Alternative Modeling Approach Research Articles

Matching decision support modeling frameworks to disease emergence stages and associated management objectives

AbstractWildlife disease management decisions often require rapid responses to situations that are fraught with uncertainty. By recognizing that management is implemented to achieve specific objectives, resource managers and science partners can identify an analysis technique and develop a monitoring plan to evaluate management effectiveness. For emerging infectious diseases, objectives may take several distinct forms, dependent on the perceived stage of disease emergence (i.e., pre‐epidemic, early outbreak, mid‐epidemic, and endemic), the expected rate of spread, and the anticipated effect of the disease on host populations. Identifying modeling techniques and metrics that are linked to management objectives will require early and consistent communication between managers and science partners. We link modeling approaches that can be used to forecast and evaluate the performance of intervention strategies with a range of disease management objectives. Our aim is to help scientists recognize alternative modeling approaches which may better align with different forms of disease management objectives, and to help managers evaluate the relevance of proposed modeling approaches to their specified objectives for disease management. Recognizing that disease management objectives can take different forms, and thus require different modeling approaches, can help wildlife disease response teams (i.e., natural resource managers, scientists, and stakeholders working collaboratively) better prepare and respond to disease threats.

Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit

ObjectiveConventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk...

On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions

Abstract. The study aims to demonstrate that an effective solution can be implemented for modeling complex carbonate basins, in the situation of limited data availability. Considering the alternative modeling approaches under circumstances of data shortage is more significant knowing the vulnerability and effectiveness of these kinds of basins to drought and climate change conditions. In this regard, a hybrid approach that combines time series analysis and reservoir modeling is proposed to describe behavior in carbonate basins. Time series analysis estimates the contributing area and response time of the fractured carbonate system beyond the catchment's hydrographic boundaries. The results obtained align with previous literature-based field surveys. This information is then used to develop a conceptual reservoir system using the GEOframe modeling system. The model is validated using in situ discharge observations and Earth observations (EO) data on evapotranspiration and snow. Model reliability is assessed using traditional goodness of fit indicators, hydrological signatures, and a novel statistical method based on empirical conditional probability. This approach enables detailed analysis and investigation of water budget components in Mediterranean carbonate catchments, highlighting their response to significant precipitation deficits. Overall, our results demonstrate that flows from carbonate rock areas outside the hydrographic boundaries significantly impact the water budget of the upper Nera River. The storage capacity of the carbonate basin plays a crucial role in sustaining river discharge during drought years. In a single dry year, meteorological drought is considerably attenuated, while in subsequent dry years, it is slightly intensified. Multi-year droughts result in slower recovery due to the time required for precipitation to replenish the depleted storage that supported river discharge in previous dry years. This unique behavior makes these basins particularly vulnerable to the more severe and frequent drought episodes expected under future climate change.

Quantifying pesticide emissions for drift deposition in comparative risk and impact assessment

Estimating emissions of chemical pesticides used in agriculture is an essential component in evaluating the potential toxicity-related impacts on humans and ecosystems in various comparative risk and impact assessment frameworks, such as life cycle assessment, environmental footprinting, absolute environmental sustainability assessment, chemical substitution, and risk prioritization. Emissions related to drift deposition—usually derived from drift experiments—can reach non-target areas, and vary as a function of crop characteristics and application technique. We derive cumulative drift deposition fractions for a wide range of experimental drift functions for use in comparative and mass-balanced approaches. We clarify that cumulative drift deposition fractions require to integrate the underlying drift functions over the relevant deposition area and to correct for the ratio of deposition area to treated field area to arrive at overall mass deposited per unit mass of applied pesticide. Our results show that for most crops, drift deposition fractions from pesticide application are below 0.03 (i.e. 3% of applied mass), except for grapes and fruit trees, where drift fractions can reach 5% when using canon or air blast sprayers. Notably, aerial applications on soybeans can result in significantly higher drift deposition fractions, ranging from 20% to 60%. Additionally, varying the nozzle position can lead to a factor of five differences in pesticide deposition, and establishing buffer zones can effectively reduce drift deposition. To address remaining limitations in deriving cumulative drift deposition fractions, we discuss possible alternative modelling approaches. Our proposed approach can be implemented in different quantitative and comparative assessment frameworks that require emission estimates of agricultural pesticides, in support of reducing chemical pollution and related impacts on human health and the environment.

Generative spatial generalized dissimilarity mixed modelling (spGDMM): An enhanced approach to modelling beta diversity

Abstract Turnover, or change in the composition of species over space and time, is one of the primary ways to define beta diversity. Inferring what factors impact beta diversity is not only important for understanding biodiversity processes but also for conservation planning. At present, a popular approach to understanding the drivers of compositional turnover is through generalized dissimilarity modelling (GDM). We argue that the current GDM approach suffers several limitations and provide an alternative modelling approach that remedies these issues. We propose using generative spatial random effects models implemented in a Bayesian framework. We offer hierarchical specifications to yield full regression and spatial predictive inference, both with associated full uncertainties. The approach is illustrated by examining dissimilarity in three datasets: tree survey data from Panama's Barro Colorado Island (BCI), plant occurrence data from southwest Australia and plant abundance surveys from the Greater Cape Floristic Region (GCFR) of South Africa. We select a best model using out‐of‐sample predictive performance. We find that the form of the best model differs across the three datasets, but our models provide performance ranging from comparable to significant improvement over GDMs. Within the GCFR, the spatial random effects play a more important role in the modelling than all the environmental variables. We have proposed a model that provides several improvements to the current GDM framework. This includes advantages such as a flexible spatially varying mean function, spatial random effects that capture dependence unaccounted for by explanatory variables, and spatially heterogeneous variance structure. All these features are offered in a model that can adequately handle a large incidence of total dissimilarity through ‘one‐inflation’, as would be expected from highly biodiverse areas with steep turnover gradients.

Digital rock advances from a material point method approach for simulation of frame moduli and a sedimentary petrology-inspired method for creation of synthetic samples through simulation of deposition and diagenesis

We compare hydromechanical simulation results that use two alternative sources of 3D digital rock input: micro-CT analysis and “synthetic rocks” created by using a newly developed process simulation methodology that more rigorously reflects knowledge from sedimentary petrology compared with previous efforts. We evaluate the performance of these alternative representations using St. Peter Sandstone samples where “dry” static bulk modulus ( K) and shear modulus ( G) are simulated using a new extension of the material point method that resolves contacts using high-resolution surface meshes and considers three alternative contact modeling approaches: “purely frictional,” “fully bonded,” and “cohesive zones.” We evaluate the model performance on two samples from the data set with multiple static moduli measurements (sample 1_2: porosity 24.6 vol%, K 10.2–14.7 GPa, and G 11.6–14.0 GPa; sample 11_2: porosity 12.4 vol%, K 13.5–24.6 GPa, and G 12.8–17.9 GPa). Purely frictional results underpredict measured modulus values, whereas fully bonded results overpredict them. Measured values are most closely approximated by results with cohesive zones that consider sets of discrete spring-like features at contacts. In contrast, shear modulus results from finite-element model simulations on structured grids tend to be significantly greater than measured values, particularly for samples with <18 vol% porosity. Permeability values from digital rock-physics simulations for the studied samples are within factors of 2–5 of conventional core analysis measurements (2860 and 58 mD for samples 1_2 and 11_2, respectively). We determine that the process modeling approach (1) accurately reproduces the measured rock microstructure parameters from thin-section analysis, (2) leads to simulation results for dry static moduli and permeability with accuracy comparable to simulations that use micro-CT samples, and (3) provides a rigorous basis for predicting diagenetically induced variations in hydromechanical properties over the range from unconsolidated sand to indurated rock.

Clarifying status of DNNs as models of human vision.

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.

Abstract 12291: Impact of History of Mental Health Disorder on In-Hospital Mortality in Patients With Heart Failure. A Population-Based Cohort Study

Introduction: Mental health disorders are associated with lower life expectancy and patients with mental disorders are at an increased risk of heart failure. Data on the prognostic impact of mental disorders on heart failure outcomes is limited and inconclusive. Hypothesis: Mental health disorders are associated with increased risk of in-hospital mortality in patients with heart failure. Methods: The study population were patients aged ≥18 years in acute care hospitals with heart failure as principal diagnosis in Texas for years 2016 through 2021. Three prespecified models were applied to estimate the association between mental disorders and in-hospital mortality. Multilevel, multivariable logistic regression with propensity adjustment was the primary analysis approach with propensity score matching and multivariable logistic regression without propensity adjustment applied as alternative analyses. Additional analyses were completed for patient subgroups, categories of mental disorders, and classifications of heart failures. Results: Out of 513,565 heart failure hospitalizations, 103,675 (20.2%) had mental disorders. Heart failure hospitalizations with vs without mental disorders were more commonly female (61.3% vs 46.0%), white (55.3% vs 46.9%), and obese (31.6% vs 26.4%), but had less frequent need for mechanical ventilation (1.8% vs 2.1%); p < 0.0001 for each. Unadjusted in-hospital mortality among heart failure hospitalizations with and without mental disorders was 1.7% vs 2.2%. On adjusted analyses, mental disorders remained associated with lower odds of in-hospital mortality (adjusted odds ratio 0.8650 [95% CI 0.8149-0.9183]). The results were similar for the alternative modeling approaches, subgroup analyses, and classifications of heart failure. Conclusion: Mental disorders were associated with substantially lower risk of in-hospital mortality for patients with a principal diagnosis of heart failure.

Abstract 14620: Association of Rural Residence With In-Hospital Mortality for Heart Failure Hospitalizations in Texas: A Population-Based Cohort Study

Background: Rural residents with heart failure may be at higher risk of mortality due to disparities in access to health care services. Patterns of urban-rural disparity in health care resources may vary across the United States and population level analyses of the impact of rural residence on hospitalized patients with heart failure in Texas are not conclusive. Hypothesis: Rural residence is associated with increased risk of in-hospital mortality in patients with heart failure. Methods: We used publicly available, statewide, and deidentified hospitalization records to study patients aged ≥18 years with heart failure as principal diagnosis in Texas for years 2016 through 2021. Three models estimated the association between rural residence and in-hospital mortality. Multilevel logistic regression with propensity adjustment was the primary analysis approach with propensity score matching and multivariable logistic regression without propensity score matching applied as alternative analyses. Additional analyses were completed for patient subgroups and for categories of heart failures. Results: Out of 460,798 heart failure hospitalizations (HFH), 61,480 (13.3%) were classified as rural residents. HFH with vs without rural residence were older (70.4% vs 64.8% aged ≥ 65years), more often white (63.5% vs 46.9%), less frequently had private insurance (26.2% vs 35.4%) and were less often admitted to teaching hospitals (19.5% vs 28.9%); p < 0.0001 for each. Unadjusted in-hospital mortality among HFH with and without rural residence was 2.5% vs 2.1%. On adjusted analyses, rural residence remained associated with higher odds of in-hospital mortality (adjusted odds ratio 1.1685 (95% CI [1.0878 - 1.2554]). The results were similar for the alternative modeling approaches, subgroup analyses, and categories of heart failures. Conclusions: Rural residence in Texas was associated with higher risk of in-hospital mortality in patients with heart failure.

Predictive Modeling for Highway Pavement Rutting: A Comparative Analysis of Auto-Machine Learning and Structural Equation Models

Highway pavements deteriorate over time as successive wheel loads cause rutting, cracking, texture loss, and so forth. Design standards and pavement performance models account for some of the known contributory factors, such as levels of traffic and vehicle composition. However, such models are limited in their predictive power, and highway authorities must conduct regular pavement condition surveys rather than relying on the standard deterioration models alone. The ways in which multiple factors affect pavement deterioration, including rutting, are complex and are believed to include feedback loops where rutting then influences driving position, exacerbating the rutting levels. Standard regression models are not well suited to representing such complex causal mechanisms. This paper compares two alternative modeling approaches, structural equation models and auto-machine learning, and evaluates the predictive ability and practicalities of each. The findings indicate that auto-machine learning (AutoML) may be superior in its predictive ability. However, the “black box” nature of AutoML results makes them potentially less useful to practitioners. A process of using machine learning to help inform a structural equation model is proposed.

How Much Will Climate Change Reduce Productivity in a High-Technology Supply Chain? Evidence from Silicon Wafer Manufacturing

The frequency of hot days in much of the world is increasing. What is the impact of high temperatures on productivity? Can technology-based adaptation mitigate such effects of climate change? We provide some answers to these questions by examining how high outdoor temperatures affect a high-technology, precision manufacturing setting. Exploiting individual-level data on the quantity and quality of work done across 35,190 worker-shifts in a leading NYSE-listed silicon wafer maker in China, we evidence a negative effect of outdoor heat on productivity. The effects are large: in our preferred linear specification, an increase in wet bulb temperature of 10,^{circ }C causes a reduction in output of 8.3%. Temperature effects exist even though the manufacturer’s work-spaces are indoors and protected by high-quality climate control systems. Results are not driven by extreme weather events and are robust to alternative modelling approaches. They illustrate the potential future adverse economic effects of climate change in most of the industrialised world.

Bioconcentration Assessment in Fish Based on In Vitro Intrinsic Clearance: Predictivity of an Empirical Model Compared to In Vitro-In Vivo Extrapolation Models.

To estimate the bioconcentration factor (BCF), the in vitro intrinsic clearance (CLINVITRO,INT) from rainbow trout liver S9 fractions (RT-S9) can be applied to in vitro-in vivo extrapolation (IVIVE) models, yet uncertainties remain in model parameterization. An alternative model approach is evaluated: a regression model was built in the form log BCF = a × log Kow + b × log CLINVITRO,INT. The coefficients a and b were fitted based on a training set of 40 chemicals. A high robustness of the coefficients and good accuracy of BCF prediction were found on independent datasets of neutral organic chemicals (measured log Kow 3.3-6.2). BCF predictions were similar to or in better agreement with in vivo BCFs compared to IVIVE models (2.4- to 2.9- vs 2.8- to 3.6-fold misprediction) for training and test sets. Species-matched models (trout, carp) did not result in improvements. This study presents the largest dataset on CLINVITRO,INT and BCFs to assess predictivity of the RT-S9 assay. The robustness of the regression statistics on different datasets and the high statistical weight of the CLINVITRO,INT term illustrate the predictive power of the RT-S9 assay as an important step toward regulatory acceptance to replace animal experiments.

A pressure-coupled Representative Interactive Linear Eddy Model (RILEM) for engine simulations

The Representative Linear Eddy Model (RILEM) was introduced by Lackmann et al. (2018) as an alternative modeling approach to simulate turbulent non-premixed combustion in engines. The model utilizes a RANS approach for turbulence and the Linear Eddy Model (LEM) with a presumed probability density function (PDF) approach for combustion closure. A distinct feature of RILEM is its potential to handle arbitrary combustion regimes and the consideration of complex physical phenomena such as differential diffusion effects. The original version of RILEM implemented a volume-based coupling between LEM and the flow solver. This work presents a new variant of RILEM, i.e., Multiple Representative Interactive Linear Eddy Model (MRILEM) based on a pressure-based coupling, to overcome some deficiencies of the original RILEM, namely statistical fidelity. Due to the introduced pressure coupling, the effects of heat losses (wall heat fluxes, latent heat of evaporation) on combustion are intrinsically included via the pressure trace. Furthermore, we introduce a new step function PDF for the progress variable defined by its mean value only. Issues with an incomplete solution space for mixture fraction and progress variable due to the stochastic nature of LEM are remedied with a PDF scaling technique, aided by a novel parameterization of the progress-variable PDF The new variant of RILEM is evaluated using part- and full-load cases of a heavy-duty metal engine. The impact of utilizing multiple LEM lines on the completeness of the solution space and its influence on the distribution of scalar values in the CFD domain was demonstrated. Results for pressure trace, flame structure, and CO emissions are analyzed and compared with simulations using the Multi-Zone Well-Mixed Model (MZWM) model and experiments. While pressure traces agree well among the different models and experiments, noteworthy differences are observed between the models regarding CO emissions and temperature. Effects of turbulence chemistry interaction were noticed when comparing MRILEM to the results of the MZWM simulation, namely flame brush and species mass fraction distribution.

A Computationally Efficient Multiscale, Multi-Phase Modeling Approach Based on CPFEM to Assess the Effect of Second Phase Particles on Mechanical Properties

Crystal plasticity finite element (CPFEM) modeling of metals that can be age hardened consisting of second phase particles is extensively performed based on representative volume element (RVE) models. The RVE model is generated for ferritic low-carbon steel using the data obtained from microstructural observation through optical microscopy (OM) and electron backscatter diffraction (EBSD). The generated RVE is required to statistically represent the original material in terms of grain topology and texture in microscale, as well as the configuration of second phase particles in submicron scale. The multiscale, multi-phase nature of the generated RVE leads to a computationally expensive modeling procedure. To overcome this issue, an alternative multiscale modeling approach based on a homogenization scheme is proposed, in which the effect of second phase particles on deformation behavior is accounted for with no need for the explicit presence of particles in RVE. Lastly, a thorough parametric analysis is performed to investigate the sensitivity of the mechanical properties to the second phase particles in terms of size, volume fraction, geometrical distribution, and deformable or non-deformable properties of precipitates in the investigated material. The results show that the proposed multiscale modeling approach successfully accounts for the effect of second phase particles on deformation behavior, while the computational cost is reduced by more than 99%. In addition, the simulations show that the configuration of second phase particles at a microscale plays an important role in defining the mechanical behavior of the material.

An intercomparison of models predicting growth of Antarctic krill (Euphausia superba): The importance of recognizing model specificity.

Antarctic krill (Euphausia superba) is a key species of the Southern Ocean, impacted by climate change and human exploitation. Understanding how these changes affect the distribution and abundance of krill is crucial for generating projections of change for Southern Ocean ecosystems. Krill growth is an important indicator of habitat suitability and a series of models have been developed and used to examine krill growth potential at different spatial and temporal scales. The available models have been developed using a range of empirical and mechanistic approaches, providing alternative perspectives and comparative analyses of the key processes influencing krill growth. Here we undertake an intercomparison of a suite of the available models to understand their sensitivities to major driving variables. This illustrates that the results are strongly determined by the model structure and technical characteristics, and the data on which they were developed and validated. Our results emphasize the importance of assessing the constraints and requirements of individual krill growth models to ensure their appropriate application. The study also demonstrates the value of the development of alternative modelling approaches to identify key processes affecting the dynamics of krill. Of critical importance for modelling the growth of krill is appropriately assessing and accounting for differences in estimates of food availability resulting from alternative methods of observation. We suggest that an intercomparison approach is particularly valuable in the development and application of models for the assessment of krill growth potential at circumpolar scales and for future projections. As another result of the intercomparison, the implementations of the models used in this study are now publicly available for future use and analyses.

Invertible Koopman Network and its application in data-driven modeling for dynamic systems

Koopman operator, acting on an infinite-dimensional Hilbert space of the observables, provides a global systematic linear representation of nonlinear systems, which is a leading candidate for data-driven modeling. In practical applications, the observable function is required to have an invertible property to predict or control the original system. However, two approaches for constructing the invertible observable functions in current research, introducing the full-state observables and adopting the auto-encoder, have drawbacks such as overly strong assumptions and lossy reconstruction. To address these issues, we propose the Invertible Koopman Network (IKN) to approximate the Koopman operator. With special invertible blocks, IKN achieves structure-level reversibility while retaining sufficient nonlinear mapping capability. This network results in a transformation from the complex dynamic system’s states to the observables with simpler dynamics and affords lossless reconstruction via its invertible process. Then, we use the IKN as a dynamical embedding model and adopt the Transformer based on an autoregressive task to learn the evolution pattern of the observables. Through the greedy decoding strategy, the trained IKN and Transformer model enable data-driven prediction where only initial value is provided. The method is tested on three typical chaotic dynamic systems (Lorenz system, Chen system, and Kuramoto-Sivashinsky equation) and is shown to capture the systems’ intrinsic evolution patterns, such as the structure of the strange attractor. Comparative experiments with alternative data-driven modeling approaches illustrate that our proposed method can model a longer evolutionary process with less error, further demonstrating its effectiveness and superiority.

Distributionally robust optimization for collaborative emergency response network design

The post-disaster emergency response capacity of a single country is limited, and a collaborative approach that pools emergency resources can improve disaster resilience. In this paper, we study a multi-country collaborative emergency response network design problem with uncertain demand and transportation time. Considering the benefits of inter-regional cooperation in sharing emergency facilities and resources, we construct a collaborative emergency response network (CERN) design framework. The cost of CERN is allocated among the partner countries according to their expect standalone response cost and the level of economic development. In practice, the distribution information of random parameters is not perfectly known, so we propose a distributionally robust optimization (DRO) model to design the CERN. A scenario-wise ambiguity set is constructed to characterize the uncertain parameters based on disaster-level-related events. To solve the proposed DRO model, we propose a decomposition-based algorithm with a valid inequality. In the numerical study, we first verify the advantage of the CERN design approach. The proposed scenario-wise DRO method is subsequently compared with alternative modeling approaches to assess its out-of-sample performance. The findings confirm the efficacy of the constructed ambiguity set in capturing the uncertainty stemming from varying magnitudes of catastrophic events. The computational study demonstrates that the proposed algorithm exhibits superior computational efficiency compared to the commercial solver CPLEX for large-scale problems. Additionally, we conduct sensitivity analyses on various parameter configurations and provide managerial insights for the CERN design problem.

Unobserved heterogeneity and temporal instability in an analysis of household water consumption under block rate pricing

Abstract There is a lack of studies investigating household water consumption while considering the possible heterogeneity in the observed data and its temporal instability, often resulting in inconsistent and biased parameter estimations and, as a result, inaccurate forecasting of household water consumption. To address these constraints, the current study investigates temporal shifts in the household water consumption pattern and the effects of different socioeconomic factors on forecasting household water consumption. Using the results of a household survey performed seasonally in three major cities in Northern Jordan over a 4-year period, separate seasonal models of water consumption are estimated using three alternate modeling approaches to account for possible unobserved heterogeneity. Likelihood ratio tests were performed to investigate the temporal stability of the models' estimations over different seasons across the 4-year period. The findings of these tests indicated that the data are temporally stable over two datasets (the summer and winter seasons). Also, the findings revealed that household water consumption is influenced by a variety of factors, with the impact of many of these factors varying across observations. Finally, the findings highlight the need for additional research into how unobserved heterogeneity can be best modeled in temporal contexts for accurate water consumption forecasting.

A Multipath Error Reduction Method for BDS Using Tikhonov Regularization with Parameter Optimization

Multipath error is an important factor restricting the relative positioning accuracy of the Beidou Navigation Satellite System (BDS). Because of the complexity of the reflection environment, the mathematical modeling of multipath errors is quite difficult. The sidereal filtering algorithm corrects multipath errors by using the feature of period repetition, which can greatly reduce its influence and improve the accuracy of positioning and attitude measurement. In view of the constellation heterogeneity of BDS, it is more complicated to apply sideral filtering. Based on the reconstructed single-difference residual of the carrier phase, the multipath repetition time of the Beidou satellite is estimated using the idea of segmentation. The Tikhonov regularization method and the classical wavelet method are used to extract the multipath of the single-difference residual of the carrier phase, and the “clean” sequence of the single-difference residual is obtained. The experimental results show that it is feasible to extract the multipath error correctly by Tikhonov regularization, and the multipath error is smoother than the original residual measurement. Furthermore, the estimation method of the regularization parameter is further optimized. After using the optimized Tikhonov regularization method with sidereal filtering, the mean RMS improvements of GEO, IGSO, and MEO satellites are 45.9%, 38.2%, and 37.5%, respectively. The positioning accuracy on E, N, and U components is improved by 24.8%, 26.3%, and 42.7%, respectively. The attitude resolution accuracy is improved by 22.9% in the yaw angle and 12.6% in the pitch angle. The proposed method can be an alternative BDS multipath error modeling and mitigation approach.

Objective molecular dynamics investigation of dissociation and recombination kinetics in high-temperature nitrogen

In this study, we propose the use of the novel approach of objective molecular dynamics (OMD) simulating far-from-equilibrium gas dynamics problems with chemical reactions. The OMD method has an exact relation to models in continuum mechanics and can be used to improve those models. We provide a detailed molecular dynamics investigation of chemically reacting nitrogen gas in a space-homogeneous adiabatic reactor. The analysis is based on a first-principles derived reactive ReaxFF potential energy surface, which captures the relevant processes of rovibrational relaxation, dissociation, and exchange as well as recombination in a gas evolving under non-equilibrium conditions. We examine the evolution of the internal mode population distribution of all the molecules as well as the rovibrational probability distribution of the pre-collision dissociating and post-collision recombined N2 molecules to investigate the microscopic selectivity of various reactive processes. Subsequently, we make comparisons with results obtained by means of an alternative modeling approach called direct molecular simulation. The current work illustrates the application of the method of OMD to study the compression and expansion kinetics of dissociation-recombination nitrogen mixture relevant to normal shock wave and nozzle expansion.