Framework Of Model Research Articles

The spatial planning of housing and urban green space: A combined stated choice experiment and land-use modeling approach

Urban green space (UGS) is receiving increasing attention as a means to make cities better adapted to climate change and to create high-quality living environments for citizens. The spatial planning of UGS generally asks a trade-off between optimizing land-use and accessibility characteristics which may differ between housing types. In this paper, we use a stated choice experiment to estimate residential location preferences related to neighborhood land-use, UGS and accessibility characteristics in the framework of a land-use model. A national sample of 394 Dutch homeowners participated in the experiments and mixed logit models were estimated by housing type distinguishing detached, row houses and apartments. The estimation results show that trade-offs made between a green neighborhood and accessibility to urban amenities tend to differ between the housing types. The estimates are used as parameters in a housing land-use allocation model. An application of the model to a residential area development problem shows that creating green buffers along road infrastructure is the most beneficial way of allocating UGS considering the housing value for residents. The application further shows that apartments benefit more strongly from UGS in the neighborhood than from high accessibility of urban amenities compared to detached and row houses. Finally, we find that the optimal spatial allocation of UGS depends on whether maximizing housing market-value or residents’ utility is the prime objective.

Dynamical study of D*DK and D*DD¯ systems at quark level

Inspired by the recent report from the LHCb Collaboration on Tcc, which can be interpreted as a molecular DD*, we investigated two trimeson systems of the Tcc partner with IJP=01− in the framework of a chiral quark model. In the first case, because of the attraction between D* and D¯, we explored the existence of bound states in the system D*DD¯, which is obtained by adding D¯ into the molecular bound state Tcc (DD*). Similarly, in the second case, we explored whether there is a bound state in the D*DK system, which is obtained by adding K into the Tcc, given the attraction between D and K. The results show that both of them are bound states, in which the binding energy of the molecular state DD*K is relatively small, only 0.4±0.4 MeV, while the binding energy of DD*D¯ is up to 1.6±0.3 MeV. According to the calculation results of the root-square-mean distances, the spatial structure of the two systems shows the obvious (DD*)−(D¯/K) structure, in which D is close to D* while DD* as a whole is relatively distant from the third hadron (D¯/K), which are similar to the nucleon-electron structure. As a result, we strongly recommend that these bound states DD*D¯ and DD*K are searched for experimentally. Published by the American Physical Society 2024

Physics‐Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow

AbstractWe propose a physics‐informed convolutional decoder (PICD) framework for inverse modeling of heterogenous groundwater flow. PICD stands out as a direct inversion method, eliminating the need for repeated forward model simulations. The framework combines data‐driven and physics‐driven approaches by integrating monitoring data and domain knowledge into the inversion process. PICD utilizes a convolutional decoder to effectively approximate the spatial distribution of hydraulic heads, while Karhunen–Loève expansion (KLE) is employed to parameterize hydraulic conductivities. During the training process, the stochastic vector in KLE and the parameters of the convolutional decoder are adjusted simultaneously to minimize the data‐mismatch and the physical violation. The final optimized stochastic vectors correspond to the estimation of hydraulic conductivities, and the trained convolutional decoder can predict the evolution and distribution of hydraulic heads. Various scenarios of groundwater flow are examined and results demonstrate the framework's capability to accurately estimate heterogeneous hydraulic conductivities and to deliver satisfactory predictions of hydraulic heads, even with sparse measurements.

Polymorphism-aware models in RevBayes: Species trees, disentangling Balancing Selection and CG-biased gene conversion.

The role of balancing selection is a long-standing evolutionary puzzle. Balancing selection is a crucial evolutionary process that maintains genetic variation (polymorphism) over extended periods of time; however, detecting it poses a significant challenge. Building upon the polymorphism-aware phylogenetic models (PoMos) framework rooted in the Moran model, we introduce PoMoBalance model. This novel approach is designed to disentangle the interplay of mutation, genetic drift, directional selection (GC-biased gene conversion), along with the previously unexplored balancing selection pressures on ultra-long timescales comparable with species divergence times by analysing multi-individual genomic and phylogenetic divergence data. Implemented in the open-source RevBayes Bayesian framework, PoMoBalance offers a versatile tool for inferring phylogenetic trees as well as quantifying various selective pressures. The novel aspect of our approach in studying balancing selection lies in PoMos' ability to account for ancestral polymorphisms and incorporate parameters that measure frequency-dependent selection, allowing us to determine the strength of the effect and exact frequencies under selection. We implemented validation tests and assessed the model on the data simulated with SLiM and a custom Moran model simulator. Real sequence analysis of Drosophila populations reveals insights into the evolutionary dynamics of regions subject to frequency-dependent balancing selection, particularly in the context of sex-limited colour dimorphism in Drosophila erecta.

Qualitative evaluation of a molecular point-of-care testing study for influenza in UK primary care.

Influenza contributes to the surge in winter infections and the consequent winter pressures on the health service. Molecular point-of-care testing(POCT) for influenza might improve patient management by providing rapid and accurate clinical diagnosis to inform the timely initiation of antiviral therapy and reduce unnecessary admissions and antibiotics use. To explore factors that influence the adoption or non-adoption of POCT in English general practices and provide insights to enable its integration into routine practice workflows. A qualitative implementation evaluation was conducted in ten general practices within the English national sentinel network (Oxford-RCGP Research and Surveillance Centre), from April to July 2023. Using the nonadoption, abandonment, scale-up, spread, and sustainability framework, data collection and analysis were conducted across ten practices. We made ethnographic observations of the POCT workflow and surveyed the practice staff for their perspectives on POCT implementation. Data were analysed using a mix of descriptive statistics, graphical modelling techniques and framework approach. Ethnographic observations identified two modes of POCT integration into practice workflow: 1) clinician POCT workflow - typically involving batch testing due to time constraints, 2) research nurse/healthcare assistant POCT workflow - characterised by immediate testing of individual patients. Survey indicated that most primary care staff considered the POCT training offered was sufficient, and these practices were ready for change and had the capacity and resources to integrate POCT in workflows. General practices should demonstrate flexibility in the workflow and workforce they deploy to integrate POCT into routine clinical workflow.

Understanding key mineral supply chain dynamics using economics‐informed material flow analysis and Bayesian optimization

AbstractThe low‐carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics‐informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics‐informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector‐specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold‐Gold JIE data openness badge described at http://jie.click/badges.

Effect of Background Scattering on Efimov Scenario for Overlapping Narrow Feshbach Resonances

Efimov physics in the vicinity of two overlapping narrow Feshbach resonances can be explored within a framework of a three-channel model where a non-interacting open channel is coupled to two closed molecular channels. Here, we determine how it compares to the extended two-channel model, which includes an open channel with finite background scattering and a single molecular channel. We identify the parameter range in which the three-channel model surpasses the extended two-channel model. Furthermore, the three-channel model is extended to include background scattering, and then both models are applied to the experimentally relevant system of bosonic lithium atoms polarized on two different energy levels, with an isolated and two overlapping narrow Feshbach resonances, respectively. We confirm, in agreement with previous studies, that being small, the background scattering length in lithium has a negligible effect on the Efimov features in the case of isolated resonance. However, in the case of overlapping Feshbach resonances, the inclusion of background scattering improves the performance of the theory with respect to the experimentally measured position of the Efimov resonance.

Integrated framework for geological modeling: integration of data, knowledge, and methods

Integrated framework for geological modeling: integration of data, knowledge, and methods

Progressive transfer learning for advancing machine learning-based reduced-order modeling

To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

Modeling of glubam roof truss, parameter identification and updating based on parallel genetic algorithm

This research introduces an innovative approach to the design and simulation of bio-based laminated structures, specifically focusing on glue-laminated bamboo (glubam) used in roof trusses. Our study fills a critical gap by investigating the mechanical behaviors of bolted connections in bamboo-based structures, which have not been comprehensively studied before. We employ a dual-phase methodology: initially, cyclic tests on bolted steel to glubam joints assess their hysteretic behavior, followed by tests on glubam planar roof trusses to evaluate structural responses under practical conditions. Our novel contribution is the development of a simplified mechanical-based hysteretic model, incorporating connector and spring elements in series or parallel within the ABAQUS software. This model significantly improves on existing models by allowing for initial calibration through a parallel genetic algorithm (PGA), enhancing both accuracy and efficiency. Subsequent incorporation of this model into the simulation of truss joints enabled the creation of an advanced hybrid roof truss model within ABAQUS. The final stage of our research demonstrates the application of a PGA-based model-updating framework, which substantially increases the model’s predictive accuracy. This work not only advances the understanding of structural behavior in bio-based construction materials but also introduces a robust framework for model updating that can be applied to other engineering simulations, contributing to more sustainable and resource-efficient construction practices.

A quantitative modeling framework to understand the physiology of the hypothalamic-pituitary-adrenal axis and interaction with cortisol replacement therapy.

Congenital adrenal hyperplasia (CAH) is characterized by impaired adrenal cortisol production. Hydrocortisone (synthetic cortisol) is the drug-of-choice for cortisol replacement therapy, aiming to mimic physiological cortisol circadian rhythm. The hypothalamic-pituitary-adrenal (HPA) axis controls cortisol production through the pituitary adrenocorticotropic hormone (ACTH) and feedback mechanisms. The aim of this study was to quantify key mechanisms involved in the HPA axis activity regulation and their interaction with hydrocortisone therapy. Data from 30 healthy volunteers was leveraged: Endogenous ACTH and cortisol concentrations without any intervention as well as cortisol concentrations measured after dexamethasone suppression and single dose administration of (i) 0.5-10mg hydrocortisone as granules, (ii) 20mg hydrocortisone as granules and intravenous bolus. A stepwise model development workflow was used: A newly developed model for endogenous ACTH and cortisol was merged with a refined hydrocortisone pharmacokinetic model. The joint model was used to simulate ACTH and cortisol trajectories in CAH patients with varying degrees of enzyme deficiency, with or without hydrocortisone administration, and healthy individuals. Time-dependent ACTH-driven endogenous cortisol production and cortisol-mediated feedback inhibition of ACTH secretion processes were quantified and implemented in the model. Comparison of simulated ACTH and cortisol trajectories between CAH patients and healthy individuals showed the importance of administering hydrocortisone before morning ACTH secretion peak time to suppress ACTH overproduction observed in untreated CAH patients. The developed framework allowed to gain insights on the physiological mechanisms of the HPA axis regulation, its perturbations in CAH and interaction with hydrocortisone administration, paving the way towards cortisol replacement therapy optimization.

A coupled modeling framework for investigating flow maldistribution in cross-flow-corrugated plate heat exchangers

Accurate prediction of the impact of flow maldistribution on the thermo-hydraulic behavior of plate heat exchangers is important for thermal design and optimization in a number of practical applications. In this paper, a coupled modeling framework, consisting of a heat transfer model and a hydraulic model, is proposed for quantitatively evaluating the impact of maldistribution on the thermo-hydraulic performance of a cross-flow-corrugated plate heat exchanger. The numerical results of the proposed approach are validated by comparison with experimental data. Using the coupled model, the flow distribution nonuniformity, heat exchanger effectiveness deteriorations, and pressure drop deviations are analyzed. Furthermore, the combined effects of side stream maldistribution are investigated for different inlet Reynolds numbers and inlet temperatures. The results demonstrate that when the inlet gas Reynolds number is in the range of 1100–2700, nonuniform distribution of the gas side will cause air maldistribution, and a 10%–30% effectiveness deterioration rate. It is found that air maldistribution will not result in gas maldistribution when the inlet air Reynolds number is in the range of 6000–18 000, but increasing air nonuniformity. Will lead to increasing deterioration in heat exchanger effectiveness. In the presence of air and gas flow maldistribution, the pressure drops on the gas and air sides increase by 5% and less than 12.4%, respectively. This work provides a computational framework for optimization of cross-flow-corrugated plate heat exchangers to achieve desired thermo-hydraulic performance.

Fast mvSLOUCH: Multivariate Ornstein–Uhlenbeck‐based models of trait evolution on large phylogenies

Abstract The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein–Uhlenbeck processes. The results of our analysis demonstrate the ability of the modelling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.

The dynamics of evolutionary branching in an ecological model

AbstractEco-evolutionary modelling involves the coupling of ecological equations to evolutionary ones. The interaction between ecological dynamics and evolutionary processes is essential to simulating evolutionary branching, a precursor to speciation. The creation and maintenance of biodiversity in models depends upon their ability to capture the dynamics of evolutionary branching. Understanding these systems requires low-dimension models that are amenable to analysis. The rapid reproduction rates of marine plankton ecosystems and their importance in determining the fluxes of climatically important gases between the ocean and atmosphere suggest that the next generation of global climate models needs to incorporate eco-evolutionary models in the ocean. This requires simple population-level models, that can represent such eco-evolutionary processes with orders of magnitude fewer equations than models that follow the dynamics of individual phenotypes. We present a general framework for developing eco-evolutionary models and consider its general properties. This framework defines a fitness function and assumes a beta distribution of phenotype abundances within each population. It simulates the change in total population size, the mean trait value, and the trait differentiation, from which the variance of trait values in the population may be calculated. We test the efficacy of the eco-evolutionary modelling framework by comparing the dynamics of evolutionary branching in a six-equation eco-evolutionary model that has evolutionary branching, with that of an equivalent one-hundred equation model that simulates the dynamics of every phenotype in the population. The latter model does not involve a population fitness function, nor does it assume a distribution of phenotype abundance across trait values. The eco-evolutionary population model and the phenotype model produce similar evolutionary branching, both qualitatively and quantitatively, in both symmetric and asymmetric fitness landscapes. In order to better understand the six-equation model, we develop a heuristic three-equation eco-evolutionary model. We use the density-independent mortality parameter as a convenient bifurcation parameter, so that differences in evolutionary branching dynamics in symmetric and asymmetric fitness landscapes may be investigated. This model shows that evolutionary branching of a stable population is flagged by a zero in the local trait curvature; the trait curvature then changes sign from negative to positive and back to negative, along the solution. It suggests that evolutionary branching points may be generated differently, with different dynamical properties, depending upon, in this case, the symmetry of the system. It also suggests that a changing environment, that may change attributes such as mortality, could have profound effects on an ecosystem’s ability to adapt. Our results suggest that the properties of the three-dimensional model can provide useful insights into the properties of the higher-dimension models. In particular, the bifurcation properties of the simple model predict the processes by which the more complicated models produce evolutionary branching points. The corresponding bifurcation properties of the phenotype and population models, evident in the dynamics of the phenotype distributions they predict, suggest that our eco-evolutionary modelling framework captures the essential properties that underlie the evolution of phenotypes in populations.

Extending the historical in situ gauge network over Indian river reaches utilizing the Surface Water and Ocean Topography (SWOT) mission

ABSTRACT We present a preliminary study on using the Surface Water and Ocean Topography (SWOT) mission to extend water surface elevation (WSE) measurements from a sparsely distributed in situ gauge network along a one-dimensional (1D) study reach within the Mahanadi River basin in India. We use the Centre National d’Etudes Spatiales (CNES) simulator to simulate 3 years of synthetic measurements of SWOT WSE, which are assimilated into a 1D stochastic model based on six independent configurations. For each configuration, the model is derived using historical measurements of in situ WSE to simulate daily WSE estimates every 200 m along the study reach. Assimilated outputs showed high Nash-Sutcliffe efficiency (0.697–0.811), high correlation (0.838–0.901) and low root mean square error (0.242–0.513 m) with respect to in situ validation data. Results indicate consistent performance of the SWOT-assimilated model framework and are relevant considering the declining availability of in situ monitoring stations.

Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo

Important policy questions during infections disease outbreaks include: i) How effective are particular interventions?; ii) When can resource-intensive interventions be removed? We used mathematical modelling to address these questions during the 2017 Ebola outbreak in Likati Health Zone, Democratic Republic of the Congo (DRC). Eight cases occurred before 15 May 2017, when the Ebola Response Team (ERT; co-ordinated by the World Health Organisation and DRC Ministry of Health) was deployed to reduce transmission. We used a branching process model to estimate that, pre-ERT arrival, the reproduction number was R=1.49\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R=1.49$$\\end{document} (95% credible interval (0.67, 2.81)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({{{{\\mathrm{0.67,2.81}}}}})$$\\end{document}). The risk of further cases occurring without the ERT was estimated to be 0.97 (97%). However, no cases materialised, suggesting that the ERT’s measures were effective. We also estimated the risk of withdrawing the ERT in real-time. By the actual ERT withdrawal date (2 July 2017), the risk of future cases without the ERT was only 0.01, indicating that the ERT withdrawal decision was safe. We evaluated the sensitivity of our results to the estimated R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R$$\\end{document} value and considered different criteria for determining the ERT withdrawal date. This research provides an extensible modelling framework that can be used to guide decisions about when to relax interventions during future outbreaks.

Predicting the Liquid Steel End-Point Temperature during the Vacuum Tank Degassing Process Using Machine Learning Modeling

The present work focuses on predicting the steel melt temperature following the vacuum treatment step in a vacuum tank degasser (VTD). The primary objective is to establish a comprehensive methodology for developing and validating machine learning (ML) models within this context. Another objective is to evaluate the model by analyzing the alignment of the SHAP values with metallurgical domain expectations, thereby validating the model’s predictions from a metallurgical perspective. The proposed methodology employs a Random Forest model, incorporating a grid search with domain-informed variables grouped into batches, and a robust model-selection criterion that ensures optimal predictive performance, while keeping the model as simple and stable as possible. Furthermore, the Shapley Additive Explanations (SHAP) algorithm is employed to interpret the model’s predictions. The selected model achieved a mean adjusted R2 of 0.631 and a hit ratio of 75.3% for a prediction error within ±5∘C. Despite the moderate predictive performance, SHAP highlighted several aspects consistent with metallurgical domain expertise, emphasizing the importance of domain knowledge in interpreting ML models. Improving data quality and refining the model framework could enhance predictive performance.

Parameter-Fitting-Free Continuum Modeling of Electric Double Layer in Aqueous Electrolyte.

Electric double layers (EDLs) play fundamental roles in various electrochemical processes. Despite the extensive history of EDL modeling, there remain challenges in the accurate prediction of its structure without expensive computation. Herein, we propose a predictive multiscale continuum model of EDL that eliminates the need for parameter fitting. This model computes the distribution of the electrostatic potential, electron density, and species' concentrations by taking the extremum of the total grand potential of the system. The grand potential includes the microscopic interactions that are newly introduced in this work: polarization of solvation shells, electrostatic interaction in parallel plane toward the electrode, and ion-size-dependent entropy. The parameters that identify the electrode and electrolyte materials are obtained from independent experiments in the literature. The model reproduces the trends in the experimental differential capacitance with multiple electrode and nonadsorbing electrolyte materials (Ag(110) in NaF, Ag(110) in NaClO4, and Hg in NaF), which verifies the accuracy and predictiveness of the model and rationalizes the observed values to be due to changes in electron stability. However, our calculation on Pt(111) in KClO4 suggests the need for the incorporation of electrode/ion-specific interactions. Sensitivity analyses confirmed that effective ion radius, ion valence, the electrode's Wigner-Seitz radius, and the bulk modulus of the electrode are significant material properties that control the EDL structure. Overall, the model framework and findings provide insights into EDL structures and predictive capability at low computational cost.

The impact of grazing on biodiversity and forest succession in the Brazilian dry forest region is constrained by non-equilibrium dynamics

The impacts of grazing on rangelands have historically been studied within the framework of the equilibrium model, which predicts significant impacts of grazing on ecosystems. However, in recent decades, studies have observed a non-equilibrium pattern, suggesting that abiotic factors play a primary role compared to grazing. These studies are primarily focused on rangelands, despite animal husbandry occurring in other biomes, such as seasonally dry tropical forests. Our study examines the influence of goat grazing on biodiversity and forest succession in the Brazilian dry forest (Caatinga). Considering its high interannual precipitation variability, we hypothesize a response that aligns with the non-equilibrium paradigm. We established a gradient of grazing intensity and history in areas at different stages of vegetation succession. A survey of tree - shrub and herbaceous species was conducted at each site and the biomass of both strata was quantified. Linear mixed models and Permanova were employed to assess differences in richness, composition, structure, and biomass among the areas. Our results suggest that grazing (history and intensity) and forest fallow age did not affect species richness, but only species composition. Low and high grazing intensity drive ecosystems toward similar compositions, which align with the non-equilibrium model predictions. Biomass in the herbaceous layer remained unaffected by grazing history, intensity, or forest fallow age, whereas woody biomass was influenced by grazing intensity in older forest fallows. Although trees in low-intensity grazing sites were significantly taller compared to those in other levels, overall, grazing did not disrupt the natural succession process. Older forest fallows exhibited greater diversity and higher basal area compared to new forest fallows, irrespective of grazing intensity. Our findings suggest that: a) grazing has minimal effects on biodiversity and biomass due to non-equilibrium dynamics, and b) with appropriate management, grazing can coexist with the conservation of the Caatinga.

What drives the adoption of digital technology in the micro, small, and medium enterprises sector in a developing economy?

Purpose This study aims to explore the determinants of digital technology adoption within micro, small and medium enterprises (MSMEs) operating in a developing economy. Grounded in the theoretical frameworks of fit-viability and task-technology fit models, this study investigates the impact of environmental fit, task-technology fit and viability on the intention to adopt digital technologies among MSMEs. Design/methodology/approach This study validates the theoretical model using structural equation modeling, using data collected from 280 experienced respondents employed in the MSMEs in India. Findings The results indicate that both functional and symbolic benefits positively impact managers’ intention to adopt digital technology. However, subgroup analysis reveals that in the case of service enterprises, only functional benefits have a positive influence on managers’ intention to adopt digital technology. In addition, the findings underscore the crucial role of viability in shaping the intention to adopt digital technologies among MSMEs. This study highlights how functional and symbolic benefits motivate digital technology adoption in MSMEs. Originality/value There is a dearth of empirical studies investigating the factors influencing the adoption of digital technology by MSME firms, especially within the context of developing economies and specifically within the MSME domain. This study contributes to the theoretical discussion surrounding digital technology adoption among MSMEs in India. Through empirical research, it expands on the fit-viability model and formulates a technology adoption model within the MSME context.