Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important con...Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.展开更多
The Metropolitan Area Network (MAN) has faced serious problems after years of rapid development. The model of three-dimensional IP-based MAN, proposed by ZTE, is a next-generation MAN solution, which not only solves t...The Metropolitan Area Network (MAN) has faced serious problems after years of rapid development. The model of three-dimensional IP-based MAN, proposed by ZTE, is a next-generation MAN solution, which not only solves the existing problems but also brings new ideas for the development of next-generation MAN.展开更多
The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series ...The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series at boundaries, river geometry, and adjusted coefficients. Therefore, an artificial neural network (ANN) technique using a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is adopted as an effective alternative in salinity simulation studies. The present study focuses on comparing the performance of BPNN, RBFNN, and three-dimensional hydrodynamic models as applied to a tidal estuarine system. The observed salinity data sets collected from 18 to 22 May, 16 to 22 October, and 26 to 30 October 2002 (totaling 4320 data points) were used for BPNN and RBFNN model training and for hydrodynamic model calibration. The data sets collected from 30 May to 2 June and 11 to 15 November 2002 (totaling 2592 data points) were adopted for BPNN and RBFNN model verification and for hydrodynamic model verification. The results revealed that the ANN (BPNN and RBFNN) models were capable of predicting the nonlinear time series behavior of salinity to the multiple forcing signals of water stages at different stations and freshwater input at upstream boundaries. The salinity predicted by the ANN models was better than that predicted by the physically based hydrodynamic model. This study suggests that BPNN and RBFNN models are easy-to-use modeling tools for simulating the salinity variation in a tidal estuarine system.展开更多
Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffle...Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.展开更多
The turbulence mechanism plays an important part in the mixing process and momentum transfer of turbulence. A three-dimensional Prandtl mixing length tidal model has been developed to simulate tidal flows and water qu...The turbulence mechanism plays an important part in the mixing process and momentum transfer of turbulence. A three-dimensional Prandtl mixing length tidal model has been developed to simulate tidal flows and water quality. The eddy viscosities and diffusivities are computed from the Prandtl mixing length model. In order to model the water quality of an estuary or coastal area many interdependent processes need to be simulated. These may be conveniently separated into three main groups: transport and mixing processes, biochemical interaction of water quality variables and the utilization and re-cycling of nutrients by living matter. The model simulates full oxygen and nutrient balance, primary productivity and the transport, reaction mechanism and fate of pollutants over tidal time-scales. The model is applied to numerical simulation of tidal flows and water quality in Dalian Bay. The model has been calibrated against a limited data set of historical water quality observations and in general demonstrates excellent agreement with all available data.展开更多
To study the rock deformation with three- dimensional model under rolling forces of disc cutter, by car- rying out the circular-grooving test with disc cutter rolling around on the rock, the rock mechanical behavior u...To study the rock deformation with three- dimensional model under rolling forces of disc cutter, by car- rying out the circular-grooving test with disc cutter rolling around on the rock, the rock mechanical behavior under rolling disc cutter is studied, the mechanical model of disc cutter rolling around the groove is established, and the the- ory of single-point and double-angle variables is proposed. Based on this theory, the physics equations and geometric equations of rock mechanical behavior under disc cutters of tunnel boring machine （TBM） are studied, and then the bal- ance equations of interactive forces between disc cutter and rock are established. Accordingly, formulas about normal force, rolling force and side force of a disc cutter are de- rived, and their validity is studied by tests. Therefore, a new method and theory is proposed to study rock- breaking mech- anism of disc cutters.展开更多
The problem of taking an unorganized point cloud in 3D space and fitting a polyhedral surface to those points is both important and difficult. Aiming at increasing applications of full three dimensional digital terrai...The problem of taking an unorganized point cloud in 3D space and fitting a polyhedral surface to those points is both important and difficult. Aiming at increasing applications of full three dimensional digital terrain surface modeling, a new algorithm for the automatic generation of three dimensional triangulated irregular network from a point cloud is pro- posed. Based on the local topological consistency test, a combined algorithm of constrained 3D Delaunay triangulation and region-growing is extended to ensure topologically correct reconstruction. This paper also introduced an efficient neighbor- ing triangle location method by making full use of the surface normal information. Experimental results prove that this algo- rithm can efficiently obtain the most reasonable reconstructed mesh surface with arbitrary topology, wherein the automati- cally reconstructed surface has only small topological difference from the true surface. This algorithm has potential applica- tions to virtual environments, computer vision, and so on.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many ...Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many resources and takes too long to compute,while neural network forecasting lacks regional data to train regional forecasting models.In this study,we used the DUAL wind model to build typhoon wind fields,and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation-Simulating Waves Nearshore(ADCIRC-SWAN)model.Then,a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset,and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region.The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.展开更多
This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV i...This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
Age-related osteoporosis poses a significant challenge in musculoskeletal health;a condition characterized by reduced bone density and increased fracture susceptibility in older individuals necessitates a better under...Age-related osteoporosis poses a significant challenge in musculoskeletal health;a condition characterized by reduced bone density and increased fracture susceptibility in older individuals necessitates a better understanding of underlying molecular and cellular mechanisms.Emerging evidence suggests that osteocytes are the pivotal orchestrators of bone remodeling and represent novel therapeutic targets for age-related bone loss.Our study uses the prematurely aged PolgD257A/D257A(PolgA)mouse model to scrutinize age-and sex-related alterations in musculoskeletal health parameters(frailty,grip strength,gait data),bone and particularly the osteocyte lacuno-canalicular network(LCN).Moreover,a new quantitative in silico image analysis pipeline is used to evaluate the alterations in the osteocyte network with aging.Our findings underscore the pronounced degenerative changes in the musculoskeletal health parameters,bone,and osteocyte LCN in PolgA mice as early as 40 weeks,with more prominent alterations evident in aged males.Our findings suggest that the PolgA mouse model serves as a valuable model for studying the cellular mechanisms underlying age-related bone loss,given the comparable aging signs and age-related degeneration of the bone and the osteocyte network observed in naturally aging mice and elderly humans.展开更多
[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a...[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.展开更多
Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weight...Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.展开更多
In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clu...In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.展开更多
In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models...In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models,some existing discretized continuous neuron models,and discrete neural networks in simulating complex neural dynamics.It places particular emphasis on the importance of memristors in the composition of neural networks,especially their unique memory and nonlinear characteristics.The integration of memristors into discrete neural networks,including Hopfield networks and their fractional-order variants,cellular neural networks and discrete neuron models has enabled the study and construction of various neural models with memory.These models exhibit complex dynamic behaviors,including superchaotic attractors,hidden attractors,multistability,and synchronization transitions.Furthermore,the present paper undertakes an analysis of more complex dynamical properties,including synchronization,speckle patterns,and chimera states in discrete coupled neural networks.This research provides new theoretical foundations and potential applications in the fields of brain-inspired computing,artificial intelligence,image encryption,and biological modeling.展开更多
Wireless Sensor Networks(WSN)have gained significant attention over recent years due to their extensive applications in various domains such as environmentalmonitoring,healthcare systems,industrial automation,and smar...Wireless Sensor Networks(WSN)have gained significant attention over recent years due to their extensive applications in various domains such as environmentalmonitoring,healthcare systems,industrial automation,and smart cities.However,such networks are inherently vulnerable to different types of attacks because they operate in open environments with limited resources and constrained communication capabilities.Thepaper addresses challenges related to modeling and analysis of wireless sensor networks and their susceptibility to attacks.Its objective is to create versatile modeling tools capable of detecting attacks against network devices and identifying anomalies caused either by legitimate user errors or malicious activities.A proposed integrated approach for data collection,preprocessing,and analysis in WSN outlines a series of steps applicable throughout both the design phase and operation stage.This ensures effective detection of attacks and anomalies within WSNs.An introduced attackmodel specifies potential types of unauthorized network layer attacks targeting network nodes,transmitted data,and services offered by the WSN.Furthermore,a graph-based analytical framework was designed to detect attacks by evaluating real-time events from network nodes and determining if an attack is underway.Additionally,a simulation model based on sequences of imperative rules defining behaviors of both regular and compromised nodes is presented.Overall,this technique was experimentally verified using a segment of a WSN embedded in a smart city infrastructure,simulating a wormhole attack.Results demonstrate the viability and practical significance of the technique for enhancing future information security measures.Validation tests confirmed high levels of accuracy and efficiency when applied specifically to detecting wormhole attacks targeting routing protocols in WSNs.Precision and recall rates averaged above the benchmark value of 0.95,thus validating the broad applicability of the proposed models across varied scenarios.展开更多
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 flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal ...The flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal reservoirs.In any case,the traditional techniques of coal rock fracture observation are unable to precisely define the flow of CBM.In this study,coal samples were subjected to an in situ loading scanning test in order to create a pore network model(PNM)and determine the pore and fracture dynamic evolution law of the samples in the loading path.On this basis,the structural characteristic parameters of the samples were extracted from the PNM and the impact on the permeability performance of CBM was assessed.The findings demonstrate that the coal samples'internal porosity increases by 2.039%under uniaxial loading,the average throat pore radius increases by 205.5 to 36.1μm,and the loading has an impact on the distribution and morphology of the pores in the coal rock.The PNM was loaded into the finite element program COMSOL for seepage modeling,and the M3 stage showed isolated pore connectivity to produce microscopic fissures,which could serve as seepage channels.In order to confirm the viability of the PNM and COMSOL docking technology,the streamline distribution law of pressure and velocity fields during the coal sample loading process was examined.The absolute permeability of the coal samples was also obtained in order for comparison with the measured results.The macroscopic CBM flow mechanism in complex lowpermeability coal rocks can be revealed through three-dimensional reconstruction of the microscopic fracture structure and seepage simulation.This study lays the groundwork for the fine description and evaluation of coal reservoirs as well as the precise prediction of gas production in CBM wells.展开更多
The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system...The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.展开更多
基金This work was financially supported by the National Key R&D Program of China(No.2021YFC2900500)the National Natural Science Foundation of China(Nos.52074020 and 42202306)+2 种基金the Open Fund of State Key Laboratory of Water Resource Protection and Utilization in Coal Mining(No.WPUKFJJ2019-06)the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(No.FRF-IDRY-21001)the Natural Science Foundation of Jiangsu Province,China(No.BK20200993).
文摘Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.
文摘The Metropolitan Area Network (MAN) has faced serious problems after years of rapid development. The model of three-dimensional IP-based MAN, proposed by ZTE, is a next-generation MAN solution, which not only solves the existing problems but also brings new ideas for the development of next-generation MAN.
文摘The simulation of salinity at different locations of a tidal river using physically-based hydrodynamic models is quite cumbersome because it requires many types of data, such as hydrological and hydraulic time series at boundaries, river geometry, and adjusted coefficients. Therefore, an artificial neural network (ANN) technique using a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is adopted as an effective alternative in salinity simulation studies. The present study focuses on comparing the performance of BPNN, RBFNN, and three-dimensional hydrodynamic models as applied to a tidal estuarine system. The observed salinity data sets collected from 18 to 22 May, 16 to 22 October, and 26 to 30 October 2002 (totaling 4320 data points) were used for BPNN and RBFNN model training and for hydrodynamic model calibration. The data sets collected from 30 May to 2 June and 11 to 15 November 2002 (totaling 2592 data points) were adopted for BPNN and RBFNN model verification and for hydrodynamic model verification. The results revealed that the ANN (BPNN and RBFNN) models were capable of predicting the nonlinear time series behavior of salinity to the multiple forcing signals of water stages at different stations and freshwater input at upstream boundaries. The salinity predicted by the ANN models was better than that predicted by the physically based hydrodynamic model. This study suggests that BPNN and RBFNN models are easy-to-use modeling tools for simulating the salinity variation in a tidal estuarine system.
基金CAPES and CNPq(Brazilian federal research agencies)for their financial support.
文摘Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.
基金The project is supported by The National Natural Science Foundation of China
文摘The turbulence mechanism plays an important part in the mixing process and momentum transfer of turbulence. A three-dimensional Prandtl mixing length tidal model has been developed to simulate tidal flows and water quality. The eddy viscosities and diffusivities are computed from the Prandtl mixing length model. In order to model the water quality of an estuary or coastal area many interdependent processes need to be simulated. These may be conveniently separated into three main groups: transport and mixing processes, biochemical interaction of water quality variables and the utilization and re-cycling of nutrients by living matter. The model simulates full oxygen and nutrient balance, primary productivity and the transport, reaction mechanism and fate of pollutants over tidal time-scales. The model is applied to numerical simulation of tidal flows and water quality in Dalian Bay. The model has been calibrated against a limited data set of historical water quality observations and in general demonstrates excellent agreement with all available data.
基金supported by the National Natural Science Foundation of China (51075147)
文摘To study the rock deformation with three- dimensional model under rolling forces of disc cutter, by car- rying out the circular-grooving test with disc cutter rolling around on the rock, the rock mechanical behavior under rolling disc cutter is studied, the mechanical model of disc cutter rolling around the groove is established, and the the- ory of single-point and double-angle variables is proposed. Based on this theory, the physics equations and geometric equations of rock mechanical behavior under disc cutters of tunnel boring machine （TBM） are studied, and then the bal- ance equations of interactive forces between disc cutter and rock are established. Accordingly, formulas about normal force, rolling force and side force of a disc cutter are de- rived, and their validity is studied by tests. Therefore, a new method and theory is proposed to study rock- breaking mech- anism of disc cutters.
基金Supported by the National Natural Science Foundation of China （No.40671158）, the National 863 Program of China（No.2006AA12Z224） and the Program for New Century Excellent Talents in University （No.NCET-05-0626）.
文摘The problem of taking an unorganized point cloud in 3D space and fitting a polyhedral surface to those points is both important and difficult. Aiming at increasing applications of full three dimensional digital terrain surface modeling, a new algorithm for the automatic generation of three dimensional triangulated irregular network from a point cloud is pro- posed. Based on the local topological consistency test, a combined algorithm of constrained 3D Delaunay triangulation and region-growing is extended to ensure topologically correct reconstruction. This paper also introduced an efficient neighbor- ing triangle location method by making full use of the surface normal information. Experimental results prove that this algo- rithm can efficiently obtain the most reasonable reconstructed mesh surface with arbitrary topology, wherein the automati- cally reconstructed surface has only small topological difference from the true surface. This algorithm has potential applica- tions to virtual environments, computer vision, and so on.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金supported by the National Natural Science Foundation of China(Grant No.42076214)Natural Science Foundation of Shandong Province(Grant No.ZR2024QD057).
文摘Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many resources and takes too long to compute,while neural network forecasting lacks regional data to train regional forecasting models.In this study,we used the DUAL wind model to build typhoon wind fields,and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation-Simulating Waves Nearshore(ADCIRC-SWAN)model.Then,a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset,and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region.The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RP23066).
文摘This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金the European Research Council(ERC Advanced MechAGE-ERC-2016-ADG-741883)the Swiss National Science Foundation(no.188522).
文摘Age-related osteoporosis poses a significant challenge in musculoskeletal health;a condition characterized by reduced bone density and increased fracture susceptibility in older individuals necessitates a better understanding of underlying molecular and cellular mechanisms.Emerging evidence suggests that osteocytes are the pivotal orchestrators of bone remodeling and represent novel therapeutic targets for age-related bone loss.Our study uses the prematurely aged PolgD257A/D257A(PolgA)mouse model to scrutinize age-and sex-related alterations in musculoskeletal health parameters(frailty,grip strength,gait data),bone and particularly the osteocyte lacuno-canalicular network(LCN).Moreover,a new quantitative in silico image analysis pipeline is used to evaluate the alterations in the osteocyte network with aging.Our findings underscore the pronounced degenerative changes in the musculoskeletal health parameters,bone,and osteocyte LCN in PolgA mice as early as 40 weeks,with more prominent alterations evident in aged males.Our findings suggest that the PolgA mouse model serves as a valuable model for studying the cellular mechanisms underlying age-related bone loss,given the comparable aging signs and age-related degeneration of the bone and the osteocyte network observed in naturally aging mice and elderly humans.
基金supported by the Hebei Provincial Key Research and Development Project(21372803D)。
文摘[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.
基金supported by the Key R&D Projects in Jiangsu Province(BE2021729)the Key Primary Research Project of Primary Strengthening Program(KYZYJKKCJC23001).
文摘Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.
基金partially supported by the National Natural Science Foundation of China(62161016)the Key Research and Development Project of Lanzhou Jiaotong University(ZDYF2304)+1 种基金the Beijing Engineering Research Center of Highvelocity Railway Broadband Mobile Communications(BHRC-2022-1)Beijing Jiaotong University。
文摘In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.
基金supported by the Natural Science Foundation of Hunan Province(Grant No.2025JJ50368)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.24A0248)the Guiding Science and Technology Plan Project of Changsha City(Grant No.kzd2501129)。
文摘In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models,some existing discretized continuous neuron models,and discrete neural networks in simulating complex neural dynamics.It places particular emphasis on the importance of memristors in the composition of neural networks,especially their unique memory and nonlinear characteristics.The integration of memristors into discrete neural networks,including Hopfield networks and their fractional-order variants,cellular neural networks and discrete neuron models has enabled the study and construction of various neural models with memory.These models exhibit complex dynamic behaviors,including superchaotic attractors,hidden attractors,multistability,and synchronization transitions.Furthermore,the present paper undertakes an analysis of more complex dynamical properties,including synchronization,speckle patterns,and chimera states in discrete coupled neural networks.This research provides new theoretical foundations and potential applications in the fields of brain-inspired computing,artificial intelligence,image encryption,and biological modeling.
基金the International Scientific Complex“Astana”was funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan(Grant No.AP19680345).
文摘Wireless Sensor Networks(WSN)have gained significant attention over recent years due to their extensive applications in various domains such as environmentalmonitoring,healthcare systems,industrial automation,and smart cities.However,such networks are inherently vulnerable to different types of attacks because they operate in open environments with limited resources and constrained communication capabilities.Thepaper addresses challenges related to modeling and analysis of wireless sensor networks and their susceptibility to attacks.Its objective is to create versatile modeling tools capable of detecting attacks against network devices and identifying anomalies caused either by legitimate user errors or malicious activities.A proposed integrated approach for data collection,preprocessing,and analysis in WSN outlines a series of steps applicable throughout both the design phase and operation stage.This ensures effective detection of attacks and anomalies within WSNs.An introduced attackmodel specifies potential types of unauthorized network layer attacks targeting network nodes,transmitted data,and services offered by the WSN.Furthermore,a graph-based analytical framework was designed to detect attacks by evaluating real-time events from network nodes and determining if an attack is underway.Additionally,a simulation model based on sequences of imperative rules defining behaviors of both regular and compromised nodes is presented.Overall,this technique was experimentally verified using a segment of a WSN embedded in a smart city infrastructure,simulating a wormhole attack.Results demonstrate the viability and practical significance of the technique for enhancing future information security measures.Validation tests confirmed high levels of accuracy and efficiency when applied specifically to detecting wormhole attacks targeting routing protocols in WSNs.Precision and recall rates averaged above the benchmark value of 0.95,thus validating the broad applicability of the proposed models across varied scenarios.
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The National Key R&D Program,Grant/Award Number:2023YFC2907203National Natural Science Foundation of China,Grant/Award Numbers:52374121,52074121。
文摘The flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal reservoirs.In any case,the traditional techniques of coal rock fracture observation are unable to precisely define the flow of CBM.In this study,coal samples were subjected to an in situ loading scanning test in order to create a pore network model(PNM)and determine the pore and fracture dynamic evolution law of the samples in the loading path.On this basis,the structural characteristic parameters of the samples were extracted from the PNM and the impact on the permeability performance of CBM was assessed.The findings demonstrate that the coal samples'internal porosity increases by 2.039%under uniaxial loading,the average throat pore radius increases by 205.5 to 36.1μm,and the loading has an impact on the distribution and morphology of the pores in the coal rock.The PNM was loaded into the finite element program COMSOL for seepage modeling,and the M3 stage showed isolated pore connectivity to produce microscopic fissures,which could serve as seepage channels.In order to confirm the viability of the PNM and COMSOL docking technology,the streamline distribution law of pressure and velocity fields during the coal sample loading process was examined.The absolute permeability of the coal samples was also obtained in order for comparison with the measured results.The macroscopic CBM flow mechanism in complex lowpermeability coal rocks can be revealed through three-dimensional reconstruction of the microscopic fracture structure and seepage simulation.This study lays the groundwork for the fine description and evaluation of coal reservoirs as well as the precise prediction of gas production in CBM wells.
文摘The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.
