Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polym...Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polymer network inside is ambiguous.In this work,we construct periodic random network(PRN)models for the effective polymer network in hydrogels and investigate the non-affine deformation of polymer chains intrinsically originates from the structural randomness from bottom up.The non-affine deformation in PRN models is manifested as the actual stretch of polymer chains randomly deviated from the chain stretch predicted by affine assumption,and quantified by a non-affine ratio of each polymer chain.It is found that the non-affine ratios of polymer chains are closely related to bulk deformation state,chain orientation,and initial chain elongation.By fitting the non-affine ratio of polymer chains in all PRN models,we propose a non-affine constitutive model for the hydrogel polymer network based on micro-sphere model.The stress-strain curves of the proposed constitutive models under uniaxial tension condition agree with the simulation results of different PRN models of hydrogels very well.展开更多
The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only...The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only used for limited cases.In this letter,we propose Kolmogorov-Arnold networks(KAN)base models for wavenumber-frequency spectra of pressure fluctuations under turbulent boundary layers.The results are compared with DNS results.In turbulent channel flows,it is found that the KAN base model leads to a smooth wavenumber-frequency spectrum with sparse samples.In the turbulent flow over an axisymmetric body of revolution,the KAN base model captures the wavenumber-frequency spectra near the convective peak.展开更多
Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amoun...Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amount of micropores on the I - Sw curve using numerical modeling. The effects of formation water salinity on the electrical resistivity of the rock are discussed. Then the relative magnitudes of the different influencing factors are discussed. The effects of the different factors on the I - Sw curve are analyzed by fitting simulation results. The results show that the connectivity of the void spaces and the amount of micropores have a large effect on the I - S, curve, while the other factors have little effect. The formation water salinity has a large effect on the absolute resistivity values. The non-Archie phenomenon is prevalent, which is remarkable in rocks with low permeability.展开更多
A meso-scale truss network model was developed to predict chloride diffusion in concrete. The model regards concrete as a three-phase composite of mortar matrix, coarse aggregates, and the interfacial transition zone ...A meso-scale truss network model was developed to predict chloride diffusion in concrete. The model regards concrete as a three-phase composite of mortar matrix, coarse aggregates, and the interfacial transition zone （ITZ） between the mortar matrix and the aggregates. The diffusion coefficient of chloride in the mortar and the ITZ can be analytically determined with only the water-to-cement ratio and volume fraction of fine aggregates. Fick＇s second law of diffusion was used as the governing equation for chloride diffusion in a homogenous medium （e.g., mortar）; it was discretized and applied to the truss network model. The solution procedure of the truss network model based on the diffusion law and the meso-scale composite structure of concrete is outlined. Additionally, the dependence of the diffusion coefficient of chloride in the mortar and the ITZ on exposure duration and temperature is taken into account to illustrate their effect on chloride diffusion coefficient. The numerical results show that the exposure duration and environmental temperature play important roles in the diffusion rate of chloride ions in concrete. It is also concluded that the meso-scale truss network model can be applied to chloride transport analysis of damaged （or cracked） concrete.展开更多
In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integr...In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.展开更多
Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patien...Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).展开更多
Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This st...Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.展开更多
Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Parti...Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase.The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style.Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.展开更多
Titanium dioxide （TiO2） nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitropbenol （4-NP）. All catalysts were analyzed by X-ray diffraction （XRD） and ...Titanium dioxide （TiO2） nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitropbenol （4-NP）. All catalysts were analyzed by X-ray diffraction （XRD） and scanning electron microscopy （SEM）. An artificial neural network model （ANN） was developed to predict the photocatalytic removal of 4-NP in the presence of TiOz nanoparticles prepared under desired conditions. The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.展开更多
Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of ...Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.展开更多
Discontinuity waviness is one of the most important properties that influence shear strength of jointed rock masses,and it should be incorporated into numerical models for slope stability assessment.However,in most ex...Discontinuity waviness is one of the most important properties that influence shear strength of jointed rock masses,and it should be incorporated into numerical models for slope stability assessment.However,in most existing numerical modeling tools,discontinuities are often simplified into planar surfaces.Discrete fracture network modeling tools such as MoFrac allow the simulation of non-planar discontinuities which can be incorporated into lattice-spring-based geomechanical software such as Slope Model for slope stability assessment.In this study,the slope failure of the south wall at Cadia Hill open pit mine is simulated using the lattice-spring-based synthetic rock mass(LS-SRM)modeling approach.First,the slope model is calibrated using field displacement monitoring data,and then the influence of different discontinuity configurations on the stability of the slope is investigated.The modeling results show that the slope with non-planar discontinuities is comparatively more stable than the ones with planar discontinuities.In addition,the slope becomes increasingly unstable with the increases of discontinuity intensity and size.At greater pit depth with higher in situ stress,both the slope models with planar and non-planar discontinuities experience localized failures due to very high stress concentrations,and the slope model with planar discontinuities is more deformable and less stable than that with non-planar discontinuities.展开更多
Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the ef...Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the effects of complex pore structures and wettability.To address this issue,based on the digital rock of low permeability sandstone,a direct numerical simulation is performed considering the interphase drag and boundary slip to clarify the microscopic water-oil displacement process.In addition,a dual-porosity pore network model(PNM)is constructed to obtain the water-oil relative permeability of the sample.The displacement efficiency as a recovery process is assessed under different wetting and pore structure properties.Results show that microscopic displacement mechanisms explain the corresponding macroscopic relative permeability.The injected water breaks through the outlet earlier with a large mass flow,while thick oil films exist in rough hydrophobic surfaces and poorly connected pores.The variation of water-oil relative permeability is significant,and residual oil saturation is high in the oil-wet system.The flooding is extensive,and the residual oil is trapped in complex pore networks for hydrophilic pore surfaces;thus,water relative permeability is lower in the water-wet system.While the displacement efficiency is the worst in mixed-wetting systems for poor water connectivity.Microporosity negatively correlates with invading oil volume fraction due to strong capillary resistance,and a large microporosity corresponds to low residual oil saturation.This work provides insights into the water-oil flow from different modeling perspectives and helps to optimize the development plan for enhanced recovery.展开更多
Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden p...Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden particularly in developed countries. Many studies suggest that ulcerative colitis results from an abnormal immune response against components of cornrnensal rnicrobiota in genetically susceptible individuals. However, understanding of the disease mechanisms at cellular and molecular levels remains largely elusive. In this paper, a network model is developed based on our previous study and computer simulations are perforrned using an agent-based network modeling to elucidate the dynamics of immune response in ulcerative colitis progression. Our modeling study identifies several important positive feedback loops as a driving force for ulcerative colitis initiation and progression. The results demonstrate that although immune response in ulcerative colitis patients is dominated by anti-inflarnrnatory/regulatory cells such as alternatively activated rnacrophages and type II natural killer T cells, proinflarnrnatory cells including classically activated rnacrophages, T helper 1 and T helper 17 cells, and their secreted cytokines tumor necrosis factor-α, interleukin-12, interleukin-23, interleukin-17 and interferon-γ remain at certain levels （lower than those in Crohn＇s disease, another inflammatory bowel disease）. Long-terrn exposure to these proinflarnrnatory components, causes rnucosal tissue damage persistently, leading to ulcerative colitis. Our simulation results are qualitatively in agreement with clinical and laboratory measurements, offering novel insight into the disease mechanisms.展开更多
Multiplicative multifractal process could well modal video traffic. The multiplier distributions in the multiplicatire multifractal model for video traffic are investigated and it is found that Gaussian is not suitabl...Multiplicative multifractal process could well modal video traffic. The multiplier distributions in the multiplicatire multifractal model for video traffic are investigated and it is found that Gaussian is not suitable for describing the multipliers on the small time scales. A new statistical distribution-symmetric Pareto distribution is introduced. It is applied instead of Gaussian for the multipliers on those scales. Based on that, the algorithm is updated so that symmetric pareto distribution and Gaussian distribution are used to model video traffic but on different time scales. The simulation results demonstrate that the algorithm could model video traffic more accurately.展开更多
The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling（SEM） demonstrated the potential for analyzing such associati...The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling（SEM） demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM（PLS-SEM） and geographically-weighted regression（GWR） modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass（AGB）. The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems.展开更多
Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid...Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.展开更多
One of the key challenges in largescale network simulation is the huge computation demand in fine-grained traffic simulation.Apart from using high-performance computing facilities and parallelism techniques,an alterna...One of the key challenges in largescale network simulation is the huge computation demand in fine-grained traffic simulation.Apart from using high-performance computing facilities and parallelism techniques,an alternative is to replace the background traffic by simplified abstract models such as fluid flows.This paper suggests a hybrid modeling approach for background traffic,which combines ON/OFF model with TCP activities.The ON/OFF model is to characterize the application activities,and the ordinary differential equations(ODEs) based on fluid flows is to describe the TCP congestion avoidance functionality.The apparent merits of this approach are(1) to accurately capture the traffic self-similarity at source level,(2) properly reflect the network dynamics,and(3) efficiently decrease the computational complexity.The experimental results show that the approach perfectly makes a proper trade-off between accuracy and complexity in background traffic simulation.展开更多
Chronic obstructive pulmonary disease(COPD) is a chronic inflammatory disorder characterized by airflow obstruction and progressive damage of lung tissues. As currently more than 3 billion people use biomass fuel for ...Chronic obstructive pulmonary disease(COPD) is a chronic inflammatory disorder characterized by airflow obstruction and progressive damage of lung tissues. As currently more than 3 billion people use biomass fuel for cooking and heating worldwide, exposure to biomass smoke(BS) is recognized as a significant risk factor for COPD. Recent clinical data have shown that BS-COPD patients have a Th2-type inflammatory profile significantly different from that in COPD induced by cigarette smoke. As COPD is essentially proinflammatory,however, the mechanism underlying this Th2-type anti-inflammatory profile remains elusive.In this work, a network model is applied to study BS-induced inflammatory dynamics. The network model involves several positive feedback loops, activations of which are responsible for different mechanisms by which clinical phenotypes of COPD are produced. Our modeling study in this work has identified a subset of BS-COPD patients with a mixed M1-and Th2-type inflammatory profile. The model’s prediction is in good agreement with clinical experiments and our in silico knockout simulations have demonstrated several important network components that play an important role in the disease. Our modeling study provides novel insight into BS-COPD progression, offering a rationale for targeted therapy and personalized medicine for treatment of the disease in future.展开更多
Fluid flow in fractured media has been studied for decades and received considerable attention in the oil and gas industry because of the high productivity of naturally fractured reservoirs.Due to formation complexity...Fluid flow in fractured media has been studied for decades and received considerable attention in the oil and gas industry because of the high productivity of naturally fractured reservoirs.Due to formation complexity and reservoir heterogeneity,characterizing fluid flow with an appropriate reservoir model presents a challenging task that differs relatively from homogeneous conventional reservoirs in many aspects of view,including geological,petrophysical,production,and economics.In most fractured reservoirs,fracture networks create complex pathways that affect hydrocarbon flow,well performance,hence reservoir characterization.A better and comprehensive understanding of the available reservoir modeling approaches is much needed to accurately characterize fluid flow behavior in NFRs.Therefore,in this paper,a perspective review of the available modeling approaches was presented for fluid flow characterization in naturally fractured medium.Modeling methods were evaluated in terms of their description,application,advantages,and disadvantages.This study has also included the applications of these reservoir models in fluid flow characterizing studies and governing equations for fluid flow.Dual continuum models were proved to be better than single continuum models in the presence of large scale fractures.In comparison,discrete models were more appropriate for reservoirs that contain a smaller number of fractures.However,hybrid modeling was the best method to provide accurate and scalable fluid flow modeling.It is our understanding that this paper will bridge the gap between the fundamental understanding and application of NFRs modeling approaches and serve as a useful reference for engineers and researchers for present and future applications.展开更多
Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large ...Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large time-scale. This paper investigates the change of non-stationary self-similarity of network traffic over time,and proposes a method of combining the discrete wavelet transform (DWT) and Schwarz information criterion (SIC) to detect change points of self-similarity in network traffic. The traffic is segmented into pieces around changing points with homogenous characteristics for the Hurst parameter,named local Hurst parameter,and then each piece of network traffic is modeled using fractional Gaussian noise (FGN) model with the local Hurst parameter. The presented experimental performance on data set from the Internet Traffic Archive (ITA) demonstrates that the method is more accurate in describing the non-stationary self-similarity of network traffic.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12202339 and 12172273)Xi’an Jiaotong University Tang Scholar.
文摘Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polymer network inside is ambiguous.In this work,we construct periodic random network(PRN)models for the effective polymer network in hydrogels and investigate the non-affine deformation of polymer chains intrinsically originates from the structural randomness from bottom up.The non-affine deformation in PRN models is manifested as the actual stretch of polymer chains randomly deviated from the chain stretch predicted by affine assumption,and quantified by a non-affine ratio of each polymer chain.It is found that the non-affine ratios of polymer chains are closely related to bulk deformation state,chain orientation,and initial chain elongation.By fitting the non-affine ratio of polymer chains in all PRN models,we propose a non-affine constitutive model for the hydrogel polymer network based on micro-sphere model.The stress-strain curves of the proposed constitutive models under uniaxial tension condition agree with the simulation results of different PRN models of hydrogels very well.
基金supported by the National Natural Science Foundation of China Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.11988102)the National Natural Science Foundation of China(Grant Nos.92252203,12102439,and 12425207)+1 种基金the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-087)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0620102).
文摘The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only used for limited cases.In this letter,we propose Kolmogorov-Arnold networks(KAN)base models for wavenumber-frequency spectra of pressure fluctuations under turbulent boundary layers.The results are compared with DNS results.In turbulent channel flows,it is found that the KAN base model leads to a smooth wavenumber-frequency spectrum with sparse samples.In the turbulent flow over an axisymmetric body of revolution,the KAN base model captures the wavenumber-frequency spectra near the convective peak.
基金This project is sponsored by National Natural Science Foundation of China, No. 40574030.
文摘Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amount of micropores on the I - Sw curve using numerical modeling. The effects of formation water salinity on the electrical resistivity of the rock are discussed. Then the relative magnitudes of the different influencing factors are discussed. The effects of the different factors on the I - Sw curve are analyzed by fitting simulation results. The results show that the connectivity of the void spaces and the amount of micropores have a large effect on the I - S, curve, while the other factors have little effect. The formation water salinity has a large effect on the absolute resistivity values. The non-Archie phenomenon is prevalent, which is remarkable in rocks with low permeability.
基金supported by the Key Project of the Chinese Ministry of Education (Grant No. 109046)the Center for Concrete Corea, Korea of the Yonsei University of Korea, the Grant-in-Aid for Scientific Research from the Japanese Government (A) (Grant No. 19206048)
文摘A meso-scale truss network model was developed to predict chloride diffusion in concrete. The model regards concrete as a three-phase composite of mortar matrix, coarse aggregates, and the interfacial transition zone （ITZ） between the mortar matrix and the aggregates. The diffusion coefficient of chloride in the mortar and the ITZ can be analytically determined with only the water-to-cement ratio and volume fraction of fine aggregates. Fick＇s second law of diffusion was used as the governing equation for chloride diffusion in a homogenous medium （e.g., mortar）; it was discretized and applied to the truss network model. The solution procedure of the truss network model based on the diffusion law and the meso-scale composite structure of concrete is outlined. Additionally, the dependence of the diffusion coefficient of chloride in the mortar and the ITZ on exposure duration and temperature is taken into account to illustrate their effect on chloride diffusion coefficient. The numerical results show that the exposure duration and environmental temperature play important roles in the diffusion rate of chloride ions in concrete. It is also concluded that the meso-scale truss network model can be applied to chloride transport analysis of damaged （or cracked） concrete.
基金the management of Sierra Rutile Company for providing the drillhole dataset used in this studythe Japanese Ministry of Education Science and Technology (MEXT) Scholarship for academic funding
文摘In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.
基金Fundamental Research Funds for the Central Universities in China,No.N161608001 and No.N171903002
文摘Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3080200)the National Natural Science Foundation of China(Grant No.42022053)the China Postdoctoral Science Foundation(Grant No.2023M731264).
文摘Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.
基金supported by the National Natural Science Foundation of China(51479158)the Fundamental Research Funds for the Central Universities(WUT:2018III061GX)
文摘Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase.The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style.Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.
文摘Titanium dioxide （TiO2） nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitropbenol （4-NP）. All catalysts were analyzed by X-ray diffraction （XRD） and scanning electron microscopy （SEM）. An artificial neural network model （ANN） was developed to predict the photocatalytic removal of 4-NP in the presence of TiOz nanoparticles prepared under desired conditions. The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.
基金financially supported by the National Natural Science Foundation of China(Nos.51275415 and50905144)the Natural Science Basic Research Plan in Shanxi Province(No.2011JQ6004)the Program of the Ministry of Education of China for Introducing Talents of Discipline to Universities(No.B08040)
文摘Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.
基金Ontario Trillium Scholarship for supporting the doctorate program at Laurentian UniversityFinancial supports from the Natural Sciences and Engineering Research Council of Canada(NSERC CRD 470490-14)of Canada+1 种基金Nuclear Waste Management Organization(NWMO)Rio Tinto。
文摘Discontinuity waviness is one of the most important properties that influence shear strength of jointed rock masses,and it should be incorporated into numerical models for slope stability assessment.However,in most existing numerical modeling tools,discontinuities are often simplified into planar surfaces.Discrete fracture network modeling tools such as MoFrac allow the simulation of non-planar discontinuities which can be incorporated into lattice-spring-based geomechanical software such as Slope Model for slope stability assessment.In this study,the slope failure of the south wall at Cadia Hill open pit mine is simulated using the lattice-spring-based synthetic rock mass(LS-SRM)modeling approach.First,the slope model is calibrated using field displacement monitoring data,and then the influence of different discontinuity configurations on the stability of the slope is investigated.The modeling results show that the slope with non-planar discontinuities is comparatively more stable than the ones with planar discontinuities.In addition,the slope becomes increasingly unstable with the increases of discontinuity intensity and size.At greater pit depth with higher in situ stress,both the slope models with planar and non-planar discontinuities experience localized failures due to very high stress concentrations,and the slope model with planar discontinuities is more deformable and less stable than that with non-planar discontinuities.
基金supported by National Natural Science Foundation of China(Grant No.42172159)Science Foundation of China University of Petroleum,Beijing(Grant No.2462023XKBH002).
文摘Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the effects of complex pore structures and wettability.To address this issue,based on the digital rock of low permeability sandstone,a direct numerical simulation is performed considering the interphase drag and boundary slip to clarify the microscopic water-oil displacement process.In addition,a dual-porosity pore network model(PNM)is constructed to obtain the water-oil relative permeability of the sample.The displacement efficiency as a recovery process is assessed under different wetting and pore structure properties.Results show that microscopic displacement mechanisms explain the corresponding macroscopic relative permeability.The injected water breaks through the outlet earlier with a large mass flow,while thick oil films exist in rough hydrophobic surfaces and poorly connected pores.The variation of water-oil relative permeability is significant,and residual oil saturation is high in the oil-wet system.The flooding is extensive,and the residual oil is trapped in complex pore networks for hydrophilic pore surfaces;thus,water relative permeability is lower in the water-wet system.While the displacement efficiency is the worst in mixed-wetting systems for poor water connectivity.Microporosity negatively correlates with invading oil volume fraction due to strong capillary resistance,and a large microporosity corresponds to low residual oil saturation.This work provides insights into the water-oil flow from different modeling perspectives and helps to optimize the development plan for enhanced recovery.
基金supported by the National Natural Science Foundation of China (No.21273209)
文摘Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden particularly in developed countries. Many studies suggest that ulcerative colitis results from an abnormal immune response against components of cornrnensal rnicrobiota in genetically susceptible individuals. However, understanding of the disease mechanisms at cellular and molecular levels remains largely elusive. In this paper, a network model is developed based on our previous study and computer simulations are perforrned using an agent-based network modeling to elucidate the dynamics of immune response in ulcerative colitis progression. Our modeling study identifies several important positive feedback loops as a driving force for ulcerative colitis initiation and progression. The results demonstrate that although immune response in ulcerative colitis patients is dominated by anti-inflarnrnatory/regulatory cells such as alternatively activated rnacrophages and type II natural killer T cells, proinflarnrnatory cells including classically activated rnacrophages, T helper 1 and T helper 17 cells, and their secreted cytokines tumor necrosis factor-α, interleukin-12, interleukin-23, interleukin-17 and interferon-γ remain at certain levels （lower than those in Crohn＇s disease, another inflammatory bowel disease）. Long-terrn exposure to these proinflarnrnatory components, causes rnucosal tissue damage persistently, leading to ulcerative colitis. Our simulation results are qualitatively in agreement with clinical and laboratory measurements, offering novel insight into the disease mechanisms.
文摘Multiplicative multifractal process could well modal video traffic. The multiplier distributions in the multiplicatire multifractal model for video traffic are investigated and it is found that Gaussian is not suitable for describing the multipliers on the small time scales. A new statistical distribution-symmetric Pareto distribution is introduced. It is applied instead of Gaussian for the multipliers on those scales. Based on that, the algorithm is updated so that symmetric pareto distribution and Gaussian distribution are used to model video traffic but on different time scales. The simulation results demonstrate that the algorithm could model video traffic more accurately.
基金supported by the National Natural Science Foundation of China (41371371)the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05050402)
文摘The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling（SEM） demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM（PLS-SEM） and geographically-weighted regression（GWR） modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass（AGB）. The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems.
基金Supported by the Specialized Research Fund for the Doctoral Program of Higher Education(No.20120042120014)
文摘Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.
基金supported by the Science and Technology Project of Zhejiang Province(No. 2014C01051)the National High Technology Development 863 Program of China( No.2015AA011901)
文摘One of the key challenges in largescale network simulation is the huge computation demand in fine-grained traffic simulation.Apart from using high-performance computing facilities and parallelism techniques,an alternative is to replace the background traffic by simplified abstract models such as fluid flows.This paper suggests a hybrid modeling approach for background traffic,which combines ON/OFF model with TCP activities.The ON/OFF model is to characterize the application activities,and the ordinary differential equations(ODEs) based on fluid flows is to describe the TCP congestion avoidance functionality.The apparent merits of this approach are(1) to accurately capture the traffic self-similarity at source level,(2) properly reflect the network dynamics,and(3) efficiently decrease the computational complexity.The experimental results show that the approach perfectly makes a proper trade-off between accuracy and complexity in background traffic simulation.
基金This work was supported by the National Natural Science Foundation of China(No.21273209).
文摘Chronic obstructive pulmonary disease(COPD) is a chronic inflammatory disorder characterized by airflow obstruction and progressive damage of lung tissues. As currently more than 3 billion people use biomass fuel for cooking and heating worldwide, exposure to biomass smoke(BS) is recognized as a significant risk factor for COPD. Recent clinical data have shown that BS-COPD patients have a Th2-type inflammatory profile significantly different from that in COPD induced by cigarette smoke. As COPD is essentially proinflammatory,however, the mechanism underlying this Th2-type anti-inflammatory profile remains elusive.In this work, a network model is applied to study BS-induced inflammatory dynamics. The network model involves several positive feedback loops, activations of which are responsible for different mechanisms by which clinical phenotypes of COPD are produced. Our modeling study in this work has identified a subset of BS-COPD patients with a mixed M1-and Th2-type inflammatory profile. The model’s prediction is in good agreement with clinical experiments and our in silico knockout simulations have demonstrated several important network components that play an important role in the disease. Our modeling study provides novel insight into BS-COPD progression, offering a rationale for targeted therapy and personalized medicine for treatment of the disease in future.
文摘Fluid flow in fractured media has been studied for decades and received considerable attention in the oil and gas industry because of the high productivity of naturally fractured reservoirs.Due to formation complexity and reservoir heterogeneity,characterizing fluid flow with an appropriate reservoir model presents a challenging task that differs relatively from homogeneous conventional reservoirs in many aspects of view,including geological,petrophysical,production,and economics.In most fractured reservoirs,fracture networks create complex pathways that affect hydrocarbon flow,well performance,hence reservoir characterization.A better and comprehensive understanding of the available reservoir modeling approaches is much needed to accurately characterize fluid flow behavior in NFRs.Therefore,in this paper,a perspective review of the available modeling approaches was presented for fluid flow characterization in naturally fractured medium.Modeling methods were evaluated in terms of their description,application,advantages,and disadvantages.This study has also included the applications of these reservoir models in fluid flow characterizing studies and governing equations for fluid flow.Dual continuum models were proved to be better than single continuum models in the presence of large scale fractures.In comparison,discrete models were more appropriate for reservoirs that contain a smaller number of fractures.However,hybrid modeling was the best method to provide accurate and scalable fluid flow modeling.It is our understanding that this paper will bridge the gap between the fundamental understanding and application of NFRs modeling approaches and serve as a useful reference for engineers and researchers for present and future applications.
基金the National High Technology Research and Development Program (863) of China(Nos. 2005AA145110 and 2006AA01Z436)the Natural Science Foundation of Shanghai of China(No. 05ZR14083)the Pudong New Area Technology Innovation Public Service Platform of China(No. PDPT2005-04)
文摘Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large time-scale. This paper investigates the change of non-stationary self-similarity of network traffic over time,and proposes a method of combining the discrete wavelet transform (DWT) and Schwarz information criterion (SIC) to detect change points of self-similarity in network traffic. The traffic is segmented into pieces around changing points with homogenous characteristics for the Hurst parameter,named local Hurst parameter,and then each piece of network traffic is modeled using fractional Gaussian noise (FGN) model with the local Hurst parameter. The presented experimental performance on data set from the Internet Traffic Archive (ITA) demonstrates that the method is more accurate in describing the non-stationary self-similarity of network traffic.
