From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

Improvements of corner frequency and scaling factor for stochastic finite-fault modeling

In this paper, three existing source spectral models for stochastic finite-fault modeling of ground motion were reviewed. These three models were used to calculate the far-field received energy at a site from a vertical fault and the mean spectral ratio over 15 stations of the Northridge earthquake, and then compared. From the comparison, a necessary measure was observed to maintain the far-field received energy independent of subfault size and avoid overestimation of the long- period spectra/level. Two improvements were made to one of the three models （i.e., the model based on dynamic comer frequency） as follows： （i） a new method to compute the subfault comer frequency was proposed, where the subfault comer frequency is determined based on a basic value calculated from the total seismic moment of the entire fault and an increment depending on the seismic moment assigned to the subfault; and （ii） the difference of the radiation energy from each suhfault was considered into the scaling factor. The improved model was also compared with the unimproved model through the far-field received energy and the mean spectral ratio. The comparison proves that the improved model allows the received energy to be more independent of subfault size than the unimproved model, and decreases the overestimation degree of the long-period spectral amplitude.

Biomechanical risk factors of non-contact ACL injuries:A stochastic biomechanical modeling study

Significant efforts have been made to identify modifiable risk factors of non-contact anterior cruciate ligament(ACL)injuries in male and female athletes.However,current literature on the risk factors for ACL injury are purely descriptive.An understanding of biomechanical relationship between risk and risk factors of the non-contact ACL injury is necessary to develop effective prevention programs.Purpose:To compare lower extremity kinematics and kinetics between trials with and without non-contact ACL injuries and to determine if any difference exists between male and female trials with non-contact ACL injuries regarding the lower extremity motion patterns.Methods:In this computer simulation study,a stochastic biomechanical model was used to estimate the ACL loading at the time of peak posterior ground reaction force(GRF)during landing of the stop-jump task.Monte Carlo simulations were performed to simulate the ACL injuries with repeated random samples of independent variables.The distributions of independent variables were determined from in vivo laboratory data of 40 male and 40 female recreational athletes.Results:In the simulated injured trials,both male and female athletes had significantly smaller knee flexion angles,greater normalized peak posterior and vertical GRF,greater knee valgus moment,greater patella tendon force,greater quadriceps force,greater knee extension moment,and greater proximal tibia anterior shear force in comparison to the simulated uninjured trials.No significant difference was found between genders in any of the selected biomechanical variables in the trials with simulated non-contact ACL injuries.Conclusion:Small knee flexion angle,large posterior GRF,and large knee valgus moment are risk factors of non-contact ACL injury determined by a stochastic biomechanical model with a cause-and-effect relationship.Copyright(c)2012,Shanghai University of Sport.Production and hosting by Elsevier B.V.All rights reserved.

Energy Factor and Mathematical Modeling for Power DC/DC Converters

Mathematical modelling for power DC/DC converters is a historical problem accompanying DC/DC conversion technology since 1940’s. The traditional mathematical modelling is not available for complex structure converters since the differential equation order increases very high. We have to search other way to establish mathematical modelling for power DC/DC converters.We have theoretically defined a new concept-Energy Factor （EF） in this paper and researched the relations between EF and the mathematical modelling for power DC/DC converters. EF is a new concept in power DC/DC conversion technology, which thoroughly differs from the traditional concepts such as power factor （PF）, power transfer efficiency （η）, total harmonic distortion （THD） and ripple factor （RF）. EF and the subsequential EFV (and EFVD) can illustrate the system stability, reference response and interference recovery. This investigation is very helpful for system design and DC/DC converters characteristics foreseeing. Two DC/DC converters: Buck converter and Super-Lift Luo-Converter as the samples are analysed in this paper to demonstrate the applications of EF, EFV (and EFVD), PE, SE, VE （and VED）, time constant τ and damping time constant τd.

Prediction of Vibration Characteristics in Beam Structure Using Sub-Scale Modeling with Experimental Validation

Geometric or sub-scale modeling techniques are used for the evaluation of large and complex dynamic structures to ensure accurate reproduction of load path and thus leading to true dynamic characteristics of such structures. The sub-scale modeling technique is very effective in the prediction of vibration characteristics of original large structure when the experimental testing is not feasible due to the absence of a large testing facility. Previous researches were more focused on free and harmonic vibration case with little or no consideration for readily encountered random vibration. A sub-scale modeling technique is proposed for estimating the vibration characteristics of any large scale structure such as Launch vehicles, Mega structures, etc., under various vibration load cases by utilizing precise scaled-down model of that dynamic structure. In order to establish an analytical correlation between the original structure and its scaled models, different scale models of isotropic cantilever beam are selected and analyzed under various vibration conditions（ i.e. free, harmonic and random） using finite element package ANSYS. The developed correlations are also validated through experimental testing The prediction made from the vibratory response of the scaled-down beam through the established sets of correlation are found similar to the response measured from the testing of original beam structure. The established correlations are equally applicable in the prediction of dynamic characteristics of any complex structure through its scaled-down models. This paper presents modified sub-scale modeling technique that enables accurate prediction of vibration characteristics of large and complex structure under not only sinusoidal but also for random vibrations.

Factors Affecting Quality of Equipment Maintenance Based on Structural Equation Modeling

Factors affecting equipment maintenance quality usually include personnel,equipment and facilities,raw materials and spare parts, craft, environment and quality management. These six factors affecting maintenance quality are not all the same degree. In order to find out which factor can influence equipment maintenance quality greatly and achieve sophisticated management,characteristic quantities of six factors are presented. A structural equation modeling( SEM) is designed using the six factors as latent variables and their characteristic quantities as the observed variable.According to the basic data obtained from the questionnaire survey,calculate the standardized regression weight by parameter estimation of the SEM. Then the key factors affecting the quality of the equipment maintenance are determined with the help of the standardized regression weight.

An Effective Feature Modeling Approach for 3D Structural Topology Design Optimization

This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features bythe feature modeling technology, and the models of feature elements are established. The feature elements thatmeet the design requirements are found by employing a feature matching technology, and the constraint factorscombined with the pseudo density of elements are initialized according to the optimized feature elements. Then,through controlling the constraint factors and utilizing the optimization criterion method along with the filteringtechnology of independent mesh, the structural design optimization is implemented. The present feature modelingapproach is applied to the feature-based structural topology optimization using empirical data. Meanwhile, theimproved mathematical model based on the density method with the constraint factors and the correspondingsolution processes are also presented. Compared with the traditional method which requires complicated constraintprocessing, the present approach is flexibly applied to the 3D structural design optimization with added holesby changing the constraint factors, thus it can design a structure with predetermined features more directly andeasily. Numerical examples show effectiveness of the proposed feature modeling approach, which is suitable for thepractical engineering design.

Structural Modeling of the Top Turonian Reservoir in the Northern Seme Oilfield (Benin, West-Africa)

The Sèmè oilfield is located in Benin’s offshore coastal sedimentary basin, near the Benin-Nigeria border, and contains two important oil bearing structures called “Sèmè North” and “Sèmè South”. In this coastal basin, Turonian sandstones of Abeokuta formation (Cenomanian-Turonian to Early Senonian age) form two reservoir units differentiated by two seismic horizons H6 and H6.5. The H6 seismic horizon represents the upper reservoir unit and is the main reservoir from which, more than 22 million barrels of crude oil had previously been produced in Sèmè oilfield. In order to improve knowledge of field petroleum geology, the present study presents the structural features of this upper reservoir unit. The use of Petrel software modules for the integration of 15 wells data, allowed presenting a structural model and illustrative cross sections that precise the geometry and specifying the structural characteristics of this reservoir unit within Sèmè field. The displayed structural architecture shows that the upper Turonian sandstones unit is composed of 11 layers including 7 reservoir layers (A, B, C1, C2, D1, D2, E) and 4 intra-reservoir layers (1, 2, 3 and 4) controlled by faults systems. The model provides basic framework necessary for geological characterization of the reservoir through a static model. The results of this study can be used for petrophysical modeling, Gross Rock Volume (GRV) determination and technical redevelopment of the field.

Quantifying Responses of Winter Wheat Physiological Processes to Soil Water Stress for Use in Growth Simulation Modeling

A deep understanding of crop-water eco-physiological relations is the basis for quantifying plant physiological responses to soil water stress. Pot experiments were conducted to investigate the winter wheat crop-water relations under both drought and waterlogging conditions in two sequential growing seasons from 2000 to 2002, and then the data were used to develop and validate models simulating the responses of winter wheat growth to drought and waterlogging stress. The experiment consisted of four treatments, waterlogging (keep 1 to 2 cm water layer depth above soil surface), control (70%-80% field capacity), light drought (40%-50% field capacity) and severe drought (30%-40% field capacity) with six replicates at five stages in the 2000-2001 growth season. Three soil water content treatments (waterlogging, control and drought) with two replicates were designed in the 2001-2002 growth season. Waterlogging and control treatments are the same as in the 2000-2001 growth season. For the drought treatment, no water was supplied and the soil moisture decreased from field capacity to wilting point. Leaf net photosynthetic rate, transpiration rate, predawn leaf water potential, soil water potential, soil water content and dry matter weight of individual organs were measured. Based on crop-water eco-physiological relations, drought and waterlogging stress factors for winter wheat growth simulation model were put forward. Drought stress factors integrated soil water availability, the sensitivity of different development stages and the difference between physiological processes (such as photosynthesis, transpiration and partitioning). The quantification of waterlogging stress factor considered different crop species, soil water status, waterlogging days and sensitivity at different growth stages. Data sets from the pot experiments revealed favorable performance reliability for the simulation sub-models with the drought and waterlogging stress factors.

Effects of main range and strength factors on change of COD in a shallow lake

A model involving the relationship between non conservative matter (COD) and main range factors(loading, water stage) and strength factor (temperature) in a shallow lake has been established on the basis of less change of water stage area curve in certain extent, broader distribution in pollution sources and more homogeneous in quantity of heat. The effects of COD inflow and /or outflow, and hydro meterological factors upon COD content in Lake Taihu were calculated and simulated under the actual situation of the loadings and regimen. The results simulated by using the model are in better agreement with the observed field data. The error of the model was also discussed.

New approaches to cognitive work analysis through latent variable modeling in mining operations

This paper discusses the utilization of latent variable modeling related to occupational health and safety in the mining industry.Latent variable modeling,which is a statistical model that relates observable and latent variables,could be used to facilitate researchers’understandings of the underlying constructs or hypothetical factors and their magnitude of effect that constitute a complex system.This enhanced understanding,in turn,can help emphasize the important factors to improve mine safety.The most commonly used techniques include the exploratory factor analysis(EFA),the confirmatory factor analysis(CFA)and the structural equation model with latent variables(SEM).A critical comparison of the three techniques regarding mine safety is provided.Possible applications of latent variable modeling in mining engineering are explored.In this scope,relevant research papers were reviewed.They suggest that the application of such methods could prove useful in mine accident and safety research.Application of latent variables analysis in cognitive work analysis was proposed to improve the understanding of human-work relationships in mining operations.

Integration of aspect and slope in snowmelt runoff modeling in a mountain watershed

This study assessed the performances of the traditional temperature-index snowmelt runoff model(SRM) and an SRM model with a finer zonation based on aspect and slope(SRM + AS model) in a data-scarce mountain watershed in the Urumqi River Basin,in Northwest China.The proposed SRM + AS model was used to estimate the melt rate with the degree-day factor(DDF) through the division of watershed elevation zones based on aspect and slope.The simulation results of the SRM + AS model were compared with those of the traditional SRM model to identify the improvements of the SRM + AS model's performance with consideration of topographic features of the watershed.The results show that the performance of the SRM + AS model has improved slightly compared to that of the SRM model.The coefficients of determination increased from 0.73,0.69,and 0.79 with the SRM model to 0.76,0.76,and 0.81 with the SRM + AS model during the simulation and validation periods in 2005,2006,and 2007,respectively.The proposed SRM + AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization,careful input data selection,and data preparation.

Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

The development of functional relationships between the observed deposition rate and the experimental conditions is an important step toward understanding and optimizing low-pressure chemical vapor deposition(LPCVD)or low-pressure chemical vapor infiltration(LPCVI).In the field of ceramic matrix composites(CMCs),methyltrichlorosilane(CH3 SiCl3,MTS)is the most widely used source gas system for SiC,because stoichiometric SiC deposit can be facilitated at 900°C–1300°C.However,the reliability and accuracy of existing numerical models for these processing conditions are rarely reported.In this study,a comprehensive transport model was coupled with gas-phase and surface kinetics.The resulting gas-phase kinetics was confirmed via the measured concentration of gaseous species.The relationship between deposition rate and 24 gaseous species has been effectively evaluated by combining the special superiority of the novel extreme machine learning method and the conventional sticking coefficient method.Surface kinetics were then proposed and shown to reproduce the experimental results.The proposed simulation strategy can be used for different material systems.

Analytical modeling of sandwich beam for piezoelectric bender elements

Piezoelectric bender elements are widely used as electromechanical sensors and actuators, An analytical sandwich beam model for piezoelectric bender elements was developed based on the first-order shear deformation theory （FSDT）, which assumes a single rotation angle for the whole cross-section and a quadratic distribution function for coupled electric potential in piezoelectric layers, and corrects the effect of transverse shear strain on the electric displacement integration. Free vibration analysis of simplysupported bender elements was carried out and the numerical results showed that, solutions of the present model for various thickness-to-length ratios are compared well with the exact two-dimensional solutions, which presents an efficient and accurate model for analyzing dynamic electromechanical responses of bender elements.

Solubility measurement and thermodynamic modeling of carbamazepine(form III)in five pure solvents at various temperatures

In this work,the solubilities of carbamazepine(CBZ)(form III)in ethyl acetate,methyl acetate,ethylene glycol,chloroform and cyclohexylamine were determined by laser monitoring techniques at pressure above sea level,and the solubility data of CBZ(form III)in different pure solvents were fitted by the Modified Apelblat model andλh model.The result shows that the solubility of CBZ(form III)in five solvents increases as temperature rises,and the solubility in chloroform was the largest.The experimental solubility values of CBZ(form III)in ethyl acetate,methyl acetate,chloroform and cyclohexylamine were in better agreement with the simulated fitting values of theλh model.For ethylene glycol,the r value was much larger than the other four solvents,and it can be seen from theλh model that ethylene glycol was closer to the ideal solution system than the other four solvents.

Species distribution modeling in regions of high need and limited data： waterfowl of China

Background: A number of conservation and societal issues require understanding how species are distributed on the landscape, yet ecologists are often faced with a lack of data to develop models at the resolution and extent desired, resulting in inefficient use of conservation resources.Such a situation presented itself in our attempt to develop waterfowl distribution models as part of a multi-disciplinary team targeting the control of the highly pathogenic H5N1 avian influenza virus in China.Methods: Faced with limited data, we built species distribution models using a habitat suitability approach for China's breeding and non-breeding(hereafter, wintering) waterfowl.An extensive review of the literature was used to determine model parameters for habitat modeling.Habitat relationships were implemented in GIS using land cover covariates.Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available.Results: We developed suitability models for 42 waterfowl species(30 breeding and 39 wintering) at 1 km resolution for the extent of China, along with cumulative and genus level species richness maps.Breeding season models showed highest waterfowl suitability in wetlands of the high-elevation west-central plateau and northeastern China.Wintering waterfowl suitability was highest in the lowland regions of southeastern China.Validation measures indicated strong performance in predicting species presence.Comparing our model outputs to China's protected areas indicated that breeding habitat was generally better covered than wintering habitat, and identified locations for which additional research and protection should be prioritized.Conclusions: These suitability models are the first available for many of China's waterfowl species, and have direct utility to conservation and habitat planning and prioritizing management of critically important areas, providing an example of how this approach may aid others faced with the challenge of addressing conservation issues with little data to inform decision making.

SPH Modeling of Droplet Impact on Solid Boundary

A droplet undergoes spreading,rebounding or splashing when it impacts solid boundary,which is a typical phenomenon of free surface flow that exists widely in modern industry.Smoothed particle hydrodynamics(SPH)method is applied to numerically study the dynamical behaviors of the droplet impacting solid boundary,and both the spreading and rebounding phenomena of the droplet are reproduced in the simulation.The droplet deformation,flow fields and pressure fields inside the droplet at different moments are analyzed.Two important factors,the initial velocity and diameter,are discussed in determining the maximum spreading factor,revealing that the maximum spreading factor increases with the increase of the impact velocity and droplet diameter respectively.

Measurement and Modeling for the Solubility of Hydrogen Sulfide in Primene JM-T

The primary aliphatic amine Primene JM-T was investigated as a potential absorbent for H2S removal. The solubility of HzS in JM-T was measured at 298, 313,333,353, and 368 K with H2S partial pressures from 20 to 760 kPa and HzS loading from 0.02 to 0.8 mol H2S per mol JM-T. Relevant physical properties, such as density, viscoslty and dielectric constant, ot the matenal were measured. ＇The thermodynamlc model with two-suttlX Margules equation was used to correlate the experimental vapor-liquid equilibrium data. Furthermore, the absorption mechanism in non-aqueous system is suggested and the difference between non-aqueous and aqueous absorption system is pointed out.

Solubility and mass transfer of H2, CH4, and their mixtures in vacuum gas oil: An experimental and modeling study

In this work,the solubility data and liquid-phase mass transfer coefficients of hydrogen(H2),methane(CH4)and their mixtures in vacuum gas oil(VGO)at temperatures(353.15-453.15 K)and pressures(1-7 MPa)were measured,which are necessary for catalytic cracking process simulation and design.The solubility of H2 and CH4 in VGO increases with the increase of pressure,but decreases with the increase of temperature.Henry’s constants of H2 and CH4 follow the relation of In H=-413.05/T+5.27 and In H=-990.67/T+5.87,respectively.The molar fractions of H2 and system pressures at different equilibrium time were measured to estimate the liquid-phase mass transfer coefficients.The results showed that with the increase of pressure,the liquid-phase mass transfer coefficients increase.Furthermore,the solubility of H2 and CH4 in VGO was predicted by the predictive COSMO-RS model,and the predicted values agree well with experimental data.In addition,the gas-liquid equilibrium(GLE)for H2+CH4+VGO system at different feeding gas ratios in volume fraction(i.e.,H285%+CH415%and H290%+CH410%)was measured.The selectivity of H2 to CH4 predicted by the COSMO-RS model agrees well with experimental data.This work provides the basic thermodynamic and dynamic data for fuel oil catalytic cracking processes.

Bayesian Network and Factor Analysis for Modeling Pine Wilt Disease Prevalence

A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times. Seven factors that influence the distribution of PWD were extracted from the QuickBird images and were used as the independent variables. The results showed that the BN model predicted PWD with high accuracy. In a sensitivity analysis, elevation (EL), the normal differential vegetation index (NDVI), the distance to settlements (DS) and the distance to roads (DR) were strongly associated with PWD prevalence, and slope (SL) exhibited the weakest association with PWD prevalence. The study showed that BN is an effective tool for modeling PWD prevalence and quantifying the impact of various factors.