, les universités et les établissements de recherche se concentrent sur l’évaluation, la prévention et le traitement du DMA pour préserver les écosystèmes avoisinant les installations minières. La plupart des contributions scientifiques abordent les aspects avals de la gestion du DMA impliquant les étapes opérationnelles et post-fermeture du cycle minier. Peu de solutions pratiques ont été suggérées pour la phase de développement en raison du manque de rejets solides in-situ et les données expérimentales nécessaires pour envisager lesdites contributions scientifiques. Par conséquent, le concept de gestion en amont a reçu très peu d'attention. De même, les approches de modélisation utilisées pour prévoir le DMA sont largement étudiées. Bien qu'elles offrent de nombreux avantages, la majorité de ces études de modélisation sont réalisées pendant les phases d'exploitation et de fermeture du cycle minier, car elles abordent les stratégies de conception et la performance des scénarios de restauration. En outre, la recherche scientifique basée sur des approches interdisciplinaires pour atténuer le risque environnemental du DMA devrait être davantage mise en évidence et développée. La revue de la littérature souligne trois concepts principaux ; la géométallurgie, le principe de conception pour la fermeture et la réflexion en amont. La géométallurgie a été principalement développée pour dissoudre les limites interdisciplinaires entre le géologue, le métallurgiste et l'ingénieur minier afin d'optimiser

Codes for reproducing the results in the research article "Estimating cross-border mobility from the difference in peak-timing: A case study in Poland-Germany border regions"

Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.

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

Background: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. Result: We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. Conclusion: J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinfo

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

The breadth of theoretical results on efficient Markov Chain Monte Carlo (MCMC) sampling schemes on discrete spaces is slim when compared to the available theory for MCMC sampling schemes on continuous spaces. Nonetheless, in [Zan17] a simple framework to design Metropolis-Hastings (MH) proposal kernels that incorporate local information about the target is presented. The class of functions for which the resulting MH kernels are Peskun optimal in high-dimensional regimes is characterized. We will refer to these functions as \textit{balancing functions} and to the class of resulting MH proposal kernels as \textit{pointwise informed proposals}. In [PG19], the class of balancing functions is used to construct Markov Jump Processes (MJP) on discrete state spaces. As a result, the Zanella process is constructed. In the absence of a theoretical result on the optimal balancing function to choose from the class of balancing functions, a heuristic approach is proposed using the Zanella process. To further encourage the mixing behaviour of the simulated chain, the algebraic structure of the state space is exploited to achieve non-reversible Markov chains on short to medium timescales. Simulations are performed for all the considered MCMC sampling schemes by studying the Bayesian record linkage problem., Applied Mathematics

Plant cell walls are versatile materials that can adopt a wide range of mechanical properties through controlled deposition of cellulose fibrils. Wall integrity requires a sufficiently homogeneous fibril distribution to cope effectively with wall stresses. Additionally, specific conditions, such as the negative pressure in water transporting xylem vessels, may require more complex wall patterns, e.g., bands in protoxylem. The orientation and patterning of cellulose fibrils are guided by dynamic cortical microtubules. New microtubules are predominantly nucleated from parent microtubules causing positive feedback on local microtubule density with the potential to yield highly inhomogeneous patterns. Inhomogeneity indeed appears in all current cortical array simulations that include microtubule-based nucleation, suggesting that plant cells must possess an as-yet unknown balancing mechanism to prevent it. Here, in a combined simulation and experimental approach, we show that a limited local recruitment of nucleation complexes to microtubules can counter the positive feedback, whereas local tubulin depletion cannot. We observe that nucleation complexes preferentially appear at the plasma membrane near microtubules. By incorporating our experimental findings in stochastic simulations, we find that the spatial behavior of nucleation complexes delicately balances the positive feedback, such that differences in local microtubule dynamics-as in developing protoxylem-can quickly turn a homogeneous array into a banded one. Our results provide insight into how the plant cytoskeleton has evolved to meet diverse mechanical requirements and greatly increase the predictive power of computational cell biology studies.

Hoof disorders and sub-optimal mobility (SOM) are economically important health issues in dairy farming. Although the dynamics of hoof disorders have an important effect on cow mobility, they have not been considered in previous simulation models that estimate the economic loss of SOM. Furthermore, these models do not consider the varying severities of SOM. The objective of this study was to develop a novel bio-economic simulation model to simulate the dynamics of 8 hoof disorders: digital dermatitis (DD), interdigital hyperplasia (HYP), interdigital dermatitis/heel-horn erosion (IDHE), interdigital phlegmon (IP), overgrown hoof (OH), sole haemorrhage (SH), sole ulcer (SU) and white-line disease (WLD), their role in SOM, and estimate the economic loss of SOM in a herd of 125 dairy cows. A Reed-Frost model was used for DD and a Greenwood model for the other 7 hoof disorders. Economic analysis was conducted per mobility score according to a 5-point mobility scoring method (1 = perfect mobility; 5 = severely impaired mobility) by comparing a scenario with SOM and one without SOM. Parameters used in the model were based on literature and expert opinion and deemed credible during model validation rounds. Results showed that the mean cumulative incidence for maximum mobility scores 2–5 SOM episodes were respectively 34, 16, 7 and <1 episodes per 100 cows per pasture period and 39, 19, 8, <1 episodes per 100 cows per housing period. The mean total annual economic loss due to SOM resulting from the hoof disorders under study was €15,342: €122 per cow per year. The economic analysis uncovered direct economic losses that could be directly linked to SOM episodes and indirect economic losses that could not be directly linked to SOM episodes but arose due to the presence of SOM. The mean total annual direct economic loss for maximum mobility score 2–5 SOM episodes was €1129, €3098, €4354 and €480, respectively. The mean total annual indirect economic loss varied considerably bet

Hoof disorders and sub-optimal mobility (SOM) are economically important health issues in dairy farming. Although the dynamics of hoof disorders have an important effect on cow mobility, they have not been considered in previous simulation models that estimate the economic loss of SOM. Furthermore, these models do not consider the varying severities of SOM. The objective of this study was to develop a novel bio-economic simulation model to simulate the dynamics of 8 hoof disorders: digital dermatitis (DD), interdigital hyperplasia (HYP), interdigital dermatitis/heel-horn erosion (IDHE), interdigital phlegmon (IP), overgrown hoof (OH), sole haemorrhage (SH), sole ulcer (SU) and white-line disease (WLD), their role in SOM, and estimate the economic loss of SOM in a herd of 125 dairy cows. A Reed-Frost model was used for DD and a Greenwood model for the other 7 hoof disorders. Economic analysis was conducted per mobility score according to a 5-point mobility scoring method (1 = perfect mobility; 5 = severely impaired mobility) by comparing a scenario with SOM and one without SOM. Parameters used in the model were based on literature and expert opinion and deemed credible during model validation rounds. Results showed that the mean cumulative incidence for maximum mobility scores 2–5 SOM episodes were respectively 34, 16, 7 and <1 episodes per 100 cows per pasture period and 39, 19, 8, <1 episodes per 100 cows per housing period. The mean total annual economic loss due to SOM resulting from the hoof disorders under study was €15,342: €122 per cow per year. The economic analysis uncovered direct economic losses that could be directly linked to SOM episodes and indirect economic losses that could not be directly linked to SOM episodes but arose due to the presence of SOM. The mean total annual direct economic loss for maximum mobility score 2–5 SOM episodes was €1129, €3098, €4354 and €480, respectively. The mean total annual indirect economic loss varied conside

Hoof disorders and sub-optimal mobility (SOM) are economically important health issues in dairy farming. Although the dynamics of hoof disorders have an important effect on cow mobility, they have not been considered in previous simulation models that estimate the economic loss of SOM. Furthermore, these models do not consider the varying severities of SOM. The objective of this study was to develop a novel bio-economic simulation model to simulate the dynamics of 8 hoof disorders: digital dermatitis (DD), interdigital hyperplasia (HYP), interdigital dermatitis/heel-horn erosion (IDHE), interdigital phlegmon (IP), overgrown hoof (OH), sole haemorrhage (SH), sole ulcer (SU) and white-line disease (WLD), their role in SOM, and estimate the economic loss of SOM in a herd of 125 dairy cows. A Reed-Frost model was used for DD and a Greenwood model for the other 7 hoof disorders. Economic analysis was conducted per mobility score according to a 5-point mobility scoring method (1 = perfect mobility; 5 = severely impaired mobility) by comparing a scenario with SOM and one without SOM. Parameters used in the model were based on literature and expert opinion and deemed credible during model validation rounds. Results showed that the mean cumulative incidence for maximum mobility scores 2–5 SOM episodes were respectively 34, 16, 7 and <1 episodes per 100 cows per pasture period and 39, 19, 8, <1 episodes per 100 cows per housing period. The mean total annual economic loss due to SOM resulting from the hoof disorders under study was €15,342: €122 per cow per year. The economic analysis uncovered direct economic losses that could be directly linked to SOM episodes and indirect economic losses that could not be directly linked to SOM episodes but arose due to the presence of SOM. The mean total annual direct economic loss for maximum mobility score 2–5 SOM episodes was €1129, €3098, €4354 and €480, respectively. The mean total annual indirect economic loss varied conside

Human mobility contributes to the fast spatio-temporal propagation of infectious diseases. During an outbreak, monitoring the infection situation on either side of an international border is very crucial as there is always a higher risk of disease importation associated with cross-border migration. Mechanistic models are effective tools to investigate the consequences of cross-border mobility on disease dynamics and help in designing effective control strategies. However, in practice, due to the unavailability of cross-border mobility data, it becomes difficult to propose reliable, model-based strategies. In this study, we propose a method for estimating cross-border mobility flux between any pair of regions that share an international border from the observed difference in the timing of the infection peak in each region. Assuming the underlying disease dynamics is governed by a Susceptible-Infected-Recovered (SIR) model, we employ stochastic simulations to obtain the maximum likelihood cross-border mobility estimate for any pair of regions where the difference in peak time can be measured. We then investigate how the estimate of cross-border mobility flux varies depending on the disease transmission rate, which is a key epidemiological parameter. We further show that the uncertainty in mobility flux estimates decreases for higher disease transmission rates and larger observed differences in peak timing. Finally, as a case study, we apply the method to some selected regions along the Poland-Germany border which are directly connected through multiple modes of transportation and quantify the cross-border fluxes from the COVID-19 cases data during the period $20^{\rm th}$ February $2021$ to $20^{\rm th}$ June $2021$.

[EN] The management of water resources has been of great importance for several years, among which, special attention is being paid to groundwater, due to the amount of available resource and its sensitivity to natural and/or anthropogenic processes. A practice that has become common among professionals during the evaluation of hydrogeological resources is the assumption of single deterministic models, for the characterization of the heterogeneity of the parameters that directly and indirectly influence the modeling of hydrogeological formations, leaving in theoretical applications the use of stochastic models. The objective of the present work is to demonstrate the difference between applying deterministic and stochastic approaches when modeling flow and/or transport. For this purpose, a two-dimensional section in the xz plane of a synthetic aquifer with an extension of 1000 meters in length and 30 meters in height, as well as 14 hard data of hydraulic conductivity, are used. In order to model the heterogeneity of the hydraulic conductivity, geostatistical techniques such as estimation (ordinary kriging) and simulation (sequential gaussian simulation) are applied, in the second case 100 equiprobable simulations are generated conditioned to the provided data. The construction of the numerical flow model is performed with the MODFLOW code, with a discretization of 30 layers, 1 row and 100 columns, with cells of size (¿x = 10, ¿y = 1, ¿z = 10), simulating 3 periods whose durations correspond to 1 second, 10 days, and 80 years. For the transport solution, the MT3D code is used, considering the advective movement as predominant, the first type boundary condition or Dirichlet (on the left edge) stipulated for the flow solution is taken as the available source of pollutant, given that pollutant is not being injected directly into the system, the mass present in the aquifer is conditioned to the dragging capacity of the flow velocity. Due to the large number of realization, [ES] La gestión de los recursos hídricos ha tomado gran importancia desde hace varios años, de entre ellos se está poniendo especial atención al agua subterránea, debido a la cantidad de recurso disponible y a la sensibilidad de esta a procesos naturales y/o antropogénicos. Una práctica que se ha vuelto común entre los profesionales durante la evaluación de recursos hidrogeológicos es la asunción de modelos determinísticos únicos, para la caracterización de la heterogeneidad de los parámetros que influyen directa e indirectamente en la modelación de formaciones hidrogeológicas, dejando en aplicaciones teóricas el uso de modelos estocásticos. El objetivo del presente trabajo es demostrar la diferencia al aplicar enfoques determinísticos y estocásticos al modelar el flujo y/o transporte. Para tal cometido se utiliza una sección bidimensional en el plano xz, de un acuífero sintético con una extensión de 1000 metros de longitud y 30 metros de altura, así como 14 datos duros de conductividad hidráulica. Para modelar la heterogeneidad de la conductividad hidráulica se aplican técnicas geoestadísticas como estimación (krigeado ordinario) y simulación (simulación gaussiana secuencial), en el segundo caso se generan 100 simulaciones equiprobables condicionadas a los datos proporcionados. La construcción del modelo numérico de flujo se realiza con el código MODFLOW, con una discretización de 30 capas, 1 fila y 100 columnas con celdas de tamaño (¿x = 10, ¿y = 1, ¿z = 10), simulándose 3 periodos cuyas duraciones corresponden a 1 segundo, 10 días y 80 años. Para la solución del transporte se emplea el código MT3D, considerándose predominante el movimiento advectivo, se toma la condición de frontera de primer tipo o Dirichlet (en el borde izquierdo) estipulada para la solución del flujo como fuente disponible de contaminante, dado que no se está inyectando contaminante directamente al sistema la masa presente en el acuífero está condicionada a la capacidad de arrastre de la veloc

[EN] The management of water resources has been of great importance for several years, among which, special attention is being paid to groundwater, due to the amount of available resource and its sensitivity to natural and/or anthropogenic processes. A practice that has become common among professionals during the evaluation of hydrogeological resources is the assumption of single deterministic models, for the characterization of the heterogeneity of the parameters that directly and indirectly influence the modeling of hydrogeological formations, leaving in theoretical applications the use of stochastic models. The objective of the present work is to demonstrate the difference between applying deterministic and stochastic approaches when modeling flow and/or transport. For this purpose, a two-dimensional section in the xz plane of a synthetic aquifer with an extension of 1000 meters in length and 30 meters in height, as well as 14 hard data of hydraulic conductivity, are used. In order to model the heterogeneity of the hydraulic conductivity, geostatistical techniques such as estimation (ordinary kriging) and simulation (sequential gaussian simulation) are applied, in the second case 100 equiprobable simulations are generated conditioned to the provided data. The construction of the numerical flow model is performed with the MODFLOW code, with a discretization of 30 layers, 1 row and 100 columns, with cells of size (¿x = 10, ¿y = 1, ¿z = 10), simulating 3 periods whose durations correspond to 1 second, 10 days, and 80 years. For the transport solution, the MT3D code is used, considering the advective movement as predominant, the first type boundary condition or Dirichlet (on the left edge) stipulated for the flow solution is taken as the available source of pollutant, given that pollutant is not being injected directly into the system, the mass present in the aquifer is conditioned to the dragging capacity of the flow velocity. Due to the large number of realization, [ES] La gestión de los recursos hídricos ha tomado gran importancia desde hace varios años, de entre ellos se está poniendo especial atención al agua subterránea, debido a la cantidad de recurso disponible y a la sensibilidad de esta a procesos naturales y/o antropogénicos. Una práctica que se ha vuelto común entre los profesionales durante la evaluación de recursos hidrogeológicos es la asunción de modelos determinísticos únicos, para la caracterización de la heterogeneidad de los parámetros que influyen directa e indirectamente en la modelación de formaciones hidrogeológicas, dejando en aplicaciones teóricas el uso de modelos estocásticos. El objetivo del presente trabajo es demostrar la diferencia al aplicar enfoques determinísticos y estocásticos al modelar el flujo y/o transporte. Para tal cometido se utiliza una sección bidimensional en el plano xz, de un acuífero sintético con una extensión de 1000 metros de longitud y 30 metros de altura, así como 14 datos duros de conductividad hidráulica. Para modelar la heterogeneidad de la conductividad hidráulica se aplican técnicas geoestadísticas como estimación (krigeado ordinario) y simulación (simulación gaussiana secuencial), en el segundo caso se generan 100 simulaciones equiprobables condicionadas a los datos proporcionados. La construcción del modelo numérico de flujo se realiza con el código MODFLOW, con una discretización de 30 capas, 1 fila y 100 columnas con celdas de tamaño (¿x = 10, ¿y = 1, ¿z = 10), simulándose 3 periodos cuyas duraciones corresponden a 1 segundo, 10 días y 80 años. Para la solución del transporte se emplea el código MT3D, considerándose predominante el movimiento advectivo, se toma la condición de frontera de primer tipo o Dirichlet (en el borde izquierdo) estipulada para la solución del flujo como fuente disponible de contaminante, dado que no se está inyectando contaminante directamente al sistema la masa presente en el acuífero está condicionada a la capacidad de arrastre de la veloc

[EN] The management of water resources has been of great importance for several years, among which, special attention is being paid to groundwater, due to the amount of available resource and its sensitivity to natural and/or anthropogenic processes. A practice that has become common among professionals during the evaluation of hydrogeological resources is the assumption of single deterministic models, for the characterization of the heterogeneity of the parameters that directly and indirectly influence the modeling of hydrogeological formations, leaving in theoretical applications the use of stochastic models. The objective of the present work is to demonstrate the difference between applying deterministic and stochastic approaches when modeling flow and/or transport. For this purpose, a two-dimensional section in the xz plane of a synthetic aquifer with an extension of 1000 meters in length and 30 meters in height, as well as 14 hard data of hydraulic conductivity, are used. In order to model the heterogeneity of the hydraulic conductivity, geostatistical techniques such as estimation (ordinary kriging) and simulation (sequential gaussian simulation) are applied, in the second case 100 equiprobable simulations are generated conditioned to the provided data. The construction of the numerical flow model is performed with the MODFLOW code, with a discretization of 30 layers, 1 row and 100 columns, with cells of size (¿x = 10, ¿y = 1, ¿z = 10), simulating 3 periods whose durations correspond to 1 second, 10 days, and 80 years. For the transport solution, the MT3D code is used, considering the advective movement as predominant, the first type boundary condition or Dirichlet (on the left edge) stipulated for the flow solution is taken as the available source of pollutant, given that pollutant is not being injected directly into the system, the mass present in the aquifer is conditioned to the dragging capacity of the flow velocity. Due to the large number of realization, [ES] La gestión de los recursos hídricos ha tomado gran importancia desde hace varios años, de entre ellos se está poniendo especial atención al agua subterránea, debido a la cantidad de recurso disponible y a la sensibilidad de esta a procesos naturales y/o antropogénicos. Una práctica que se ha vuelto común entre los profesionales durante la evaluación de recursos hidrogeológicos es la asunción de modelos determinísticos únicos, para la caracterización de la heterogeneidad de los parámetros que influyen directa e indirectamente en la modelación de formaciones hidrogeológicas, dejando en aplicaciones teóricas el uso de modelos estocásticos. El objetivo del presente trabajo es demostrar la diferencia al aplicar enfoques determinísticos y estocásticos al modelar el flujo y/o transporte. Para tal cometido se utiliza una sección bidimensional en el plano xz, de un acuífero sintético con una extensión de 1000 metros de longitud y 30 metros de altura, así como 14 datos duros de conductividad hidráulica. Para modelar la heterogeneidad de la conductividad hidráulica se aplican técnicas geoestadísticas como estimación (krigeado ordinario) y simulación (simulación gaussiana secuencial), en el segundo caso se generan 100 simulaciones equiprobables condicionadas a los datos proporcionados. La construcción del modelo numérico de flujo se realiza con el código MODFLOW, con una discretización de 30 capas, 1 fila y 100 columnas con celdas de tamaño (¿x = 10, ¿y = 1, ¿z = 10), simulándose 3 periodos cuyas duraciones corresponden a 1 segundo, 10 días y 80 años. Para la solución del transporte se emplea el código MT3D, considerándose predominante el movimiento advectivo, se toma la condición de frontera de primer tipo o Dirichlet (en el borde izquierdo) estipulada para la solución del flujo como fuente disponible de contaminante, dado que no se está inyectando contaminante directamente al sistema la masa presente en el acuífero está condicionada a la capacidad de arrastre de la veloc

[EN] The management of water resources has been of great importance for several years, among which, special attention is being paid to groundwater, due to the amount of available resource and its sensitivity to natural and/or anthropogenic processes. A practice that has become common among professionals during the evaluation of hydrogeological resources is the assumption of single deterministic models, for the characterization of the heterogeneity of the parameters that directly and indirectly influence the modeling of hydrogeological formations, leaving in theoretical applications the use of stochastic models. The objective of the present work is to demonstrate the difference between applying deterministic and stochastic approaches when modeling flow and/or transport. For this purpose, a two-dimensional section in the xz plane of a synthetic aquifer with an extension of 1000 meters in length and 30 meters in height, as well as 14 hard data of hydraulic conductivity, are used. In order to model the heterogeneity of the hydraulic conductivity, geostatistical techniques such as estimation (ordinary kriging) and simulation (sequential gaussian simulation) are applied, in the second case 100 equiprobable simulations are generated conditioned to the provided data. The construction of the numerical flow model is performed with the MODFLOW code, with a discretization of 30 layers, 1 row and 100 columns, with cells of size (¿x = 10, ¿y = 1, ¿z = 10), simulating 3 periods whose durations correspond to 1 second, 10 days, and 80 years. For the transport solution, the MT3D code is used, considering the advective movement as predominant, the first type boundary condition or Dirichlet (on the left edge) stipulated for the flow solution is taken as the available source of pollutant, given that pollutant is not being injected directly into the system, the mass present in the aquifer is conditioned to the dragging capacity of the flow velocity. Due to the large number of realization, [ES] La gestión de los recursos hídricos ha tomado gran importancia desde hace varios años, de entre ellos se está poniendo especial atención al agua subterránea, debido a la cantidad de recurso disponible y a la sensibilidad de esta a procesos naturales y/o antropogénicos. Una práctica que se ha vuelto común entre los profesionales durante la evaluación de recursos hidrogeológicos es la asunción de modelos determinísticos únicos, para la caracterización de la heterogeneidad de los parámetros que influyen directa e indirectamente en la modelación de formaciones hidrogeológicas, dejando en aplicaciones teóricas el uso de modelos estocásticos. El objetivo del presente trabajo es demostrar la diferencia al aplicar enfoques determinísticos y estocásticos al modelar el flujo y/o transporte. Para tal cometido se utiliza una sección bidimensional en el plano xz, de un acuífero sintético con una extensión de 1000 metros de longitud y 30 metros de altura, así como 14 datos duros de conductividad hidráulica. Para modelar la heterogeneidad de la conductividad hidráulica se aplican técnicas geoestadísticas como estimación (krigeado ordinario) y simulación (simulación gaussiana secuencial), en el segundo caso se generan 100 simulaciones equiprobables condicionadas a los datos proporcionados. La construcción del modelo numérico de flujo se realiza con el código MODFLOW, con una discretización de 30 capas, 1 fila y 100 columnas con celdas de tamaño (¿x = 10, ¿y = 1, ¿z = 10), simulándose 3 periodos cuyas duraciones corresponden a 1 segundo, 10 días y 80 años. Para la solución del transporte se emplea el código MT3D, considerándose predominante el movimiento advectivo, se toma la condición de frontera de primer tipo o Dirichlet (en el borde izquierdo) estipulada para la solución del flujo como fuente disponible de contaminante, dado que no se está inyectando contaminante directamente al sistema la masa presente en el acuífero está condicionada a la capacidad de arrastre de la veloc

This thesis is concerned with two topics rooted in the analysis of time-series. In the first, we improve the estimation of rare-event probabilities by stochastic simulation. The proposed method, quasi-monotone splitting uses generalized splitting to estimate integrals with respect to intractable target distributions by instead estimating them with respect to the terminal state of a certain Markov chain, allowing us to use time series methods to study them. We employ two innovations to this end: Problem constraints are exploited to derive a simple, efficient estimation strategy automatically for a tractable problem class, and The performance of the estimator is improved through the use of survival analysis and extreme value theory, in which near-optimal parameters can be derived with minimal intervention. We demonstrate applications of this algorithm to a variety of wireless reliability problems. The performance of the resulting algorithms are competitive with specialized Monte Carlo estimators for specific problems, and provide novel estimators for problems previously lacking known, efficient estimators. Some of the methods in this section were developed for a paper with several co-authors which has now been published. The second topic is audio signal analysis. An important task here is style transfer, which attempts to synthesize a new signal from two others, a source and a target. The new synthetic signal should possess the microscopic “stylistic” statistics of the source, and the macroscopic “semantic” statistics of the target. We solve this problem using mosaicing style transfer, which decomposes the source signal into microscopic sub-samples, superimposing them to produce the new synthetic signal whose macroscopic statistics approximate the target. In such models, one chooses parameters by minimising some loss function which ideally approximates acoustic similarity as perceived by a human listener. We leverage the insight that human pitch perception is related

[EN] In this paper, we present LOIMOS, which is an epidemiological scenario simulator developed in the context of the fight against the pandemic caused by coronavirus SARS-CoV-2 on a global scale. LOIMOS has been fully developed under the paradigm of membrane computing using transition P systems with communication rules, active membranes and a stochastic simulator engine. In this paper we detail the main components of the system and we report some examples of epidemiological scenarios evaluated with LOIMOS.

[EN] In this paper, we present LOIMOS, which is an epidemiological scenario simulator developed in the context of the fight against the pandemic caused by coronavirus SARS-CoV-2 on a global scale. LOIMOS has been fully developed under the paradigm of membrane computing using transition P systems with communication rules, active membranes and a stochastic simulator engine. In this paper we detail the main components of the system and we report some examples of epidemiological scenarios evaluated with LOIMOS.

[EN] In this paper, we present LOIMOS, which is an epidemiological scenario simulator developed in the context of the fight against the pandemic caused by coronavirus SARS-CoV-2 on a global scale. LOIMOS has been fully developed under the paradigm of membrane computing using transition P systems with communication rules, active membranes and a stochastic simulator engine. In this paper we detail the main components of the system and we report some examples of epidemiological scenarios evaluated with LOIMOS.

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto-and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated., Hydraulic Structures and Flood Risk

Since the outbreak of the novel coronavirus (COVID-19) at December 31ths of 2019 in China it quickly spread to more than 200 countries around the word. Government on affected countries have taken actions such as social distancing, reduction of social, commercial and entertainment events, among others, in order to decrease the COVID-19 spreading rate. As a way to evaluate how effective are such actions, we design an agent-based microsimulation model that allows for representing the COVID-19 spreading in Medellín, Colombia by representing individually the social interactions. To set those interactions, we cluster population (home, school, working, and shopping) and use a transport activity to move among clusters generating social interactions. Accordingly, we reproduce the number of cases and deaths caused by the COVID-19 according to Medellín-real data by using the proposed individual social interaction model. Also, we test our model with two scenarios: first one with real government actions and second one without any government actions in Medellín-Colombia. Our model results show that earlypublic-health policies allows for flatting the curve of the COVID-19 spreading in contrast to the scenario without restrictions. As future work, we will include more clusters—e.g.; leisure clusters, transport clusters—and the dynamic of the foreign COVID-19 cases ., Desde el brote del nuevo coronavirus (COVID-19) el 31 de diciembre de 2019 en China se extendió rápidamente a más de 200 países de todo el mundo. Los gobiernos de los países afectados han tomado medidas como el distanciamiento social para disminuir la tasa de propagación de COVID-19. Como una forma de evaluar la efectividad de tales acciones, diseñamos un modelo de microsimulación basado en agentes que permite representar la propagación de COVID-19 en Medellín, Colombia. En consecuencia, reproducimos el número de casos y muertes causadas por COVID-19 de acuerdo con los datos reales de Medellín utilizando el modelo propuesto. Además, probamos nuestro modelo con dos escenarios: primero con acciones gubernamentales reales y segundo sin acciones gubernamentales en Medellín-Colombia. Los resultados de nuestro modelo muestran que las políticas de salud pública tempranas permiten aplanar la curva de la propagación de COVID-19 en contraste con el escenario sin restricciones. Como trabajo futuro, incluiremos más clústeres, por ejemplo, clústeres de ocio, clústeres de transporte, y la dinámica de los casos extranjeros de COVID-19

This paper aims to identify the most relevant variables that explain the economic supply of potato “Solanum tuberosum”, in its two varieties: modern and native, in growers in the Huasahuasi district, in the Junín department of Peru. The methodology used was the Stochastic Monte Carlo Simulation to evaluate the profitability and intrinsic risk of agricultural production in both types of supply. The results show that there is no significant difference in the profitability and risk of these two varieties: The expected value of the profitability, as well as the minimum and maximum value of this variable, in the case of modern potatoes, are: S /. 9639, S /. -6290; and S /. 28476. In the case of native potatoes, these values correspond to S /. 7240, S /. -3754; and S /. 24476. Also, it was found that these growers offer a greater quantity of modern potato compared to native potato, since productivity per unit of land; and the cultivated area, average per grower, amounts to 27 t.ha-1 and 16 t.ha-1; and 2,46 ha and 1,26 ha, for modern and native potatoes, respectively. However, the effective purchase - by intermediaries -; and access to large markets are the most relevant variables in making economic decisions regarding the aggregate supply of potatoes in Huasahuasi, Este estudio tiene por objetivo identificar las variables más relevantes que explican la oferta económica de la “papa” Solanum tuberosum, en sus dos variedades: moderna y nativa, en los productores del distrito de Huasahuasi, en el departamento Junín de Perú. La metodología empleada fue la Simulación Estocástica de Monte Carlo para evaluar la rentabilidad y riesgo intrínseco de la producción agrícola en ambos tipos de oferta. Los resultados muestran que no existe una diferencia significativa en la rentabilidad y riesgo en estas dos variedades. El valor esperado de la rentabilidad, así como el valor mínimo y máximo de esta variable, para el caso de la papa moderna, son: S/. 9639, S/. -6290; y S/. 28476, respectivamente. En el caso de la papa nativa, estos valores corresponden a S/. 7240, S/. -3754; y S/. 24476. Asimismo, se logró identificar que los productores ofertan una mayor cantidad de papa moderna en comparación de la papa nativa, puesto que la productividad por unidad de tierra; y la superficie sembrada promedio por productor, ascienden a 27 t.ha-1 y16 t.ha-1; y 2,46 ha y 1,26 ha, para la papa moderna y nativa, respectivamente. No obstante, la compra efectiva – por parte de intermediarios –; y el acceso a grandes mercados, son las variables más relevantes en la toma de decisiones económicas respecto de la oferta agregada de papa en Huasahuasi

In the last decades, mathematical and computational models have become ubiquitous to the field of systems biology. Specifically, the multiscale nature of biological processes makes the design and simulation of such models challenging. In this thesis we offer a perspective on available methods to study and simulate such models and how they can be combined to handle biological processes evolving at different scales. The contribution of this thesis is threefold. First, we introduce Orchestral, a multiscale modular framework to simulate multicellular models. By decoupling intracellular chemical kinetics, cell-cell signaling, and cellular mechanics by means of operator-splitting, it is able to combine existing software into one massively parallel simulation. Its modular structure makes it easy to replace its components, e.g. to adjust the level of modeling details. We demonstrate the scalability of our framework on both high performance clusters and in a cloud environment. We then explore how center-based models can be used to study cellular mechanics in biological tissues. We show how modeling and numerical choices can affect the results of the simulation and mislead modelers into incorrect biological conclusions if these errors are not monitored properly. We then propose CBMOS, a Python framework specifically designed for the numerical study of such models. Finally, we study how spatial details in intracellular chemical kinetics can be efficiently approximated in a multiscale compartment-based model. We evaluate how this model compares to two other alternatives in terms of accuracy and computational cost. We then propose a computational pipeline to study and compare such models in the context of Bayesian parameter inference and illustrate its usage in three case studies., https://doi.org/10.33063/diva-442412

Discrete and stochastic models in systems biology, such as biochemical reaction networks, can be modeled as Markov jump processes. The chemical master equation describes how the probability distribution of a biochemical system's states evolves. Unfortunately, solutions to the chemical master equation only exist for trivial problems. However, the stochastic simulation algorithm (SSA) can generate exact sample paths. Large-scale simulation-based experiments involving variations to the model's parameters are computationally intensive and hinder modelers from exploring and inferring their models due to high-dimensional models. This thesis proposes methodologies and tools for model exploration and approximate parameter inference of high-dimensional stochastic models simulated via the SSA. We propose a smart computational workflow using machine learning-assisted approaches to enable model exploration of gene regulatory networks where the objective is to assess different qualitative behaviors present in the model. An artificial neural network is proposed for learning summary statistics used in approximate parameter inference. The neural network can find distinct local features from multivariate time series, enabling more complex models involving several biological species. By introducing epistemic uncertainty, we further explore Bayesian neural networks for approximate parameter inference. A classification approach is introduced, which learns the proposal posterior by an adaptive sampling scheme, ultimately reducing the number of simulations required for the inference task. We have also developed the software package Sciope to support modelers with machine learning-assisted techniques for model exploration and parameter inference. Sciope also comes with various features, such as experimental designs, traditional ABC algorithms, and a parallel backend to scale large simulation-based experiments from laptops to the cloud. Finally, to reduce the gap between modelers and b, eSSENCE

Since the outbreak of the novel coronavirus (COVID-19) at December 31ths of 2019 in China it quickly spread to more than 200 countries around the word. Government on affected countries have taken actions such as social distancing, reduction of social, commercial and entertainment events, among others, in order to decrease the COVID-19 spreading rate. As a way to evaluate how effective are such actions, we design an agent-based microsimulation model that allows for representing the COVID-19 spreading in Medellín, Colombia by representing individually the social interactions. To set those interactions, we cluster population (home, school, working, and shopping) and use a transport activity to move among clusters generating social interactions. Accordingly, we reproduce the number of cases and deaths caused by the COVID-19 according to Medellín-real data by using the proposed individual social interaction model. Also, we test our model with two scenarios: first one with real government actions and second one without any government actions in Medellín-Colombia. Our model results show that earlypublic-health policies allows for flatting the curve of the COVID-19 spreading in contrast to the scenario without restrictions. As future work, we will include more clusters—e.g.; leisure clusters, transport clusters—and the dynamic of the foreign COVID-19 cases ., Desde el brote del nuevo coronavirus (COVID-19) el 31 de diciembre de 2019 en China se extendió rápidamente a más de 200 países de todo el mundo. Los gobiernos de los países afectados han tomado medidas como el distanciamiento social para disminuir la tasa de propagación de COVID-19. Como una forma de evaluar la efectividad de tales acciones, diseñamos un modelo de microsimulación basado en agentes que permite representar la propagación de COVID-19 en Medellín, Colombia. En consecuencia, reproducimos el número de casos y muertes causadas por COVID-19 de acuerdo con los datos reales de Medellín utilizando el modelo propuesto. Además, probamos nuestro modelo con dos escenarios: primero con acciones gubernamentales reales y segundo sin acciones gubernamentales en Medellín-Colombia. Los resultados de nuestro modelo muestran que las políticas de salud pública tempranas permiten aplanar la curva de la propagación de COVID-19 en contraste con el escenario sin restricciones. Como trabajo futuro, incluiremos más clústeres, por ejemplo, clústeres de ocio, clústeres de transporte, y la dinámica de los casos extranjeros de COVID-19

In the last decades, mathematical and computational models have become ubiquitous to the field of systems biology. Specifically, the multiscale nature of biological processes makes the design and simulation of such models challenging. In this thesis we offer a perspective on available methods to study and simulate such models and how they can be combined to handle biological processes evolving at different scales. The contribution of this thesis is threefold. First, we introduce Orchestral, a multiscale modular framework to simulate multicellular models. By decoupling intracellular chemical kinetics, cell-cell signaling, and cellular mechanics by means of operator-splitting, it is able to combine existing software into one massively parallel simulation. Its modular structure makes it easy to replace its components, e.g. to adjust the level of modeling details. We demonstrate the scalability of our framework on both high performance clusters and in a cloud environment. We then explore how center-based models can be used to study cellular mechanics in biological tissues. We show how modeling and numerical choices can affect the results of the simulation and mislead modelers into incorrect biological conclusions if these errors are not monitored properly. We then propose CBMOS, a Python framework specifically designed for the numerical study of such models. Finally, we study how spatial details in intracellular chemical kinetics can be efficiently approximated in a multiscale compartment-based model. We evaluate how this model compares to two other alternatives in terms of accuracy and computational cost. We then propose a computational pipeline to study and compare such models in the context of Bayesian parameter inference and illustrate its usage in three case studies., https://doi.org/10.33063/diva-442412

Discrete and stochastic models in systems biology, such as biochemical reaction networks, can be modeled as Markov jump processes. The chemical master equation describes how the probability distribution of a biochemical system's states evolves. Unfortunately, solutions to the chemical master equation only exist for trivial problems. However, the stochastic simulation algorithm (SSA) can generate exact sample paths. Large-scale simulation-based experiments involving variations to the model's parameters are computationally intensive and hinder modelers from exploring and inferring their models due to high-dimensional models. This thesis proposes methodologies and tools for model exploration and approximate parameter inference of high-dimensional stochastic models simulated via the SSA. We propose a smart computational workflow using machine learning-assisted approaches to enable model exploration of gene regulatory networks where the objective is to assess different qualitative behaviors present in the model. An artificial neural network is proposed for learning summary statistics used in approximate parameter inference. The neural network can find distinct local features from multivariate time series, enabling more complex models involving several biological species. By introducing epistemic uncertainty, we further explore Bayesian neural networks for approximate parameter inference. A classification approach is introduced, which learns the proposal posterior by an adaptive sampling scheme, ultimately reducing the number of simulations required for the inference task. We have also developed the software package Sciope to support modelers with machine learning-assisted techniques for model exploration and parameter inference. Sciope also comes with various features, such as experimental designs, traditional ABC algorithms, and a parallel backend to scale large simulation-based experiments from laptops to the cloud. Finally, to reduce the gap between modelers and b, eSSENCE

In the last decades, mathematical and computational models have become ubiquitous to the field of systems biology. Specifically, the multiscale nature of biological processes makes the design and simulation of such models challenging. In this thesis we offer a perspective on available methods to study and simulate such models and how they can be combined to handle biological processes evolving at different scales. The contribution of this thesis is threefold. First, we introduce Orchestral, a multiscale modular framework to simulate multicellular models. By decoupling intracellular chemical kinetics, cell-cell signaling, and cellular mechanics by means of operator-splitting, it is able to combine existing software into one massively parallel simulation. Its modular structure makes it easy to replace its components, e.g. to adjust the level of modeling details. We demonstrate the scalability of our framework on both high performance clusters and in a cloud environment. We then explore how center-based models can be used to study cellular mechanics in biological tissues. We show how modeling and numerical choices can affect the results of the simulation and mislead modelers into incorrect biological conclusions if these errors are not monitored properly. We then propose CBMOS, a Python framework specifically designed for the numerical study of such models. Finally, we study how spatial details in intracellular chemical kinetics can be efficiently approximated in a multiscale compartment-based model. We evaluate how this model compares to two other alternatives in terms of accuracy and computational cost. We then propose a computational pipeline to study and compare such models in the context of Bayesian parameter inference and illustrate its usage in three case studies., https://doi.org/10.33063/diva-442412

Discrete and stochastic models in systems biology, such as biochemical reaction networks, can be modeled as Markov jump processes. The chemical master equation describes how the probability distribution of a biochemical system's states evolves. Unfortunately, solutions to the chemical master equation only exist for trivial problems. However, the stochastic simulation algorithm (SSA) can generate exact sample paths. Large-scale simulation-based experiments involving variations to the model's parameters are computationally intensive and hinder modelers from exploring and inferring their models due to high-dimensional models. This thesis proposes methodologies and tools for model exploration and approximate parameter inference of high-dimensional stochastic models simulated via the SSA. We propose a smart computational workflow using machine learning-assisted approaches to enable model exploration of gene regulatory networks where the objective is to assess different qualitative behaviors present in the model. An artificial neural network is proposed for learning summary statistics used in approximate parameter inference. The neural network can find distinct local features from multivariate time series, enabling more complex models involving several biological species. By introducing epistemic uncertainty, we further explore Bayesian neural networks for approximate parameter inference. A classification approach is introduced, which learns the proposal posterior by an adaptive sampling scheme, ultimately reducing the number of simulations required for the inference task. We have also developed the software package Sciope to support modelers with machine learning-assisted techniques for model exploration and parameter inference. Sciope also comes with various features, such as experimental designs, traditional ABC algorithms, and a parallel backend to scale large simulation-based experiments from laptops to the cloud. Finally, to reduce the gap between modelers and b, eSSENCE

This paper aims to identify the most relevant variables that explain the economic supply of potato “Solanum tuberosum”, in its two varieties: modern and native, in growers in the Huasahuasi district, in the Junín department of Peru. The methodology used was the Stochastic Monte Carlo Simulation to evaluate the profitability and intrinsic risk of agricultural production in both types of supply. The results show that there is no significant difference in the profitability and risk of these two varieties: The expected value of the profitability, as well as the minimum and maximum value of this variable, in the case of modern potatoes, are: S /. 9639, S /. -6290; and S /. 28476. In the case of native potatoes, these values correspond to S /. 7240, S /. -3754; and S /. 24476. Also, it was found that these growers offer a greater quantity of modern potato compared to native potato, since productivity per unit of land; and the cultivated area, average per grower, amounts to 27 t.ha-1 and 16 t.ha-1; and 2,46 ha and 1,26 ha, for modern and native potatoes, respectively. However, the effective purchase - by intermediaries -; and access to large markets are the most relevant variables in making economic decisions regarding the aggregate supply of potatoes in Huasahuasi, Este estudio tiene por objetivo identificar las variables más relevantes que explican la oferta económica de la “papa” Solanum tuberosum, en sus dos variedades: moderna y nativa, en los productores del distrito de Huasahuasi, en el departamento Junín de Perú. La metodología empleada fue la Simulación Estocástica de Monte Carlo para evaluar la rentabilidad y riesgo intrínseco de la producción agrícola en ambos tipos de oferta. Los resultados muestran que no existe una diferencia significativa en la rentabilidad y riesgo en estas dos variedades. El valor esperado de la rentabilidad, así como el valor mínimo y máximo de esta variable, para el caso de la papa moderna, son: S/. 9639, S/. -6290; y S/. 28476, respectivamente. En el caso de la papa nativa, estos valores corresponden a S/. 7240, S/. -3754; y S/. 24476. Asimismo, se logró identificar que los productores ofertan una mayor cantidad de papa moderna en comparación de la papa nativa, puesto que la productividad por unidad de tierra; y la superficie sembrada promedio por productor, ascienden a 27 t.ha-1 y16 t.ha-1; y 2,46 ha y 1,26 ha, para la papa moderna y nativa, respectivamente. No obstante, la compra efectiva – por parte de intermediarios –; y el acceso a grandes mercados, son las variables más relevantes en la toma de decisiones económicas respecto de la oferta agregada de papa en Huasahuasi

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto-and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated., Hydraulic Structures and Flood Risk