Mechanistic Model Research Articles

Intrinsic dimension as a multi-scale summary statistics in network modeling

Complex networks are powerful mathematical tools for modelling and understanding the behaviour of highly interconnected systems. However, existing methods for analyzing these networks focus on local properties (e.g. degree distribution, clustering coefficient) or global properties (e.g. diameter, modularity) and fail to characterize the network structure across multiple scales. In this paper, we introduce a rigorous method for calculating the intrinsic dimension of unweighted networks. The intrinsic dimension is a feature that describes the network structure at all scales, from local to global. We propose using this measure as a summary statistic within an Approximate Bayesian Computation framework to infer the parameters of flexible and multi-purpose mechanistic models that generate complex networks. Furthermore, we present a new mechanistic model that can reproduce the intrinsic dimension of networks with large diameters, a task that has been challenging for existing models.

Numerical simulations of particle concentration and size effects in a slurry bubble column with a CFD‐PBM coupled model

AbstractThe combined effects of particle concentration (0–20 vol%) and particle size on the hydrodynamics of a slurry bubble column were studied by computational fluid dynamics coupled with population balance model (CFD‐PBM) in a wide range of superficial gas velocity (0.02–0.20 m/s). This CFD‐PBM coupled model included multiple effects of particles, namely increased slurry viscosity, reduced drag force, promoted bubble coalescence, and attenuated liquid turbulence, among which turbulence attenuation was crucial. To quantify liquid turbulence attenuation, a mechanistic model was established by equating the energy dissipated by particle motion to the attenuated liquid turbulence energy calculated from turbulent energy spectrum. The turbulent energy dissipation rate was reduced by over 60% at a solid concentration of 20%. Using this model, the effects of particle concentration and particle size on overall gas holdup were well predicted. This model advanced our understanding of how particles affect the gas holdup in a slurry bubble column.

Guidelines for mechanistic modeling and analysis in cardiovascular research.

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

GIS-ODE: linking dynamic population models with GIS to predict pathogen vector abundance across a country under climate change scenarios.

Mechanistic mathematical models such as ordinary differential equations (ODEs) have a long history for their use in describing population dynamics and determining estimates of key parameters that summarize the potential growth or decline of a population over time. More recently, geographic information systems (GIS) have become important tools to provide a visual representation of statistically determined parameters and environmental features over space. Here, we combine these tools to form a 'GIS-ODE' approach to generate spatiotemporal maps predicting how projected changes in thermal climate may affect population densities and, uniquely, population dynamics of Ixodes ricinus, an important tick vector of several human pathogens. Assuming habitat and host densities are not greatly affected by climate warming, the GIS-ODE model predicted that, even under the lowest projected temperature increase, I. ricinus nymph densities could increase by 26-99% in Scotland, depending on the habitat and climate of the location. Our GIS-ODE model provides the vector-borne disease research community with a framework option to produce predictive, spatially explicit risk maps based on a mechanistic understanding of vector and vector-borne disease transmission dynamics.

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. This work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

No dose adjustment of metformin or substrates of organic cation transporters (OCT)1 and OCT2 and multidrug and toxin extrusion protein (MATE)1/2K with fostemsavir coadministration based on modeling approaches.

Fostemsavir is an approved gp120-directed attachment inhibitor and prodrug for the treatment of human immunodeficiency virus type 1 infection in combination with other antiretrovirals (ARVs) in heavily treatment-experienced adults with multi-drug resistance, intolerance, or safety concerns with their current ARV regimen. Initial invitro studies indicated that temsavir, the active moiety of fostemsavir, and its metabolites, inhibited organic cation transporter (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs) at tested concentration of 100 uM, although risk assessment based on the current Food and Drug Administration invitro drug-drug interaction (DDI) guidance using the mechanistic static model did not reveal any clinically relevant inhibition on OCTs and MATEs. However, a DDI risk was flagged with EMA static model predictions. Hence, a physiologically based pharmacokinetic (PBPK) model of fostemsavir/temsavir was developed to further assess the DDI risk potential of OCT and MATEs inhibition by temsavir and predict changes in metformin (a sensitive OCT and MATEs substrate) exposure. No clinically relevant impact on metformin concentrations across a wide range of temsavir concentrations was predicted; therefore, no dose adjustment is recommended for metformin when co-administered with fostemsavir.

Differential growth regulates asymmetric size partitioning in Caulobacter crescentus.

Cell size regulation has been extensively studied in symmetrically dividing cells, but the mechanisms underlying the control of size asymmetry in asymmetrically dividing bacteria remain elusive. Here, we examine the control of asymmetric division in Caulobacter crescentus, a bacterium that produces daughter cells with distinct fates and morphologies upon division. Through comprehensive analysis of multi-generational growth and shape data, we uncover a tightly regulated cell size partitioning mechanism. We find that errors in division site positioning are promptly corrected early in the division cycle through differential growth. Our analysis reveals a negative feedback between the size of daughter cell compartments and their growth rates, wherein the larger compartment grows slower to achieve a homeostatic size partitioning ratio at division. To explain these observations, we propose a mechanistic model of differential growth, in which equal amounts of growth regulators are partitioned into daughter cell compartments of unequal sizes and maintained over time via size-independent synthesis.

Experimental Investigation of Amine Regeneration for Carbon Capture through CO2 Mineralization

Summary The most common method for point-source carbon capture involves the absorption of CO2 from flue gas by amine solutions. The CO2 is then stripped from the amine solution by desorption with steam or heat. While amine solvents exhibit high CO2 absorption efficiency, their desorption process consumes a substantial amount of energy. A more energy-efficient alternative for the regeneration of the amine can be done through a mineralization process. Past studies have shown the feasibility of mineralizing captured CO2 from amine solution using calcium oxide (CaO) or other CaO-containing industrial waste. This study aims to show experimentally the extent of mineralization over time in near-ambient conditions and develop a mechanistic model. Lab-scale experiments are conducted to determine the absorption characteristics of CO2 in monoethanolamine (MEA) solutions and the extent of mineralization. We observe that pH serves as an indicator for CO2 loading in amine solutions. At high concentrations of MEA, CO2 absorption efficiency is about 0.5 mol CO2/mol MEA. Under ambient pressure and a temperature of 40°C, CaO effectively mineralizes CO2 and regenerates MEA. The CaO initially reacts with water to produce calcium hydroxide (Ca(OH)2) and subsequently reacts with dissolved CO2, forming calcium carbonate (CaCO3). Both 13C nuclear magnetic resonance (NMR) and FTIR indicate that MEA can be regenerated with an efficiency close to 69 to 98%, by comparing the carbamate and bicarbonate peaks in the 13C NMR response. A model to describe the absorption and mineralization reaction is proposed using the PHREEQC software; the pH is matched within less than 3% error. This study demonstrates that CO2 desorption from MEA solutions can occur through mineralization, converting CO2 into carbonates at low pressure and temperature with CaO. This method has lower energy consumption and results in the most stable form of CO2, making it a safer sequestration strategy than supercritical CO2 storage in reservoirs.

Effects of global change on snakebite envenoming incidence up to 2050: a modelling assessment

Human activities are driving climate, land cover, and population change (global change), and shifting the baseline geographical distribution of snakebite. The interacting effects of global change on snakes and communities at risk of snakebite are poorly understood, limiting capacity to anticipate and manage future changes in snakebite risk. In this modelling study, we projected how global change will affect snakebite envenoming incidence in Sri Lanka, as a model system that has a high incidence of snakebite. We used the shared socioeconomic pathway (SSP) scenario analysis framework to integrate forecasts across the domains of: climate change (historical trend from WorldClim plus three underlying regional circulation models [RCMs] in the Coordinated Regional Downscaling Experiment-South Asia repository, with two emissions pathways [representative concentration pathways RCP4.5 and RCP8.5]); land cover change (Dyna-CLUE model); and human population density change (based on Gridded Population of the World data) from Jan 1, 2010 to Dec 31, 2050. Forecasts were integrated under three different development scenarios: a sustainability pathway (SSP1 and no further emissions), a middle-of-the-road pathway (SSP2 and RCP4.5), and a fossil-fuelled pathway (SSP5 and RCP8.5). For SSP2 and SSP5, we nested three different RCMs (CNRM-CM5, GFDL-CCM3, and MPI-ESM-LR; mean averaged to represent consensus) to account for variability in climate predictions. Data were used as inputs to a mechanistic model that predicted snakebite envenoming incidence based on human-snake contact patterns. From 2010 to 2050, at the national level, envenoming incidence in Sri Lanka was projected to decrease by 12·0-23·0%, depending on the scenario. The rate of decrease in envenoming incidence was higher in SSP5-RCP8.5 than in SSP1 and SSP2-RCP4.5. Change in envenoming incidence was heterogenous across the country. In SSP1, incidence decreased in urban areas expected to have population growth, and with land cover changes towards anthropised classes. In SSP2-RCP4.5 and SSP5-RCP8.5, most areas were projected to have decreases in incidence (SSP5-RCP8.5 showing the largest area with incidence reductions), while areas such as the central highlands and the north of the country showed localised increases. In the model, decreases occurred with human population growth, land use change towards anthropised classes (potentially shifting occupational risk factors), and decreasing abundance of some snake species, potentially due to global warming and reduced climatic and habitat suitability, with displacement of some snake species. Snakebite envenoming incidence was projected to decrease overall in the coming decades in Sri Lanka, but with an apparent emerging conflict with sustainability objectives. Therefore, efforts to mitigate snakebite envenoming incidence will need to consider the potential impacts of sustainability interventions, particularly related to climate and land use change and in areas where increases in incidence are projected. In view of global change, neglected tropical diseases and public health issues related to biodiversity, such as snakebite, should be managed collaboratively by both environment and health stakeholders. UK Medical Research Council.

Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences.

Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations and when estimating uncertainty in model predictions. However, methods for doing this can be computationally expensive, particularly when the number of unknown model parameters is large. The aim of this study is to develop and test an efficient profile likelihood-based method, which takes advantage of the structure of the mathematical model being used. We do this by identifying specific parameters that affect model output in a known way, such as a linear scaling. We illustrate the method by applying it to three toy models from different areas of the life sciences: (i) a predator-prey model from ecology; (ii) a compartment-based epidemic model from health sciences; and (iii) an advection-diffusion reaction model describing the transport of dissolved solutes from environmental science. We show that the new method produces results of comparable accuracy to existing profile likelihood methods but with substantially fewer evaluations of the forward model. We conclude that our method could provide a much more efficient approach to parameter inference for models where a structured approach is feasible. Computer code to apply the new method to user-supplied models and data is provided via a publicly accessible repository.

34. Mechanistic model of dietary phosphorus and calcium use and the dynamics of body ash retention in lactating sows

34. Mechanistic model of dietary phosphorus and calcium use and the dynamics of body ash retention in lactating sows

Increasing intensity of enterovirus outbreaks projected with climate change

Pathogens of the enterovirus genus, including poliovirus and coxsackieviruses, typically circulate in the summer months suggesting a possible positive association between warmer weather and transmission. Here we evaluate the environmental and demographic drivers of enterovirus transmission, as well as the implications of climate change for future enterovirus circulation. We leverage pre-vaccination era data on polio in the US as well as data on two enterovirus A serotypes in China and Japan that are known to cause hand, foot, and mouth disease. Using mechanistic modeling and statistical approaches, we find that enterovirus transmission appears positively correlated with temperature although demographic factors, particularly the timing of school semesters, remain important. We use temperature projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate future outbreaks under late 21st-century climate change for Chinese provinces. We find that outbreak size increases with climate change on average, though results differ across climate models depending on the degree of wintertime warming. In the worst-case scenario, we project peak outbreaks in some locations could increase by up to 40%.

Integral-scale validation of the SCIANTIX code for Light Water Reactor fuel rods

Mechanistic multi-scale modelling holds the potential to inform fuel performance codes by incorporating high-fidelity models, algorithms, parameters, and material properties. In this context, meso-scale codes emerge as valuable tools for developing detailed models and performing separate verification and validation steps. This work focuses on SCIANTIX, an open-source 0D meso-scale code designed to describe the behaviour of gaseous and volatile fission products in nuclear oxide fuel. The code predominantly employs engineering physics-based behavioural models featuring computational times that align with typical fuel performance code requirements. Given the numerical foundation of the code, it is applicable to both stationary and transient conditions. Following a recent work outlining the standalone SCIANTIX (version 2.0) performance and its separate-effect validation database, we present its performance when coupled with fuel performance codes to simulate light water reactor fuel rods. The experiments selected for the comparative analysis constitute an initial integral validation database. The comparison focuses on conventional engineering quantities of interest, such as integral fission gas release, demonstrating the satisfactory performance of the code. Additionally, it highlights the potential advantages of multi-scale modelling over conventional semi-empirical approaches.

Elucidating the Architectural dynamics of MuB filaments in bacteriophage Mu DNA transposition

MuB is a non-specific DNA-binding protein and AAA+ ATPase that significantly influences the DNA transposition process of bacteriophage Mu, especially in target DNA selection for transposition. While studies have established the ATP-dependent formation of MuB filament as pivotal to this process, the high-resolution structure of a full-length MuB protomer and the underlying molecular mechanisms governing its oligomerization remain elusive. Here, we use cryo-EM to obtain a 3.4-Å resolution structure of the ATP(+)-DNA(+)-MuB helical filament, which encapsulates the DNA substrate within its axial channel. The structure categorizes MuB within the initiator clade of the AAA+ protein family and precisely locates the ATP and DNA binding sites. Further investigation into the oligomeric states of MuB show the existence of various forms of the filament. These findings lead to a mechanistic model where MuB forms opposite helical filaments along the DNA, exposing potential target sites on the bare DNA and then recruiting MuA, which stimulates MuB’s ATPase activity and disrupts the previously formed helical structure. When this happens, MuB generates larger ring structures and dissociates from the DNA.

Modeling the settling and resuspension of microplastics in rivers: Effect of particle properties and flow conditions

Microplastics have numerous different shapes, affecting the fate and transport of these particles in the environment. However, theoretical models generally assume microplastics to be spherical. This study aims to develop a modeling approach that incorporates the shapes of microplastics to investigate the vertical transport of microplastics in rivers and simulate the effect of particle and flow characteristics on settling and resuspension. To achieve these aims, a mechanistic model was developed utilizing the mass-balance and hydrodynamic equations. Scenario analysis was implemented assigning different values to model parameters, such as bed shear stress, shape factor and particle size to simulate the effect of flow patterns and particle properties. The model outcomes revealed that the residence time of microplastics in the water column was longest in medium bed shear stress, whilst it was shortest in low bed shear stress. This suggests that the influence of turbulence is not unidirectional; it can both increase and decrease microplastic concentrations and residence time in the water column. According to the scenario analysis, the settling flux of microplastics was the highest for near-spherical particles and increased with the size of the particles, as well as with increasing bed shear stress. However, the resuspension of particles was primarily influenced by increasing bed shear stress, but the ranking of resuspension flux values for different shaped and sized microplastics exhibited alterations with changing flow patterns. Turbulent conditions predominantly influenced the resuspension of near-spheres and large microplastics. On the contrary, the settling of fibers and small microplastics were significantly influenced by changing flow patterns, whereas near-spheres and largest particles were least affected. The model results were sensitive to changes in shape factor developed for this model, therefore this parameter should be improved in future studies.

Attenuation of phenylnaphthenic acids related to oil sands process water using solar activated calcium peroxide: Influence of experimental factors, mechanistic modeling, and toxicity evaluation

Refractory naphthenic acids (NAs) are among the primary toxic compounds in oil sands process water (OSPW), a matrix with a complex chemical composition that poses challenges to its remediation. This study evaluated the effectiveness of calcium peroxide (CaO2) combined with solar radiation (solar/CaO2) as an advanced water treatment process for degrading model NAs (1,2,3,4-tetrahydronaphthalene-2-carboxylic acid, pentanoic acid, and diphenylacetic acid) in synthetic water (STW) and provide preliminary insights in treating real OSPW. Solar light and CaO2 acted synergistically to degrade target NAs in STW (>67 of synergistic factor) following a pseudo-first-order kinetic (R2 ≥ 0.95), with an optimal CaO2 dosage of 0.1 g L–1. Inorganic ions and dissolved organic matter were found to hinder the degradation of NAs by solar/CaO2 treatment; however, the complete degradation of NAs was reached in 6.7 h of treatment. The main degradation mechanism involved the generation of hydroxyl radicals (•OH), which contributed ∼90% to the apparent degradation rate constant (K), followed by H2O2 (4–5%) and 1O2 (0–5%). The tentative transformation pathways of three NAs were proposed, confirming an open-ring reaction and resulting in short-chain fatty acid ions as final products. Furthermore, a reduction in acute microbial toxicity and genotoxic effect was observed in the treated samples, suggesting that solar/CaO2 treatment exhibits high environmental compatibility. Furthermore, the solar/CaO2 system was successfully applied as a preliminary step for real-world applications to remove natural NAs, fluorophore organic compounds, and inorganic components from OSPW, demonstrating the potential use of this technology in the advanced treatment of oil-tailing-derived NAs.

Statistics as a Social Activity: Attitudes toward Amalgamating Evidence.

Amalgamation of evidence in statistics is conducted in several ways. Within a study, multiple observations are combined by averaging, or as factors in a likelihood or prediction algorithm. In multilevel modeling or Bayesian analysis, population or prior information is combined with data using the weighted averaging derived from probability modeling. In a scientific research project, inferences from data analysis are interpreted in light of mechanistic models and substantive theories. Within a scholarly or applied research community, data and conclusions from separate laboratories are amalgamated through a series of steps, including peer review, meta-analysis, review articles, and replication studies. These issues have been discussed for many years in the philosophy of science and statistics, gaining attention in recent decades first with the renewed popularity of Bayesian inference and then with concerns about the replication crisis in science. In this article, we review the amalgamation of statistical evidence from different perspectives, connecting the foundations of statistics to the social processes of validation, criticism, and consensus building.

A method for predicting methane production from anaerobic digestion of kitchen waste under small sample conditions.

Anaerobic digestion (AD) has the great potential to treat organic waste and achieve remarkable results effectively. However, it is very tough to establish an accurate mechanistic model for this process. Data-driven modeling technology has opened a new door to solving this problem. While when the sample set is small, traditional data-driven modeling methods are often powerless. In this paper, an effective method is proposed for data-driven high-precision modeling in small sample scenarios. A time series generative adversarial network (TimeGAN) is first utilized to augment the original high-quality small-sample data collected during the AD methane production. A novel hybrid kernel extreme learning machine (HKELM) is then designed to form a better structure of the data-driven model, whose regularization coefficient C0 is optimized by the sparrow search algorithm (SSA). Finally, this semi-finished model (SSA-HKELM) is trained by the augmented data to form the final mathematical model (TimeGAN-SSA-HKELM) for the AD methane generation process. Comparative experiments of the methane daily production prediction error haveverified the effectiveness of the method, which can be extended to other similar small sample data-driven modeling scenarios.

Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.

The Development of Simulation and Optimisation Tools with an Intuitive User Interface to Improve the Operation of Electric Arc Furnaces

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.