Mechanistic Model For Prediction Research Articles

A Mechanistic Prediction Model of Resistance to Uprooting of Coniferous Trees in Heilongjiang Province, China.

Coarse roots and the root plate play an important role in tree resistance to uprooting. In this study, a qualitative mechanistic model was developed to analyze coniferous tree resistance to uprooting in relation to tree morphological characteristics. The sizes of the crown, stem, and root plate of twenty sample spruces and twenty sample Korean pines were individually measured for this purpose. Using Ground Penetrating Radar (GPR), the coarse root distribution and root plate size were detected. In the qualitative mechanistic model, a larger crown area increased the overturning moment, while higher DBH and root plate mass increased the resistance moment. The resistance coefficient (Rm) was calculated by comparing resistive and overturning moments, classifying samples into three uprooting hazard levels. Trees with smaller crown areas, larger stems, and root plates tend to have higher resistance to uprooting, as indicated by higher Rm values. This qualitative mechanistic model provides a useful tool for assessing coniferous standing tree uprooting resistance.

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

Generating synthetic signaling networks for in silico modeling studies

Predictive models of signaling pathways have proven to be difficult to develop. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. To meet this need, we developed a method for generating artificial signaling networks that were reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. We defined the reaction degree and reaction distance to measure the topology of reaction networks, especially to consider enzymes. To determine whether our generated signaling networks displayed meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModels signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModels and thus could be useful for building network evaluation tools.

Monitoring atmospheric corrosion under multi-droplet conditions by electrical resistance sensor measurement

This paper presents a novel approach to investigate atmospheric corrosion kinetics of carbon steel under multi-droplet conditions. A homemade climate chamber has been developed to accurately control and monitor environmental conditions, including temperature (T) and relative humidity (RH), during exposure. Carbon steel corrosion kinetics are monitored with a custom-designed Electrical Resistance (ER) sensor pair. Savitzky-Golay (S-G) based filtering technique has been used for the corrosion signal processing. In parallel, top-view droplet temporal evolution has been recorded by microscopic imaging and analyzed for both droplet size distribution and the solid-liquid contact angle. The droplet size distribution can typically be described with a power-law form curve. The curve shows a decrease in height and a concurrent expansion in width with progressive drying. The introduction of NaCl into the electrolyte and surface roughness variations have also been identified to substantially influence the carbon steel corrosion rate. A strong correlation between the corrosion rate derived from the ER monitoring method and the RH can be observed. This correlation is further analyzed to incorporate the impact of droplet-based electrolyte conditions. This study offers valuable insights into the development of mechanistic and kinetic prediction models for atmospheric corrosion.

Influence of test paradigm on loading dynamics during proximal femur fracture tests simulating sideways falls

Fall-related hip fractures are a serious public health issue in older adults. As most mechanistic hip fracture risk prediction models incorporate tissue tolerance, test methods that can accurately characterize the fracture force of the femur (and factors that influence it) are imperative. While bone possesses viscoelastic properties, experimental characterization of rate-dependencies has been inconsistent in the whole-femur literature. The goal of this study was to investigate the influence of experimental paradigm on loading rate and fracture force (both means and variability) during mechanical tests simulating lateral fall loadings on the proximal femur. Six pairs of matched femurs were split randomly between two test paradigms: a ‘lower rate’ materials testing system (MTS) with a constant displacement rate of 60 mm/s, and a hip impact test system (HIT) comprised of a custom-built vertical drop tower utilizing an impact velocity of 4 m/s. The loading rate was 88-fold higher for the HIT (mean (SD) = 2465.49 (807.38) kN/s) compared to the MTS (27.78 (10.03) kN/s) paradigm. However, no difference in fracture force was observed between test paradigms (mean (SD) = 4096.4 (1272.6) N for HIT, and 3641.3 (1285.8) N for MTS). Within-paradigm variability was not significantly different across paradigms for either loading rate or fracture force (coefficients of variation ranging from 0.311 to 0.361). Within each test paradigm, significant positive relationships were observed between loading rate and fracture force (HIT adjusted R2 = 0.833, p = 0.007; MTS adjusted R2 = 0.983, p < 0.0001). Overall, this study provides evidence that energy-based impact simulators can be a valid method to measure femoral bone strength in the context of fall-related hip fractures. This study motivates future research to characterize potential non-linear relationships between loading rate and fracture threshold at both macro and microscales.

Proximal termination generates a transcriptional state that determines the rate of establishment of Polycomb silencing

The mechanisms and timescales controlling de novo establishment of chromatin-mediated transcriptional silencing by Polycomb repressive complex 2 (PRC2) are unclear. Here, we investigate PRC2 silencing at Arabidopsis FLOWERING LOCUS C (FLC), known to involve co-transcriptional RNA processing, histone demethylation activity, and PRC2 function, but so far not mechanistically connected. We develop and test a computational model describing proximal polyadenylation/termination mediated by the RNA-binding protein FCA that induces H3K4me1 removal by the histone demethylase FLD. H3K4me1 removal feeds back to reduce RNA polymerase II (RNA Pol II) processivity and thus enhance early termination, thereby repressing productive transcription. The model predicts that this transcription-coupled repression controls the level of transcriptional antagonism to PRC2 action. Thus, the effectiveness of this repression dictates the timescale for establishment of PRC2/H3K27me3 silencing. We experimentally validate these mechanistic model predictions, revealing that co-transcriptional processing sets the level of productive transcription at the locus, which then determines the rate of the ON-to-OFF switch to PRC2 silencing.

Predicting fatigue crack growth rate of cement-based and asphalt paving materials based on indirect tensile cyclic loading tests

Fatigue crack growth rate (FCGR) is a critical indicator that reflects the rate at which material deterioration occurs under cyclic loads. However, there is currently a lack of a mechanistic quantitative analysis and prediction of the FCGR of paving materials under external indirect tensile (IDT) cyclic loads. To address this issue, this study aims to develop a mechanistic model for quantitative analysis and prediction of the FCGR in paving materials subjected to external IDT cyclic loads based on the J-integral based Paris’ law. Two types of paving materials, namely cement-treated aggregates and asphalt mixtures, were selected to demonstrate the results and model of the FCGR. The results indicate that the derived J-integral serves as the driving force for crack growth and is influenced by horizontal tensile stress, initial resilient modulus, damage density, specimen size, and surface energy of the paving materials. The fatigue life of cement-treated aggregates is primarily influenced by the crack initiation stage, while for asphalt mixtures, the crack growth stage plays a more significant role. The application of Paris’ law is found to be effective in predicting the FCGR at stable crack growth stage. The coefficients of Paris’ law exhibit minimal variation across different stress levels and loading frequencies, and can be utilized to evaluate the fatigue crack resistance of different paving materials.

Mechanistic model for the compression strength prediction of masonry columns strengthened with fibre–polymer composites

A mechanistic model is presented for the strength prediction of squared columns made of masonry with a periodic arrangement and strengthened with a fibre–polymer composite jacketing. The formulation is based on an incremental plasticity theory that relies on equilibrium, compatibility, and kinematic equations. The strength domain of brick units and mortar joints is bounded by a multi-surface yield criterion: a Mohr–Coulomb strength domain with a linear cap in compression and a Rankine cut-off in tension. An elasto-plastic response with limited ductility is assumed for both masonry components. Differently, the FRP response is assumed elastic with a brittle failure governed by a limited tensile strain. Phenomenological-based assumptions are undertaken and justified. Details are also provided for the computational implementation of the procedure. The model accuracy is validated against experimental data on masonry squared columns and compared with existing standard-based formulas. Results demonstrate it provides real-time and accurate compressive strength solutions for squared masonry columns with or without a polymer-based wrapping and yet requiring few input parameters for the masonry constituents and reinforcement.

A Hybrid Model that Combines Machine Learning and Mechanistic Models for Useful Grass Growth Prediction

Recently, Machine Learning (ML) has been heralded as a panacea for modelling problems across many domains, including Smart Agriculture (SmartAg), often in opposition to traditional mechanistic models arising on decades of scientific discovery. However, mechanistic models are often successful in “real world” problem-domains where ML models encounter difficulties (e.g., where the distribution of test data is not the same as the training data, violating the so-called identical and independently distributed (i.i.d.) assumption). In this paper, we consider a specific case of this opposition between a mechanistic model of grass growth and a ML model using historical, farm measurements. In our analyses, we find that both types of model have respective strengths. The mechanical model can often handle out-of-distribution events better than ML model, but the ML model can often handle temporary fluctuations in event variables (e.g., changing climate factors). Hence, we propose a combined hybrid model that learns which model to use when predicting grass growth. We argue that this combined approach has several practical benefits in providing stable and accurate predictions under widely varying conditions such as never before seen temperature fluctuations.

Mechanistic prediction of folding angles in 4D printed shape memory polymers under varied loading conditions

Four-dimensional printing technology empowers 3D-printed structures to change shapes upon external stimulation. However, most studies did not consider recovery under loaded conditions. This paper introduces a mechanistic prediction model for forecasting recovery angles in 4D printing utilizing shape memory polymer under various loads. The model integrates Neo–Hookean model to describe the non-linear stress–strain relationship with experimentally determined force density data to characterize polymer restoration properties under various loads. Validation was demonstrated by the recovery experiment of a 3D-printed polylactic acid-thermoplastic polyurethane composite structure loaded by means of a cord and pulley mechanism. The predictive outcomes exhibited reasonable agreement with experimental results, demonstrating a trend of more accurate forecasts as the applied load increased. The model can accommodate various active materials provided that the pertaining force density data is accessible. The predictive model supports the design, optimization and material selection for 4D-printed structures to meet specific performance requirements.

Hybrid models of machine-learning and mechanistic models for indoor particulate matter concentration prediction

While indoor PM2.5 concentrations are generally lower than those outdoors, daily exposure indoors can significantly exceed outdoor levels, given that people spend over 90% of their time inside. This study systematically compares traditional methods with machine learning(ML)-based approaches in the context of indoor particulate matter concentration prediction and control research, with the aim of combining their strengths for more effective outcomes.One-year indoor PM concentration data from a non-interference-operating target office were used to develop ML models. Input variables, including background concentrations (outdoor and hallway), office conditions, and weather parameters, were employed with the CNN-LSTM algorithm. The ML models, enhanced with ensemble techniques, achieved a prediction accuracy of R2 = 0.943 on the test set, not considered during model development.Using predictions under step-change events in outdoor PM2.5 levels, the ML model determined the infiltration factor, PM2.5 removal rates, and particle penetration rates, hence mechanistic model coefficients. Collaborative hybrid models, integrating ML and mechanistic approaches, yielded dynamic models (R2 = 0.928) applicable under general conditions, whereas conventional mechanistic models derived from controlled experiments were limited to controlled conditions.

Prediction of erosive wear – A novel mathematical model

Several authors in past idealized erodent particles by assuming of spherical shapes. In reality, particles predominantly have irregular and angular shapes. The present study evolved a novel mechanistic mathematical model for prediction of solid particle erosive wear assuming the angular particles of square pyramidal shapes. The proposed model predicts the absolute volume loss as a cumulative contribution of deformation damage and cutting removal. It includes the effect of velocity, impingement angle, mass, abrasiveness factor of striking particles and the properties of target material. It can be effectively used for prediction of erosive wear for jet type of fluid flow.

A comparison of empirical and mechanistic models for wheat yield prediction at field level in Moroccan rainfed areas

Abstract In the context of climate change, in-season and longer-term yield predictions are needed to anticipate local and regional food crises and propose adaptations to farmers’ practices. Mechanistic models and machine learning are two modelling options to consider from this perspective. In this study, multiple regression (MR) and random forest (RF) models were calibrated for wheat yield prediction in Morocco, using data collected from 125 farmers’ wheat fields. Additionally, MR and RF models were calibrated both with or without remotely sensed leaf area index (LAI), while considering all farmers’ fields, or specifically to agroecological zoning in Morocco. The same farmers’ fields were simulated using a mechanistic model (APSIM-wheat). We compared the predictive performances of the empirical models and APSIM-wheat. Results showed that both MR and RF showed rather good predictive quality (normalized root mean square errors (NRMSEs) below 35 %), but were always outperformed by the APSIM model. Both RF and MR selected remotely sensed LAI at heading, climate variables (maximal temperatures at emergence and tillering), and fertilization practices (amount of nitrogen applied at heading) as major yield predictors. Integration of remotely sensed LAI in the calibration process reduced NRMSE by 4.5 % and 1.8 % on average for MR and RF models, respectively. Calibration of region-specific models did not significantly improve the predictive. These findings lead to the conclusion that mechanistic models are better at capturing the impacts of in-season climate variability and would be preferred to support short-term tactical adjustments to farmers’ practices, while machine learning models are easier to use in the perspective of mid-term regional prediction.

Pollination supply models from a local to global scale

Abstract. Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply and validate these predictions using data from a newly released global dataset on pollinator visitation rates to different crops. We use one of the most extensively used models for the mechanistic approach, while for the data-driven approach, we select from among a comprehensive set of state-of-the-art machine-learning models. Moreover, we explore a mixed approach, where data-derived inputs, rather than expert assessment, inform the mechanistic model. We find that, at a global scale, machine-learning models work best, offering a rank correlation coefficient between predictions and observations of pollinator visitation rates of 0.56. In turn, the mechanistic model works moderately well at a global scale for wild bees other than bumblebees. Biomes characterized by temperate or Mediterranean forests show a better agreement between mechanistic model predictions and observations, probably due to more comprehensive ecological knowledge and therefore better parameterization of input variables for these biomes. This study highlights the challenges of transferring input variables across multiple biomes, as expected given the different composition of species in different biomes. Our results provide clear guidance on which pollination supply models perform best at different spatial scales – the first step towards bridging the stakeholder–academia gap in modelling ecosystem service delivery under ecological intensification.

Model based process optimization of an industrial chromatographic process for separation of lactoferrin from bovine milk

Model based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the last years, it is rarely applied in food industry. This case study investigates risk ranking and possible optimization of the industrial process of purifying lactoferrin from bovine milk using SP Sepharose Big Beads with a resin particle diameter of 200 µm, based on a minimal number of lab-scale experiments combining traditional scale-down experiments with mechanistic modeling. Depending on the location and season, process water pH and the composition of raw milk can vary, posing a challenge for highly efficient process development.A predictive model based on the general rate model with steric mass action binding, extended for pH dependence, was calibrated to describe the elution behavior of lactoferrin and main impurities. The gained model was evaluated against changes in flow rate, step elution conditions, and higher loading and showed excellent agreement with the observed experimental data. The model was then used to investigate the critical process parameters, such as water pH, conductivity of elution steps, and flow rate, on process performance and purity. It was found that the elution behavior of lactoferrin is relatively consistent over the pH range of 5.5 to 7.6, while the elution behavior of the main impurities varies greatly with elution pH. As a result, a significant loss in lactoferrin is unavoidable to achieve desired purities at pH levels below pH 6.0. Optimal process parameters were identified to reduce water and salt consumption and increase purity, depending on water pH and raw milk composition. The optimal conductivity for impurity removal in a low conductivity elution step was found to be 43 mS/cm, while a conductivity of 95 mS/cm leads to the lowest overall salt usage during lactoferrin elution. Further increasing the conductivity during lactoferrin elution can only slightly lower the elution volume thus can also lead to higher total salt usage. Low flow rates during elution of 0.2 column volume per minute are beneficial compared to higher flow rates of 1 column volume per minute.The, on lab-scale, calibrated model allows predicting elution volume and impurity removal for large-scale experiments in a commercial plant processing over 106 liters of milk per day. The successful model extrapolation was possible without recalibration or detailed knowledge of the manufacturing plant. This study therefore provides a possible pathway for rapid process development of chromatographic purification in the food industries combining traditional scale-down experiments with mechanistic modeling.

Prediction of multilayer Cr/GLC coatings degradation in deep-sea environments based on integrated mechanistic and machine learning models

Improving the accuracy of coating lifetime prediction on small sample data has been an urgent issue to be addressed. In this paper, based on in-situ electrochemical impedance spectroscopy data of multilayer Cr/GLC coatings, a lifetime prediction formula related to the coating failure mechanism is developed, which provides a quantitative basis for estimating the coating lifetime. Furthermore, the combination of the mechanistic prediction model and the ANN + RF integrated machine learning model can further increase the model’s prediction accuracy, reach 97.9%, and provide a new method for predicting coating performance and lifetime in deep-sea environments.

Investigation of hydrated channels and proton pathways in a high-resolution cryo-EM structure of mammalian complex I.

Respiratory complex I, a key enzyme in mammalian metabolism, captures the energy released by reduction of ubiquinone by NADH to drive protons across the inner mitochondrial membrane, generating the proton-motive force for ATP synthesis. Despite remarkable advances in structural knowledge of this complicated membrane-bound enzyme, its mechanism of catalysis remains controversial. In particular, how ubiquinone reduction is coupled to proton pumping and the pathways and mechanisms of proton translocation are contested. We present a 2.4-Å resolution cryo-EM structure of complex I from mouse heart mitochondria in the closed, active (ready-to-go) resting state, with 2945 water molecules modeled. By analyzing the networks of charged and polar residues and water molecules present, we evaluate candidate pathways for proton transfer through the enzyme, for the chemical protons for ubiquinone reduction, and for the protons transported across the membrane. Last, we compare our data to the predictions of extant mechanistic models, and identify key questions to answer in future work to test them.

Soil pore structure mediates the effects of soil oxygen on the dynamics of greenhouse gases during wetting–drying phases

The timing and magnitude of greenhouse gas (GHG) production depend strongly on soil oxygen (O2) availability, and the soil pore geometry characteristics largely regulate O2 and moisture conditions relating to GHG biochemical processes. However, the interactions between O2 dynamics and the concentration and flux of GHGs during the soil moisture transitions under various soil pore conditions have not yet been clarified. In this study, a soil-column experiment was conducted under wetting–drying phases using three pore-structure treatments, FINE, MEDIUM, and COARSE, with 0 %, 30 %, and 50 % coarse quartz sand applied to soil, respectively. The concentrations of soil gases (O2, nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4)) were monitored at a depth of 15 cm hourly, and their surface fluxes were measured daily. Soil porosity, pore size distribution, and pore connectivity were quantified using X-ray computed microtomography. The soil O2 concentrations were found to decline sharply as soil moisture increased to the water holding capacities of 0.46, 0.41, and 0.32 cm cm−3 in the FINE, MEDIUM, and COARSE, respectively. The dynamic patterns of the O2 concentrations varied across the soil pore structures, decreasing to anaerobic in FINE (<0.01 %) and MEDIUM (0.02 %), and to hypoxic (4.42 %) in COARSE. Correspondingly, the soil N2O concentration was the highest in FINE (101 μL L−1) and the lowest in COARSE (10 μL L−1), whereas the highest surface N2O flux was observed in MEDIUM (131 μg N m−2 h−1). As soil CO2 concentrations declined, CO2 fluxes increased from FINE to MEDIUM to COARSE. Most pores of FINE, MEDIUM, and COARSE were 15–80 μm, 85–100 μm, and 105–125 μm, respectively, in terms of diameter. The X-ray CT visible (>15 μm) porosity in FINE, MEDIUM and COARSE were 0.09, 0.17, and 0.28 mm3 mm−3, respectively. The corresponding Euler-Poincaré numbers were 180,280, 76,705, and −10,604, respectively, indicating higher connectivity in COARSE than in MEDIUM or FINE. In soil dominated by small air-filled porosity which limits gas diffusion and result in low soil O2 concentration, N2O concentration was increased and CO2 flux was inhibited as the moisture content increased. The turning point in the sharp decrease in O2 concentration was found to correspond with a moisture content, and a pore diameter of 95–110 μm was associated with the critical turning point between holding water and O2 depletion in soil. These findings suggest that O2-regulated biochemical processes are key to the production and flux of GHGs, which in turn are dependent on the soil pore structure and a coupling relationship between N2O and CO2. Improved understanding of the intense effect of soil physical properties provided an empirical foundation for the future development of mechanistic prediction models for how pore-space scale processes with high temporal (hourly) resolution up to GHGs fluxes at larger spatial and temporal scales.

Comparing modeling approaches in simulating a continuous pilot-scale wet vertical stirred mill using PBM-DEM-CFD

Vertical stirred mills have become increasingly popular in size reduction operations in the fine and ultrafine range, being normally used in industry in continuous operation. The present work describes the application of a mechanistic mill modeling approach based on the population balance (PBM) to describe the performance of a pilot-scale gravity-induced continuous stirred mill using 6 mm steel grinding media. At first, DEM and coupled DEM-CFD were compared in simulation of the mill, clearly demonstrating the significant effect of incorporating explicitly the fluid flow in describing the motion of the grinding media. The work then uses data from the stirred mill grinding calcite in multiple passes for model calibration and validation, besides breakage parameters from previous studies. It shows that the population balance-based mechanistic mill model predictions vary depending on the number of mixers-in-series used to predict product size, but that are capable of describing very well the milling operation considering the mill as three perfectly mixed grinding zones in series.

Directly monitoring the shift in corrosion mechanisms of a model FeCrNi alloy driven by electric potential

Stainless steels are used in a myriad of engineering applications, including construction, automobiles, and nuclear reactors. Developing accurate, predictive mechanistic models for corrosion and electrochemical corrosion kinetics of stainless steels has been a topic of research studies over many decades. Herein, we quantified the aqueous corrosion kinetics of a model austenitic Fe–18Cr–14Ni (wt%) alloy in the presence and absence of applied potential using systematic in situ electrochemical atomic force microscopy (EC-AFM) and transmission electron microscopy (TEM). Without an applied bias, vertical dissolution of corrosion pits is controlled by the surface kinetics/diffusion hybrid mechanism, whereas lateral dissolution is diffusion controlled. When an electric bias is applied, the increase in corrosion rate is dominated by the nucleation of new pits. These insights gained by the in situ EC-AFM will allow applications of this method for a quantitative understanding of corrosion of a wider class of materials.