An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world.Plant height (H),stem diameter (SD),leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production.However,the combined effect of temperature and light on maize growth is rarely considered in crop growth models.Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H,SD,LAI and DM of maize under different mulching practices based on experimental data from 2015–2018.Either the accumulative growing degree-days (AGDD),helio thermal units (HTU),photothermal units (PTU) or photoperiod thermal units (PPTU,first proposed here) was used as a single driving factor in the models;or AGDD was combined with either accumulative actual solar hours (ASS),accumulative photoperiod response (APR,first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models.The model performances were evaluated using seven statistical indicators and a global performance index.The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching.Among the four single factor-driven models,the overall performance of the Mlog_(PTU)Model was the best,followed by the Mlog_(AGDD)Model.The Mlog_(PPTU)Model was better than the Mlog_(AGDD)Model in simulating SD and LAI.Among the 10 models,the overall performance of the Mlog_(AGDD–APR)Model was the best,followed by the Mlog_(AGDD–ASS)Model.Specifically,the Mlog_(AGDD–APR)Model performed the best in simulating H and LAI,while the Mlog_(AGDD–ADL)and Mlog_(AGDD–ASS)models performed the best in simulating SD and DM,respectively.In conclusion,the modified logistic growth equations with AGDD and either APR,ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.

A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

Hydrodynamic Assessment of A New Nature-Based Armour Unit on Rubble Mound Breakwater for Coastal Protection

This research proposes a novel nature-based design of a new concrete armour unit for the cover layer of a rubblemoundbreakwater. Armour units are versatile with respect to shape, orientation, surface condition details, and porosity.Therefore, a detailed analysis is required to investigate the exact state of their hydraulic interactions and structuralresponses. In this regard, the performance results of several traditional armour units, including the Antifer cube,Tetrapod, X-block and natural stone, are considered for the first step of this study. Then, the related observed resultsare compared with those obtained for a newly designed (artificial coral) armour unit. The research methodology utilizesthe common wave flume test procedure. Furthermore, several verified numerical models in OpenFOAM code areused to gain the extra required data. The proposed armour is configured to provide an effective shore protection as anenvironmental-friendly coastal structure. Thus it is designed with a main trunk including deep grooves to imitate thetypical geometry of a coral type configuration, so as to attain desirable performance. The observed results and ananalytic hierarchy process (AHP) concept are used to compare the hydraulic performance of the studied traditionaland newly proposed (artificial coral) armour units. The results indicate that the artificial coral armour unit demonstratesacceptable performance. The widely used traditional armour units might be replaced by newer designs for betterwave energy dissipation, and more importantly, for fewer adverse effects on the marine environment.

Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis

BACKGROUND Liver cirrhosis patients admitted to intensive care unit(ICU)have a high mortality rate.AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.METHODS We extracted demographic,etiological,vital sign,laboratory test,comorbidity,complication,treatment,and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV(MIMIC-IV)and electronic ICU(eICU)collaborative research database(eICU-CRD).Predictor selection and model building were based on the MIMIC-IV dataset.The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors.The final predictors were included in the multivariate logistic regression model,which was used to construct a nomogram.Finally,we conducted external validation using the eICU-CRD.The area under the receiver operating characteristic curve(AUC),decision curve,and calibration curve were used to assess the efficacy of the models.RESULTS Risk factors,including the mean respiratory rate,mean systolic blood pressure,mean heart rate,white blood cells,international normalized ratio,total bilirubin,age,invasive ventilation,vasopressor use,maximum stage of acute kidney injury,and sequential organ failure assessment score,were included in the multivariate logistic regression.The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases,respectively.The calibration curve also confirmed the predictive ability of the model,while the decision curve confirmed its clinical value.CONCLUSION The nomogram has high accuracy in predicting in-hospital mortality.Improving the included predictors may help improve the prognosis of patients.

Comparison of Two Recurrent Neural Networks for Rainfall-Runoff Modeling in the Zou River Basin at Atchérigbé (Bénin)

Hydrological models are developed to simulate river flows over a watershed for many practical applications in the field of water resource management. The present paper compares the performance of two recurrent neural networks for rainfall-runoff modeling in the Zou River basin at Atchérigbé outlet. To this end, we used daily precipitation data over the period 1988-2010 as input of the models, such as the Long Short-Term Memory (LSTM) and Recurrent Gate Networks (GRU) to simulate river discharge in the study area. The investigated models give good results in calibration (R2 = 0.888, NSE = 0.886, and RMSE = 0.42 for LSTM;R2 = 0.9, NSE = 0.9 and RMSE = 0.397 for GRU) and in validation (R2 = 0.865, NSE = 0.851, and RMSE = 0.329 for LSTM;R2 = 0.9, NSE = 0.865 and RMSE = 0.301 for GRU). This good performance of LSTM and GRU models confirms the importance of models based on machine learning in modeling hydrological phenomena for better decision-making.

A universal thermodynamic model of calculating mass action concentrations for structural units or ion couples in aqueous solutions and its applications in binary and ternary aqueous solutions

A universal thermodynamic model of calculating mass action concentrations for structural units or ion couples in ternary and binary strong electrolyte aqueous solution was developed based on the ion and molecule coexistence theory and verified in four kinds of binary aqueous solutions and two kinds of ternary aqueous solutions. The calculated mass action concentrations of structural units or ion couples in four binary aqueous solutions and two ternary solutions at 298.15 K have good agreement with the reported activity data from literatures after shifting the standard state and concentration unit. Therefore, the calculated mass action concentrations of structural units or ion couples from the developed universal thermodynamic model for ternary and binary aqueous solutions can be applied to predict reaction ability of components in ternary and binary strong electrolyte aqueous solutions. It is also proved that the assumptions applied in the developed thermodynamic model are correct and reasonable, i.e., strong electrolyte aqueous solution is composed of cations and anions as simple ions, H2O as simple molecule and other hydrous salt compounds as complex molecules. The calculated mass action concentrations of structural units or ion couples in ternary and binary strong electrolyte aqueous solutions strictly follow the mass action law.

ON INFORMATION THEORY METHOD FOR RELIABILITY ASSESSMENT OF SYSTEMS CONSISTING OF DIFFERENT SUCCESS FAILURE MODEL UNITS

Using the characteristic of addition of information quantity and the principle of equivalence of information quantity, this paper derives the general conversion formulae of the formation theory method conversion (synthesis) on the systems consisting of different success failure model units. According to the fundamental method of the unit reliability assessment, the general models of system reliability approximate lower limits are given. Finally, this paper analyses the application of the assessment method by examples, the assessment results are neither conservative nor radical and very satisfactory. The assessment method can be popularized to the systems which have fixed reliability structural models.

Nonlinear Mathematical Modeling and Sensitivity Analysis of Hydraulic Drive Unit

The previous sensitivity analysis researches are not accurate enough and also have the limited reference value, because those mathematical models are relatively simple and the change of the load and the initial displacement changes of the piston are ignored, even experiment verification is not conducted. Therefore, in view of deficiencies above, a nonlinear mathematical model is established in this paper, including dynamic characteristics of servo valve, nonlinear characteristics of pressure-flow, initial displacement of servo cylinder piston and friction nonlinearity. The transfer function block diagram is built for the hydraulic drive unit closed loop position control, as well as the state equations. Through deriving the time-varying coefficient items matrix and time-varying free items matrix of sensitivity equations respectively, the expression of sensitivity equations based on the nonlinear mathematical model are obtained. According to structure parameters of hydraulic drive unit, working parameters, fluid transmission characteristics and measured friction-velocity curves, the simulation analysis of hydraulic drive unit is completed on the MATLAB/Simulink simulation platform with the displacement step 2 mm, 5 mm and 10 mm, respectively. The simulation results indicate that the developed nonlinear mathematical model is sufficient by comparing the characteristic curves of experimental step response and simulation step response under different constant load. Then, the sensitivity function time-history curves of seventeen parameters are obtained, basing on each state vector time-history curve of step response characteristic. The maximum value of displacement variation percentage and the sum of displacement variation absolute values in the sampling time are both taken as sensitivity indexes. The sensitivity indexes values above are calculated and shown visually in histograms under different working conditions, and change rules are analyzed. Then the sensitivity indexes values of four measurable parameters, such as supply pressure, proportional gain, initial position of servo cylinder piston and load force, are verified experimentally on test platform of hydraulic drive unit, and the experimental research shows that the sensitivity analysis results obtained through simulation are approximate to the test results. This research indicates each parameter sensitivity characteristics of hydraulic drive unit, the performance-affected main parameters and secondary parameters are got under different working conditions, which will provide the theoretical foundation for the control compensation and structure optimization of hydraulic drive unit.

Optimization of a precise integration method for seismic modeling based on graphic processing unit

General purpose graphic processing unit （GPU） calculation technology is gradually widely used in various fields. Its mode of single instruction, multiple threads is capable of seismic numerical simulation which has a huge quantity of data and calculation steps. In this study, we introduce a GPU-based parallel calculation method of a precise integration method （PIM） for seismic forward modeling. Compared with CPU single-core calculation, GPU parallel calculating perfectly keeps the features of PIM, which has small bandwidth, high accuracy and capability of modeling complex substructures, and GPU calculation brings high computational efficiency, which means that high-performing GPU parallel calculation can make seismic forward modeling closer to real seismic records.

Toward three-dimensional in vitro models to study neurovascular unit functions in health and disease

The high metabolic demands of the brain require an efficient vascular system to be coupled with neural activity to supply adequate nutrients and oxygen.This supply is coordinated by the action of neurons,glial and vascular cells,known collectively as the neurovascular unit,which temporally and spatially regulate local cerebral blood flow through a process known as neurovascular coupling.In many neurodegenerative diseases,changes in functions of the neurovascular unit not only impair neurovascular coupling but also permeability of the blood-brain barrier,cerebral blood flow and clearance of waste from the brain.In order to study disease mechanisms,we need improved physiologicallyrelevant human models of the neurovascular unit.Advances towards modeling the cellular complexity of the neurovascular unit in vitro have been made using stem-cell derived organoids and more recently,vascularized organoids,enabling intricate studies of non-cell autonomous processes.Engineering and design innovations in microfluidic devices and tissue engineering are progressing our ability to interrogate the cerebrovasculature.These advanced models are being used to gain a better understanding of neurodegenerative disease processes and potential therapeutics.Continued innovation is required to build more physiologically-relevant models of the neurovascular unit encompassing both the cellular complexity and designed features to interrogate neurovascular unit functionality.

Thermal Model of High-Speed Spindle Units

For the purpose to facilitate development of high-speed Spindle Units (SUs) running on rolling bearings, we have developed a beam element model, algorithms, and software for computer analysis of thermal characteristics of SUs. The thermal model incorporates a model of heat generation in rolling bearings, a model of heat transfer from bearings, and models for estimation of temperature and temperature deformations of SU elements. We have carried out experimental test and made quantitative evaluation of the effect of operation conditions on friction and thermal characteristics of the SUs of grinding and turning machines of typical structures. It is found that the operation conditions make stronger effect on SU temperatures when rpm increases. A comparison between the results of analysis and experiment proves their good mutual correspondence and allows us to recommend application of the models and software developed for design and research of high-speed SUs running on rolling bearings.

A thermodynamic model of calculating mass action concentrations for structural units or ion couples in NaClO_4-H_2O and NaF-H_2O binary solutions and NaClO_4-NaF-H_2O ternary solution

A thermodynamic model of calculating mass action concentrations for structural units or ion couples in NaClO4-H2O and NaF-H2O binary solutions and NaClO4-NaF-H2O ternary strong electrolyte aqueous solutions was developed based on the ion and molecule coexistence theory （IMCT）. A transformation coefficient was needed to compare the calculated mass action concentration and the reported activity, because they were usually obtained at different standard states and concentration units. The results show that transformation coefficients between the calculated mass action concentrations and the reported activities of the same components change in a very narrow range. The transformed mass action concentrations of structural units or ion couples in NaClO4-H2O and NaF-H2O binary solutions agree well with the reported activities. The transformed mass action concentrations of structural units or ion couples in NaClO4-NaF-H2O ternary solution are also in good agreement with the reported activities in a total ionic strength range from 0.1 to 0.9 mol/kg H2O by the 0.1 mol/kg step with different ionic strength fractions of 0, 0.2, 0.4, 0.5, 0.6, 0.8, and 1, respectively. The results indicate that the developed thermodynamic model can reveal the structural characteristics of binary and ternary strong electrolyte aqueous solutions, and the calculated mass action concentrations of structural units or ion couples also strictly follow the mass action law.

LVP Modeling and Dynamic Characteristics Prediction of A Hydraulic Power Unit in Deep-Sea

A hydraulic power unit （HPU） is the driving ＂heart＂ of deep-sea working equipment. It is critical to predict its dynamic performances in deep-water before being immerged in the seawater, while the experimental tests by simulating deep-sea environment have many disadvantages, such as expensive cost, long test cycles, and difficult to achieve low-temperature simulation, which is only used as a supplementary means for confirmatory experiment. This paper proposes a novel theoretical approach based on the linear varying parameters （LVP） modeling to foresee the dynamic performances of the driving unit. Firstly, based on the varying environment features, dynamic expressions of the compressibility and viscosity of hydranlic oil are derived to reveal the fluid performances changing. Secondly, models of hydraulic system and electrical system are accomplished respectively through studying the control process and energy transfer, and then LVP models of the pressure and flow rate control is obtained through the electro-hydraulic models integration. Thirdly, dynamic characteristics of HPU are obtained by the model simulating within bounded closed sets of varying parameters. Finally, the developed HPU is tested in a deep-sea imitating hull, and the experimental results are well consistent with the theoretical analysis outcomes, which clearly declare that the LVP modeling is a rational way to foresee dynamic performances of HPU. The research approach and model analysis results can be applied to the predictions of working properties and product designs for other deep-sea hydraulic pump.

Remote nursing training model combined with proceduralization in the intensive care unit dealing with patients with COVID-19

The shortage of personal protective equipment and lack of proper nursing training have been endangering health care workers dealing with coronavirus disease 2019(COVID-19).In our treatment center,the implementation of a holistic care model of time-sharing management for severe and critical COVID-19 patients has further aggravated the shortage of intensive care unit(ICU)professional nurses.Therefore,we developed a short-term specialized and targeted nursing training program to help ICU nurses to cope with stress and become more efficient,thus reducing the number of nurses required in the ICU.In order to avoid possible human-to-human spread,small teaching classes and remote training were applied.The procedural training mode included four steps:preparation,plan,implementation,and evaluation.An evaluation was conducted throughout the process of nursing training.In this study,we documented and shared experiences in transitioning from traditional face-to-face programs to remote combined with proceduralization nursing training mode from our daily work experiences during the COVID-19 pandemic,which has shown to be helpful for nurses working in the ICU.

A finite element modeling of the human lumbar unit including the spinal cord

The purpose of this present work is to provide a tool to better understand mechanically related pathologies of the lumbar unit and the spinal structure by providing spinal cord deformations in different loading cases. In fact, spinal cord injury (SCI) resulting from a traumatic movement leades to a deformation of the neural and vascular structure of the spinal cord. And since the magnitude of the spinal cord stress is correlated with the pressure of the vertebral elements, stresses will be computed on all theses components. Physical properties of the vertebrae, various ligaments, the discs, and the spinal cord are described under simple loading as compression, and combined loading, flexion and lateral bending to evaluate the pressure undergone by different components of the lumbar unit. A nonlinear three-dimensional finite element method is used as a numerical tool to perform all the computations. This study provides accurate results for the localisation and the magnitude of maximum equivalent stress and shear stress on the lumbar unit and especially for the spinal cord. These results showed that stresses are more important when a compression of 500 N is combined with a flexion and a lateral bending. In particular, shear stresses are maximum for the spinal cord and the four intervertebral discs for the case of a flexion of 3.8 N.m and a lateral bending of 6.5 N.m.

Identification of Suitable Hydrologic Response Unit Thresholds for Soil and Water Assessment Tool Streamflow Modelling

Use of a non-zero hydrologic response unit(HRU) threshold is an effective way of reducing unmanageable HRU numbers and simplifying computational cost in the Soil and Water Assessment Tool(SWAT) hydrologic modelling. However, being less representative of watershed heterogeneity and increasing the level of model output uncertainty are inevitable when minor HRU combinations are disproportionately eliminated. This study examined 20 scenarios by running the model with various HRU threshold settings to understand the mechanism of HRU threshold effects on watershed representation as well as streamflow predictions and identify the appropriate HRU thresholds. Findings show that HRU numbers decrease sharply with increasing HRU thresholds. Among different HRU threshold scenarios, the composition of land-use, soil, and slope all contribute to notable variations which are directly related to the model input parameters and consequently affect the streamflow predictions. Results indicate that saturated hydraulic conductivity, average slope of the HRU, and curve number are the three key factors affecting stream discharge when changing the HRU thresholds. It is also found that HRU thresholds have little effect on monthly model performance, while evaluation statistics for daily discharges are more sensitive than monthly results. For daily streamflow predictions, thresholds of 5%/5%/5%(land-use/soil/slope) are the optimum HRU threshold level for the watershed to allow full consideration of model accuracy and efficiency in the present work. Besides, the results provide strategies for selecting appropriate HRU thresholds based on the modelling goal.

Research on virtual entity decision model for LVC tactical confrontation of army units

According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and generalization for the enemy,the confrontation process is modeled as a zero-sum stochastic game(ZSG).By introducing the theory of dynamic relative power potential field,the problem of reward sparsity in the model can be solved.By reward shaping,the problem of credit assignment between agents can be solved.Based on the idea of meta-learning,an extensible multi-agent deep reinforcement learning(EMADRL)framework and solving method is proposed to improve the effectiveness and efficiency of model solving.Experiments show that the model meets the requirements well and the algorithm learning efficiency is high.

Equality Testing for Soil Grid Unit Resolutions to Polygon Unit Scales with DNDC Modeling of Regional SOC Pools

Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales,namely,1:50 000(C5),1:200 000(D2),1:500 000(P5),1:1 000 000(N1),1:4 000 000(N4) and 1:14 000 000(N14),in the Taihu Region of China.Both soil unit formats were used for regional SOC pool simulation with a De Nitrification-DeC omposition(DNDC) process-based model,which spans the time period from 1982 to 2000 at the six map scales.Four indices,namely,soil type number(STN),area(AREA),average SOC density(ASOCD) and total SOC stocks(SOCS) of surface paddy soils that were simulated by the DNDC,were distinguished from all these soil polygon and grid units.Subjecting to the four index values(IV) from the parent polygon units,the variations in an index value(VIV,%) from the grid units were used to assess its dataset accuracy and redundancy,which reflects the uncertainty in the simulation of SOC pools.Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools,matching their respective soil polygon unit map scales.With these optimal raster resolutions,the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy,when VIV < 1% was assumed to be a criterion for all four indices.A quadratic curve regression model,namely,y = – 0.80 × 10^(–6)x^2 + 0.0228 x + 0.0211(R^2 = 0.9994,P < 0.05),and a power function model R? = 10.394?^(0.2153)(R^2 = 0.9759,P < 0.05) were revealed,which describe the relationship between the optimal soil grid unit resolution(y,km) and soil polygon unit map scale(1:10 000x),the ratio(R?,%) of the optimal soil grid size to average polygon patch size(?,km^2) and the ?,with the highest R^2 among different mathematical regressions,respectively.This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale,and be referenced to other landscape polygon patches' mesh partition.