Formulation Of Model Research Articles

On the functional regression model and its finite-dimensional approximations

AbstractThe problem of linearly predicting a scalar response Y from a functional (random) explanatory variable $$X=X(t),\ t\in I$$ X = X ( t ) , t ∈ I is considered. It is argued that the term “linearly” can be interpreted in several meaningful ways. Thus, one could interpret that (up to a random noise) Y could be expressed as a linear combination of a finite family of marginals $$X(t_i)$$ X ( t i ) of the process X, or a limit of a sequence of such linear combinations. This simple point of view (which has some precedents in the literature) leads to a formulation of the linear model in terms of the RKHS space generated by the covariance function of the process X(t). It turns out that such RKHS-based formulation includes the standard functional linear model, based on the inner product in the space $$L^2[0,1]$$ L 2 [ 0 , 1 ] , as a particular case. It includes as well all models in which Y is assumed to be (up to an additive noise) a linear combination of a finite number of linear projections of X. Some consistency results are proved which, in particular, lead to an asymptotic approximation of the predictions derived from the general (functional) linear model in terms of finite-dimensional models based on a finite family of marginals $$X(t_i)$$ X ( t i ) , for an increasing grid of points $$t_j$$ t j in I. We also include a discussion on the crucial notion of coefficient of determination (aimed at assessing the fit of the model) in this setting. A few experimental results are given.

A novel vortex-based velocity sampling method for the actuator-line modeling of floating offshore wind turbines in windmill state

The fluid-dynamic simulation of wind turbine aerodynamics is typically tackled by applying multi-fidelity computational tools. In this context, the so-called actuator line model combines a low-fidelity treatment of the rotor with a high-fidelity resolution of the wake. In this paper, a novel formulation of the actuator line model proposes a vortex-based method to sample the flow around the rotor to rigorously assign the forces imparted by the blades. This new technique is implemented into an in-house code developed within the OpenFOAM environment, and it is validated against wind-tunnel experiments on a laboratory-scale horizontal-axis wind turbine operated in fixed-bottom and floating conditions. The calculations are also compared against multi-fidelity simulations performed, on the same test case, in the frame of the OC6 Phase III project. The simulation results, obtained after a systematic analysis and selection of the model parameters, exhibit a remarkable agreement with the available experiments and place the present code in the proper ranking of fidelity levels, in-between momentum-balance methods and blade-resolved CFD models. Finally, the calculations for surge and pitch platform motions demonstrate the capability of the proposed technique to reliably predict the aerodynamics of turbine rotors in dynamic operation at affordable computational cost.

Compound Attitude Control Strategy for Reusable Launch Vehicle Based on Improved Particle Swarm Optimization Algorithm

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

Sensitivity analysis of hydraulic erosion and calibration of the erosion coefficient

Hydraulic erosion may pose a threat to the safety and sustainability of geo-related infrastructure, yet quantifying the intricate process of hydraulic erosion still poses a significant scientific and technical challenge. One important step in meeting this challenge is the formulation of a hydraulic erosion model with the erosion coefficient as a central controlling parameter. Calibration of the erosion coefficient (or rate) remains one of the main obstacles to improving predictive modelling, particularly in scenarios lacking long-term laboratory test data. In this study, sensitivity analysis of the key erosion indicators on the parameters controlling hydraulic erosion is conducted. A novel calibration method for the erosion coefficient is presented based on sensitivity analysis. After validating against simulation results and laboratory test findings, the proposed calibration method is applied to a hypothetical long-term hydraulic erosion case. The results show that the maximum hydraulic erosion time is sensitive to all considered parameters (erosion coefficient, initial fraction of fluidized solid particle, initial porosity and maximum porosity), while the erosion curve shape is only sensitive to the initial porosity and the maximum porosity. The validation by existing simulation results shows that the proposed calibration method is robust and internally consistent. The validation by experimental results indicates that the proposed calibration method also has high external validity. Finally, the proposed calibration method is applied to hypothetical long-term erosion in a grouted area. The results show that the hydraulic erosion effect in the grouted area becomes increasingly severe over time. This study contributes toward a more efficient calibration of the erosion coefficient, especially for scenarios in the absence of testing porosity evolution data. The research outcome provides a theoretical foundation for the safety assessment and sustainability analysis of geotechnical structures that are subject to hydraulic erosion.

Lagrangian Finite Element Model Formulation and Experimental Validation of the Laser Impact Weld Process for Ti/Brass Joining

Information on the flyer deformation during laser impact welding (LIW) is an important aspect to consider when high reliability of the welded components is required. For this reason, accurate numerical models simulating thermal and mechanical aspects are needed. In the present work, the cross-section morphology during LIW of Ti/Brass joints at varying laser pulse energies is modeled by a 2D finite element (FE) model. A hydrodynamic plasma pressure model able to describe the evolution of the pressure load step by step, taking into account the progressive deformation of the flyer, was implemented. Hence, this paper proposes an alternative method to the conventional node concentrated forces or predefined velocity as flyer boundary conditions. The levels of the equivalent plastic strain (PEEQ), shear stress, and critical flyer velocity at the collision point were used as reference parameters to predict the success of the welding bond, distinguishing the welded area from the unwelded area. The model was validated by comparison with the experimental data, which showed the effectiveness of the proposed FE code in predicting the cross-section morphology of the welded materials. Moreover, practical industrial information such as variation in the flyer impact velocity, collision angle, and process temperatures was predicted by varying the process laser pulse energy according to the basic principle of the process.

Challenges in applying water budget framework for estimating groundwater storage changes from GRACE observations

The application of a water budget framework to isolate Gravity Recovery and Climate Experiment (GRACE) groundwater storage anomalies (GRACE-GWA) from GRACE terrestrial water storage anomalies (GRACE-TWSA) is hindered by the lack of direct observations of water budget components. In GRACE groundwater studies, water budget components are frequently applied to isolate changes in a storage component from various auxiliary methods (e.g., land surface or hydrology models, reanalysis, or remote sensing) and are used as a supplement to, or a substitute for, in-situ measurements when direct observations are sparse or unavailable. The contribution of select auxiliary datasets to isolate GRACE-GWA from GRACE-TWSA is an enduring quandary, attributed to the various assumptions applied to resemble hydrologic processes in model formulations. This study systematically allocates water budget components from assorted auxiliary sources to demonstrate bias and distortion of GRACE-GWA resulting simply from water storage component data selection. Whereas previous GRACE-GWA studies used combinations of water budget component datasets with uncertainty derived from the variance of the combined water budget components, this study applies single estimates for each component to measure bias and variability. An initial comparative analysis focused on three basins with suitable in-situ groundwater observations and complex hydrology stores (e.g., large lakes and seasonal snow cover), with correlation coefficients ranging from 0.32 to 0.89. Our systematic analysis was extended to 12 additional basins that capture a range of hydrologic characteristics to highlight the inconsistency in GRACE-GWA estimates. The variability evident in GRACE-GWA estimates highlights that the selection of water budget components carries the risk of misleading outcomes when disaggregating GRACE-TWSA into GRACE-GWA. Our water budget intercomparison provides an important assessment of the limitation in extraction of GRACE-GWA, highlighting the usefulness of comprehensive appraisal of water budget components for GRACE groundwater studies.

Thick interface coupling technique for weakly dispersive models of waves

The primary focus of this work is the coupling of dispersive free-surface flow models through the utilization of a thick interface coupling technique. The initial step involves introducing a comprehensive framework applicable to various dispersive models, demonstrating that classical weakly dispersive models are encompassed within this framework. Next, a thick interface coupling technique, well-established in hyperbolic framework, is applied. This technique enables the formulation of unified models across different subdomains, each corresponding to a specific dispersive model. The unified model preserves the conservation of mechanical energy, provided it holds for each initial dispersive model. We propose a numerical scheme that preserve the projection structure at the discrete level and as a consequence is entropy-satisfying when the continuous model conserve the mechanical energy. We perform a deep numerical analysis of the waves reflected by the interface. Finally, we illustrate the usefulness of the method with two applications known to pose problems for dispersive models, namely the imposition of a time signal as a boundary condition or the imposition of a transparent boundary condition, and wave propagation over a discontinuous bathymetry.

Cellulose formulations for hair modelling

Chemical processes to change the hair shape can lead to damaged hair fibre. Here, we propose benign formulations based on different types of cellulose using imidazolium ionic liquids (ILs) and a commercial serum as solvents. Formulations with concentrations at 3% and 0.5% of HEC (Hydroxyethyl cellulose) and CMC (Carboxymethylcellulose) showed the best results after washing, around 10% of curling effect. Images from fluorescence microscopy enable to observe the coating layer formed on the surface of the hair fiber. ATR-FTIR corroborate the presence of functional groups of cellulose on the coated hair fiber. SEM images revealed that the treated hair fiber preserved it structural integrity, especially for the 3% CMC formulation. Those formulations also presented good mechanical wear determined by mass loss percentage. The novelty of this work is the methodology established associated with the cellulose formulation for hair modelling up to three washes.Graphical

Prey group defense and hunting cooperation among generalist-predators induce complex dynamics: a mathematical study.

Group defense in prey and hunting cooperation in predators are two important ecological phenomena and can occur concurrently. In this article, we consider cooperative hunting in generalist predators and group defense in prey under a mathematical framework to comprehend the enormous diversity the model could capture. To do so, we consider a modified Holling-Tanner model where we implement Holling type IV functional response to characterize grazing pattern of predators where prey species exhibit group defense. Additionally, we allow a modification in the attack rate of predators to quantify the hunting cooperation among them. The model admits three boundary equilibria and up to three coexistence equilibrium points. The geometry of the nontrivial prey and predator nullclines and thus the number of coexistence equilibria primarily depends on a specific threshold of the availability of alternative food for predators. We use linear stability analysis to determine the types of hyperbolic equilibrium points and characterize the non-hyperbolic equilibrium points through normal form and center manifold theory. Change in the model parameters leading to the occurrences of a series of local bifurcations from non-hyperbolic equilibrium points, namely, transcritical, saddle-node, Hopf, cusp and Bogdanov-Takens bifurcation; there are also occurrences of global bifurcations such as homoclinic bifurcation and saddle-node bifurcation of limit cycles. We observe two interesting closed 'bubble' form induced by global bifurcations due to change in the strength of hunting cooperation and the availability of alternative food for predators. A three dimensional bifurcation diagram, concerning the original system parameters, captures how the alternation in model formulation induces gradual changes in the bifurcation scenarios. Our model highlights the stabilizing effects of group or gregarious behaviour in both prey and predator, hence supporting the predator-herbivore regulation hypothesis. Additionally, our model highlights the occurrence of "saltatory equilibria" in ecological systems and capture the dynamics observed for lion-herbivore interactions.

A novel dynamic batching energy management strategy for multi-microgrid networks with enhanced privacy

This article presents a novel energy management approach termed Dynamic Batching EMS (DBEMS) designed specifically for day-ahead scheduling within multi-microgrid (MMG) networks. The prime aim of this strategy is to curtail operational costs within the MMG network, fortify the privacy of individual microgrids, and optimize the utilization of available resources, surpassing existing methodologies like centralized, decentralized, and nested management strategies. The emphasis is chiefly on orchestrating cost-effective operations while bolstering data privacy for each microgrid within the complex MMG framework. The efficacy of the proposed strategy is evaluated across both grid-connected and islanded modes, focusing on a network comprising four microgrids. The model formulation is executed utilizing the Mixed Integer Linear Programming method. In the islanded mode, optimization is conducted for scenarios both inclusive and exclusive of Battery Energy Storage Systems (BESS). Comparative assessments are made against centralized and nested strategies, considering parameters including operational costs, privacy enhancements, load-shedding, computation time, and computational burden.

Viscosity-model-independent generalized Reynolds number for laminar pipe flow of shear-thinning and viscoplastic fluids

Understanding the flow dynamics of non-Newtonian fluids is crucial in various engineering, industrial, and biomedical applications. However, the existing generalized Reynolds number formulations for non-Newtonian fluids have limited applicability due to their dependencies on their specific viscosity models. In this study, we propose a new viscosity-model-independent generalized Reynolds number formulation for laminar pipe flow. The proposed method is based on the direct adaptation of the measurement principles of rotational viscometers for wall shear rate estimation. We assess the accuracy of this proposed formulation for power-law and Carreau-Yasuda viscosity models through robust friction factor experiments. The experimental results demonstrate the applicability and effectiveness of the proposed viscosity-model-independent Reynolds number, as the measured friction factor data align closely with our Reynolds number predictions. Furthermore, we compare the accuracy of our Reynolds number formulation against established generalized Reynolds formulations for pure shear-thinning (Carreau-Yasuda) and viscoplastic (Herschel-Bulkley-extended) models. The results of the comparative analysis confirm the reliability and robustness of this generalized Reynolds number in characterizing and interpreting flow behavior in systems with visco-inelastic non-Newtonian fluids. This unified generalized Reynolds number formulation presents new and significant opportunities for precise pipe flow characterization and interpretation as it is applicable to any visco-inelastic (time-independent) viscosity model without requiring additional derivations.

Analysis of rainfall erosivity factor (R) on prediction of erosion yield using USLE and RUSLE Model’s; A case study in Mayang Watershed, Jember Regency, Indonesia

The Rainfall erosivity has a relatively high effect on soil erosion, in addition to being very difficult to predict and control. The Universal Soil Loss Equation (USLE) and The Revised Universal Soil Loss Equation (RUSLE) model are commonly used to predict erosion yield in Indonesia. However, these models have several erosivity formulations that give different results. In this sense, identifying the sensitivity of different erosivity formulations in both models above is important. The aim of this study is to analyze soil erosion yield prediction influenced by the difference in erosivity equation on the same rainfall data used in the models while other parameters used are the same. The monthly rainfall and annual rainfall data were tested using the erosivity formulas. The (1) Bols and (2) Utomo equations were tested using monthly rainfall data, while the (3) Bols and (4) Hurni equations were tested using annual rainfall data. The results show that the prediction of soil erosion yields estimates using monthly rainfall data in both models have no significant differences. On the other hand, soil erosion estimates using annual rainfall data in the models have significant differences, whereas the USLE model estimation results in 63% erosion yield on low classification (0-15 ton ha<sup>-1 </sup>year<sup>-1</sup>). Meanwhile, the RUSLE model estimates only 59% erosion yield on low classifications. Another result is that the USLE model estimates lower erosion yield than the RUSLE model when the models use annual rainfall data, which may give significantly different recommendations for soil conservation in Indonesia, especially in reducing erosion yield at the Watershed level.

External Factors and Endogenous Elements Fostering Critical Thinking of Tertiary EFL Learners in China: A Grounded Theory Study

The cultivation of critical thinking (CT) skills is a fundamental aim within the realm of higher education. It assumes particular significance within the domain of Teaching English as a Foreign Language (TEFL). This study directs its focus on the Chinese tertiary TEFL context and endeavours to explicate what the external factors are, as well as endogenous elements and ways to foster students’ critical thinking. Methodologically, the study adopted grounded theory (GT) and drew upon qualitative data gathered through interviews with 36 participants, including both students and instructors recruited from prestigious universities, mid-tier colleges, and less developed college across China. The data analysis, utilizing NVivo12 software, encompasses a multilevel coding process, including open, axial, and selective coding, in accordance with the principles of GT. The outcome of this meticulous analysis results in the formulation of a comprehensive model revealing the interplay among external influences, endogenous elements, and ways to foster the development of CT skills among tertiary English as a Foreign Language (EFL) students. The implications of these findings extend pedagogically to multiple stakeholders within the tertiary TEFL context.

Early warning systems for financial distress: A machine learning approach to corporate risk mitigation

This research explores the development of an early warning system for corporate financial distress using machine learning techniques to address key challenges in corporate risk mitigation. The main objective is to enhance predictive accuracy by integrating financial and non-financial data, addressing class imbalance, and ensuring model interpretability. The research design involves the formulation of a new machine learning model, leveraging cost-sensitive learning and feature selection, and is tested with a numerical example using logistic regression. Methodologically, the study adopts a data-driven approach that incorporates diverse financial ratios, macroeconomic variables, and market sentiment indicators to predict corporate distress. The numerical results from a basic logistic regression model demonstrate poor performance, especially in handling class imbalance, revealing limitations in traditional statistical models. However, the research suggests that machine learning methods, particularly ensemble learning with cost-sensitive algorithms, offer superior predictive accuracy and practical applicability. The study concludes that integrating advanced techniques and diverse datasets leads to more reliable early warning systems, with significant implications for corporate governance and financial risk management. Future research should explore more sophisticated machine learning models and extend real-world applications across various industries and economic conditions.

Design and Simulation of Torque Control for Hot Rolling Steel Mill

Lateral movement of a strip in the hot rolling process is an important unsolved technical problem. To minimize the strip thickness variations, the state space model formulation is usually used. It results in reducing productivity and, in the extreme case, strip tearing during tailing out at the finishing mill. This movement is caused by various asymmetric factors such as mechanical asymmetry of the mill and different temperatures along the strip width direction. Furthermore, the difference in the temperature and the thickness of steel strip resulting in deformation of the steel, which affects the bending torque of the steel strip. This study aims to understand the influences of these various factors on lateral movement. In order to reduce the lateral movement and the disturbance terms, that act on uncertain inter-connected systems, a new control approach, based on a combination of Integral Sliding Mode Control (ISMC) and Linear Matrix Inequalities (LMI) technique, is proposed. Simulation results of hot rolling system with three interconnected systems are presented to validate the new controller performance.

Modeling and contribution of flexible heating systems for transmission grid congestion management

The large-scale integration of flexible heating systems in the European electricity market leads to a substantial increase of transportation requirements and consecutively grid congestions in the continental transmission grid. Novel model formulations for the grid-aware operation of both individual small-scale heat pumps and large-scale power-to-heat (PtH) units located in district heating networks are presented. The functionality of the models and the contribution of flexible heating systems for transmission grid congestion management is evaluated by running simulations for the target year 2035 for the German transmission grid. The findings show a decrease in annual conventional redispatch volumes and renewable energy sources (RES) curtailment resulting in cost savings of approximately 5% through the integration of flexible heating systems in the grid congestion management scheme. The analysis suggests that especially large-scale PtH units in combination with thermal energy storages can contribute significantly to the alleviation of grid congestion and foster RES integration.

The value of generalized linear mixed models for data analysis in the plant sciences

Modern data analysis typically involves the fitting of a statistical model to data, which includes estimating the model parameters and their precision (standard errors) and testing hypotheses based on the parameter estimates. Linear mixed models (LMMs) fitted through likelihood methods have been the foundation for data analysis for well over a quarter of a century. These models allow the researcher to simultaneously consider fixed (e.g., treatment) and random (e.g., block and location) effects on the response variables and account for the correlation of observations, when it is assumed that the response variable has a normal distribution. Analysis of variance (ANOVA), which was developed about a century ago, can be considered a special case of the use of an LMM. A wide diversity of experimental and treatment designs, as well as correlations of the response variable, can be handled using these types of models. Many response variables are not normally distributed, of course, such as discrete variables that may or may not be expressed as a percentage (e.g., counts of insects or diseased plants) and continuous variables with asymmetrical distributions (e.g., survival time). As expansions of LMMs, generalized linear mixed models (GLMMs) can be used to analyze the data arising from several non-normal statistical distributions, including the discrete binomial, Poisson, and negative binomial, as well as the continuous gamma and beta. A GLMM allows the data analyst to better match the model to the data rather than to force the data to match a specific model. The increase in computer memory and processing speed, together with the development of user-friendly software and the progress in statistical theory and methodology, has made it practical for non-statisticians to use GLMMs since the late 2000s. The switch from LMMs to GLMMs is deceptive, however, as there are several major issues that must be thought about or judged when using a GLMM, which are mostly resolved for routine analyses with LMMs. These include the consideration of conditional versus marginal distributions and means, overdispersion (for discrete data), the model-fitting method [e.g., maximum likelihood (integral approximation), restricted pseudo-likelihood, and quasi-likelihood], and the choice of link function to relate the mean to the fixed and random effects. The issues are explained conceptually with different model formulations and subsequently with an example involving the percentage of diseased plants in a field study with wheat, as well as with simulated data, starting with a LMM and transitioning to a GLMM. A brief synopsis of the published GLMM-based analyses in the plant agricultural literature is presented to give readers a sense of the range of applications of this approach to data analysis.

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution.

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Stochastic Lagrangian wall deposition model for RANS prediction of deposition in turbulent gas–solid flows

In industrial-scale Reynolds Averaged Navier–Stokes (RANS) simulations, a wall-modeled approach is often employed, specifically choosing a dimensionless wall-cell height much greater than one. However, in gas–solid flows, a coarse description of the boundary layer may lead to inaccurate results in terms of particle dispersion and deposition. In this study, we conducted an investigation on stochastic RANS dispersion models with deposition on smooth walls using a wall-modeled approach. These models were applied to turbulent channel flows in both vertical and horizontal orientations. The modeling approach involved the adoption of the Euler–Lagrange formalism and the RANS k−ϵ turbulence model in OpenFOAM®. To assess particle behavior first, a simple homogeneous isotropic stationary turbulence (HIST) case was examined. Fluid elements were also tracked in an inhomogeneous turbulence for dispersion analysis. These tests gave the good formulation for the dispersion model to be used for particle tracking coupled with turbulent channel flow simulation. However, the deposition results in turbulent channel using such stochastic dispersion model in the wall-cell were found to be consistently overestimated across various scenarios for low-inertia particles. To address these limitations arising from the coarse description of the boundary layer, we examined, implemented, and evaluated a Lagrangian stochastic wall deposition model. The activated deposition model demonstrated good agreement with experimental data.

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation.

Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care. An examination of EHR interactions using EHR audit logs could provide a scalable, unobtrusive way to quantify the nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex; however, simple analytical methods cannot discover complex temporal patterns, requiring use of state-of-the-art temporal data-mining approaches. To effectively use these approaches, it is necessary to structure the raw audit logs into a consistent and scalable logical data model that can be consumed by machine learning (ML) algorithms. We aimed to conceptualize a logical data model for nurse-EHR interactions that would support the future development of temporal ML models based on EHR audit log data. We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from the literature and our previous experience studying temporal patterns in biomedical data, we formulated a logical data model that can describe nurse-EHR interactions, the nurse-intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to the nursing workload in a scalable and extensible manner. We describe the data structure and concepts from EHR audit log data associated with nursing workload as a logical data model named RNteract. We conceptually demonstrate how using this logical data model could support temporal unsupervised ML and state-of-the-art artificial intelligence (AI) methods for predictive modeling. The RNteract logical data model appears capable of supporting a variety of AI-based systems and should be generalizable to any type of EHR system or health care setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support the nursing documentation workload and address nurse burnout.