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
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
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
Due to the deterioration of serious energy dilemma,energy-conservation and emission–reduction have been the strategic target in the past decades,thus people have identified the vital importance of higher energy effic...Due to the deterioration of serious energy dilemma,energy-conservation and emission–reduction have been the strategic target in the past decades,thus people have identified the vital importance of higher energy efficiency and the influence of lower carbon development.Since work exchange network is a significant part of energy recovery system,its optima design will have dramatically significant effect on energy consumption reduction in chemical process system.With an extension of the developed transshipment model in isothermal process,a novel step-wise methodology for synthesis of direct work exchange network(WEN)in adiabatic process involving heat integration is first proposed in this paper,where a nonlinear programming(NLP)model is formulated by regarding the minimum utility consumption as objective function and optimizing the initial WEN in accordance with the presented matching rules to get the optimized WEN configuration at first.Furthermore,we focus on the work exchange network synthesis with heat integration to attain the minimal total annual cost(TAC)with the introduction of heat-exchange equipment that is achieved by the following strategies in sequence:introducing heat-exchange equipment directly,adjusting the work quantity of the adjacent utility compressors or expanders,and approximating upper/lower pressure limits consequently to obtain considerable cost savings of expanders or compressors and work utility.Finally,a case taken from the literature is studied to illustrate the feasibility and effectiveness of the proposed method.展开更多
To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which inte...To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.展开更多
In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method a...In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method and targeted long narrow reaches of the river networks, while the 2D model targeted broad channels and embayment and solved the 2D shallow-water equations using a semi-implicit scheme applied to an unstructured grid of triangular cells. The 1D and 2D models were solved simultaneously by building a matrix for the free surface elevation at every 1D junction and 2D cell center. Velocities were then computed explicitly based on the results at the previous time step and the updated water level. The originality of the scheme arose from a novel coupling method. The results showed that the coupled 1D/2D model produced identical results as the full 2D model in classical to benchmark problems with considerable savings in computational effort. Application of the model to the Pearl River Estuary in southern China showed that complex patterns of tidal wave propagation could be efficiently modeled.展开更多
The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example...The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example that has recently gained a lot of scientific attention.It has its roots in social and economic research,as well as the evaluation of network science,such as graph theory.Scientists in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon.The motivation of this study is to understand and capture the clustering properties of large networks and social networks.We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.The random walk technique is paired with a triangle generating scheme in our proposed model.As a result,the clustering controlmechanism and preferential attachment(PA)have been realized.This research builds on the present random walk model.We took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other things.Finally,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
In this paper, a local model network H-infinity control is proposed for CE-150 helicopter stabilization. The proposed strategy capitalizes on recent developments on H-infinity control and its promising results in robu...In this paper, a local model network H-infinity control is proposed for CE-150 helicopter stabilization. The proposed strategy capitalizes on recent developments on H-infinity control and its promising results in robust stabilization of plants under unstructured uncertainties. CE-150 helicopters are known for their varying operating conditions along with external disturbances. Therefore, local model networks are introduced for their adaptive feature and since they provide a powerful combination of fuzzy logic and conventional linear control techniques to control nonlinear systems without the added computational burden of soft-computing techniques. Using the fact that the system can be linearized at different operating points, a mixed sensitivity H-infinity controller is designed for the linearized system, and combined within a network to make transitions between them. The proposed control structure ensures robustness, decoupling of the system dynamics while achieving good performance. A comparison is carried-out against the well-known proportional-integral-derivative （PID） control technique. Results are presented to illustrate the controller＇s performance in various operating conditions.展开更多
One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neu...One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective.展开更多
Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial...Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.展开更多
Empirical data show that most of the degree distribution of airline networks assume a double power law. In this work, firstly, we assume cities as sites, flight between two cities as an edge between two sites, and bui...Empirical data show that most of the degree distribution of airline networks assume a double power law. In this work, firstly, we assume cities as sites, flight between two cities as an edge between two sites, and build a dynamic evolution model for airline networks by improving the BA model, in which the conception of attractiveness plays a decisive role in the course of evolution of the networks. To this end, we discuss whether the attractiveness depends on the site label s or not separately, finally we obtain analytic degree distribution. As a result, if the attractiveness of a site is independent of the degree distribution of sites, which will follow the double power law, otherwise, it will be scale-free. Moreover, degree distribution depends on the parameters of the models, and some parameters aye more sensitive than others.展开更多
Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual conne...Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.展开更多
In this paper,we propose a mathematical model of aggregate co-channel interference over Rayleigh fading in cognitive networks.Unlike the statistical models in the literature that aim at finding the bound or approximat...In this paper,we propose a mathematical model of aggregate co-channel interference over Rayleigh fading in cognitive networks.Unlike the statistical models in the literature that aim at finding the bound or approximation of the interference,the proposed model gives an accurate expression of probability density function(PDF),cumulative distribution function(CDF) and mean and variance of the interference,which takes into account a number of factors,such as spectrum sensing scheme,and spatial distribution of the secondary users(SUs).In particular,we focus on a more general spatial structure where there are two roles of primary users(PUs)and the interfering SUs distributed in the two-dimensional space.The framework developed in this paper is easy to be applied in power control,error evaluation and other applications.展开更多
We investigate the phase transitions behavior of the majority-vote model with noise on a topology that consists of two coupled random networks. A parameter p is used to measure the degree of modularity, defined as the...We investigate the phase transitions behavior of the majority-vote model with noise on a topology that consists of two coupled random networks. A parameter p is used to measure the degree of modularity, defined as the ratio of intermodular to intramodular connectivity. For the networks of strong modularity （small p）, as the level of noise f increases, the system undergoes successively two transitions at two distinct critical noises, fc1 and fc2. The first transition is a discontinuous jump from a coexistence state of parallel and antiparallel order to a state that only parallel order survives, and the second one is continuous that separates the ordered state from a disordered state. As the network modularity worsens, fc1 becomes smaller and fc1 does not change, such that the antiparallel ordered state will vanish if p is larger than a critical value of pc. We propose a mean-field theory to explain the simulation results.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
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
As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve t...As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay.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
Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the ...Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.展开更多
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Supported by the National Natural Science Foundation of China(21576036,21406026)
文摘Due to the deterioration of serious energy dilemma,energy-conservation and emission–reduction have been the strategic target in the past decades,thus people have identified the vital importance of higher energy efficiency and the influence of lower carbon development.Since work exchange network is a significant part of energy recovery system,its optima design will have dramatically significant effect on energy consumption reduction in chemical process system.With an extension of the developed transshipment model in isothermal process,a novel step-wise methodology for synthesis of direct work exchange network(WEN)in adiabatic process involving heat integration is first proposed in this paper,where a nonlinear programming(NLP)model is formulated by regarding the minimum utility consumption as objective function and optimizing the initial WEN in accordance with the presented matching rules to get the optimized WEN configuration at first.Furthermore,we focus on the work exchange network synthesis with heat integration to attain the minimal total annual cost(TAC)with the introduction of heat-exchange equipment that is achieved by the following strategies in sequence:introducing heat-exchange equipment directly,adjusting the work quantity of the adjacent utility compressors or expanders,and approximating upper/lower pressure limits consequently to obtain considerable cost savings of expanders or compressors and work utility.Finally,a case taken from the literature is studied to illustrate the feasibility and effectiveness of the proposed method.
基金Project (No. 60074008) supported by the National Natural Science Foundation of China
文摘To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.
基金financially supporrted by the National Key Research and Development Program of China(Grant No.2017YFC1404200)the National Natural Science Foundation of China(Grant Nos.51779150 and 51979040)
文摘In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method and targeted long narrow reaches of the river networks, while the 2D model targeted broad channels and embayment and solved the 2D shallow-water equations using a semi-implicit scheme applied to an unstructured grid of triangular cells. The 1D and 2D models were solved simultaneously by building a matrix for the free surface elevation at every 1D junction and 2D cell center. Velocities were then computed explicitly based on the results at the previous time step and the updated water level. The originality of the scheme arose from a novel coupling method. The results showed that the coupled 1D/2D model produced identical results as the full 2D model in classical to benchmark problems with considerable savings in computational effort. Application of the model to the Pearl River Estuary in southern China showed that complex patterns of tidal wave propagation could be efficiently modeled.
基金This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493in part by the 2021 Yeungnam University Research Grant。
文摘The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example that has recently gained a lot of scientific attention.It has its roots in social and economic research,as well as the evaluation of network science,such as graph theory.Scientists in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon.The motivation of this study is to understand and capture the clustering properties of large networks and social networks.We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.The random walk technique is paired with a triangle generating scheme in our proposed model.As a result,the clustering controlmechanism and preferential attachment(PA)have been realized.This research builds on the present random walk model.We took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other things.Finally,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘In this paper, a local model network H-infinity control is proposed for CE-150 helicopter stabilization. The proposed strategy capitalizes on recent developments on H-infinity control and its promising results in robust stabilization of plants under unstructured uncertainties. CE-150 helicopters are known for their varying operating conditions along with external disturbances. Therefore, local model networks are introduced for their adaptive feature and since they provide a powerful combination of fuzzy logic and conventional linear control techniques to control nonlinear systems without the added computational burden of soft-computing techniques. Using the fact that the system can be linearized at different operating points, a mixed sensitivity H-infinity controller is designed for the linearized system, and combined within a network to make transitions between them. The proposed control structure ensures robustness, decoupling of the system dynamics while achieving good performance. A comparison is carried-out against the well-known proportional-integral-derivative （PID） control technique. Results are presented to illustrate the controller＇s performance in various operating conditions.
文摘One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective.
文摘Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.
基金Supported by the National Natural Science Foundation of China under Grant No 10975057the Programme of Introducing Talents of Discipline to Universities under Grant No B08033
文摘Empirical data show that most of the degree distribution of airline networks assume a double power law. In this work, firstly, we assume cities as sites, flight between two cities as an edge between two sites, and build a dynamic evolution model for airline networks by improving the BA model, in which the conception of attractiveness plays a decisive role in the course of evolution of the networks. To this end, we discuss whether the attractiveness depends on the site label s or not separately, finally we obtain analytic degree distribution. As a result, if the attractiveness of a site is independent of the degree distribution of sites, which will follow the double power law, otherwise, it will be scale-free. Moreover, degree distribution depends on the parameters of the models, and some parameters aye more sensitive than others.
基金sponsored by the General Program of the National Natural Science Foundation of China(Grant Nos.52079129 and 52209148)the Hubei Provincial General Fund,China(Grant No.2023AFB567)。
文摘Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.
基金the National Natural Science Foundation of China(Nos.61071152 and 61271316)the National Basic Research Program(973)of China(Nos.2010CB731406 and 2013CB329605)the National"Twelfth Five-Year"Plan for Science&Technology Support(No.2012BAH38B04)
文摘In this paper,we propose a mathematical model of aggregate co-channel interference over Rayleigh fading in cognitive networks.Unlike the statistical models in the literature that aim at finding the bound or approximation of the interference,the proposed model gives an accurate expression of probability density function(PDF),cumulative distribution function(CDF) and mean and variance of the interference,which takes into account a number of factors,such as spectrum sensing scheme,and spatial distribution of the secondary users(SUs).In particular,we focus on a more general spatial structure where there are two roles of primary users(PUs)and the interfering SUs distributed in the two-dimensional space.The framework developed in this paper is easy to be applied in power control,error evaluation and other applications.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11405001,11205002 and 11475003
文摘We investigate the phase transitions behavior of the majority-vote model with noise on a topology that consists of two coupled random networks. A parameter p is used to measure the degree of modularity, defined as the ratio of intermodular to intramodular connectivity. For the networks of strong modularity （small p）, as the level of noise f increases, the system undergoes successively two transitions at two distinct critical noises, fc1 and fc2. The first transition is a discontinuous jump from a coexistence state of parallel and antiparallel order to a state that only parallel order survives, and the second one is continuous that separates the ordered state from a disordered state. As the network modularity worsens, fc1 becomes smaller and fc1 does not change, such that the antiparallel ordered state will vanish if p is larger than a critical value of pc. We propose a mean-field theory to explain the simulation results.
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.
基金supported by the Science and Technology Project of State Grid Corporation of China:"Research on the Power-Grid Services Oriented"IP+Optics"Coordination Choreography Technology"
文摘As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay.
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 Project of the Key Open Laboratory of Atmospheric Detection,China Meteorological Administration(2023KLAS02M)the Second Batch of Science and Technology Project of China Meteorological Administration("Jiebangguashuai"):the Research and Development of Short-term and Near-term Warning Products for Severe Convective Weather in Beijing-Tianjin-Hebei Region(CMAJBGS202307).
文摘Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.
