The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legisla...The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legislation to establish their data sovereignty,they are also actively engaging in the negotiation of cross-border data flow rules within international trade agreements to construct data sovereignty.During these negotiations,countries express differing regulatory claims,with some focusing on safeguarding sovereignty and protecting human rights,some prioritizing economic promotion and security assurance,and others targeting traditional and innovative digital trade barriers.These varied approaches reflect the tension between three pairs of values:collectivism and individualism,freedom and security,and tradition and innovation.Based on their distinct value pursuits,three representative models of data sovereignty construction have emerged globally.At the current juncture,when international rules for digital trade are still in their nascent stages,China should timely establish its data sovereignty rules,actively participate in global data sovereignty competition,and balance its sovereignty interests with other interests.Specifically,China should explore the scope of system-acceptable digital trade barriers through free trade zones;integrate domestic and international legal frameworks to ensure the alignment of China’s data governance legislation with its obligations under international trade agreements;and use the development of the“Digital Silk Road”as a starting point to prioritize the formation of digital trade rules with countries participating in the Belt and Road Initiative,promoting the Chinese solutions internationally.展开更多
The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-ro...The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.展开更多
Cross-border data flows not only involve cross-border trade issues,but also severely challenge personal information protection,national data security,and the jurisdiction of justice and enforcement.As the current digi...Cross-border data flows not only involve cross-border trade issues,but also severely challenge personal information protection,national data security,and the jurisdiction of justice and enforcement.As the current digital trade negotiations could not accommodate these challenges,China has initiated the concept of secure cross-border data flow and has launched a dual-track multi-level regulatory system,including control system for overseas transfer of important data,system of crossborder provision of personal information,and system of cross-border data request for justice and enforcement.To explore a global regulatory framework for cross-border data flows,legitimate and controllable cross-border data flows should be promoted,supervision should be categorized based on risk concerned,and the rule of law should be coordinated at home and abroad to promote system compatibility.To this end,the key is to build a compatible regulatory framework,which includes clarifying the scope of important data to define the“Negative List”for preventing national security risks,improving the cross-border accountability for protecting personal information rights and interests to ease pre-supervision pressure,and focusing on data access rights instead of data localization for upholding the jurisdiction of justice and enforcement.展开更多
The regulations of cross-border data flows is a growing challenge for the international community.International trade agreements,however,appear to be pioneering legal methods to cope,as they have grappled with this is...The regulations of cross-border data flows is a growing challenge for the international community.International trade agreements,however,appear to be pioneering legal methods to cope,as they have grappled with this issue since the 1990s.The World Trade Organization(WTO)rules system offers a partial solution under the General Agreement on Trade in Services(GATS),which covers aspects related to cross-border data flows.The Comprehensive and Progressive Agreement for Trans-Pacific Partnership(CPTPP)and the United States-Mexico-Canada Agreement(USMCA)have also been perceived to provide forward-looking resolutions.In this context,this article analyzes why a resolution to this issue may be illusory.While they regulate cross-border data flows in various ways,the structure and wording of exception articles of both the CPTPP and USMCA have the potential to pose significant challenges to the international legal system.The new system,attempting to weigh societal values and economic development,is imbalanced,often valuing free trade more than individual online privacy and cybersecurity.Furthermore,the inclusion of poison-pill clauses is,by nature,antithetical to cooperation.Thus,for the international community generally,and China in particular,cross-border data flows would best be regulated under the WTO-centered multilateral trade law system.展开更多
Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a sign...Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.展开更多
A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow acc...A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.展开更多
Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security asses...Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.展开更多
Based on the global asset portfolio model,this paper created a panel threshold model using EPFR fund data to empirically test the non-linear spillover effects of US economic policy uncertainties on cross-border capita...Based on the global asset portfolio model,this paper created a panel threshold model using EPFR fund data to empirically test the non-linear spillover effects of US economic policy uncertainties on cross-border capital flow for emerging economies.Our study led to the following findings:(1)When the level of global investor risk tolerance is high,rising US EPU will induce a capital inflow into emerging economies,as manifested in the“portfolio rebalancing effect.”When the level of global investor risk tolerance is below a critical threshold,this gives rise to risk aversion and emerging economies will experience net capital outflow,i.e.the“flight to quality effect”.(2)Equity fund investors have a lower risk tolerance threshold than bond fund investors.(3)According to our heterogeneity analysis,more attention should be paid to monitoring capital flow through actively managed funds,ETF funds,and retail investor funds.The economy should increase financial efficiency and economic resiliency to mitigate capital outflow pressures from the external environment.展开更多
This paper introduces MultiPHydro,an in-house computational solver developed for simulating hydrodynamic and multiphase fluid—body interaction problems,with a specialized focus on multiphase flow dynamics.The solver ...This paper introduces MultiPHydro,an in-house computational solver developed for simulating hydrodynamic and multiphase fluid—body interaction problems,with a specialized focus on multiphase flow dynamics.The solver employs the boundary data immersion method(BDIM)as its core numerical framework for handling fluid—solid interfaces.We briefly outline the governing equations and physical models integrated within MultiPHydro,including weakly-compressible flows,cavitation modeling,and the volume of fluid(VOF)method with piecewise-linear interface reconstruction.The solver’s accuracy and versatility are demonstrated through several numerical benchmarks:single-phase flow past a cylinder shows less than 10%error in vortex shedding frequency and under 4%error in hydrodynamic resistance;cavitating flows around a hydrofoil yield errors below 7%in maximum cavity length;water-entry cases exhibit under 5%error in displacement and velocity;and water-exit simulations predict cavity length within 7.2%deviation.These results confirm the solver’s capability to reliably model complex fluid-body interactions across various regimes.Future developments will focus on refining mathematical models,improving the modeling of phase-interaction mechanisms,and implementing GPU-accelerated parallel algorithms to enhance compatibility with domestically-developed operating systems and deep computing units(DCUs).展开更多
In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Fir...In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Firstly,this paper introduces the big data-driven forecast model of scenic spot passenger flow.Based on the traditional autoregressive integral moving average model and artificial neural network model,it builds a big data analysis and forecast model.Through the analysis of data source,model building,scenic spot passenger flow accuracy,and modeling time comparison,it affirms the advantages of big data analysis in forecasting scenic spot passenger flow.Finally,it puts forward four commercial operation optimization strategies:adjusting the ticket pricing of scenic spots,upgrading the catering and accommodation services in scenic spots,planning and designing play projects,and formulating accurate scenic spot marketing strategies,in order to provide references for the optimization and upgrading of smart tourist attractions in the future.展开更多
In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However...In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.展开更多
In section‘Track decoding’of this article,one of the paragraphs was inadvertently missed out after the text'…shows the flow diagram of the Tr2-1121 track mode.'The missed paragraph is provided below.
Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the ...Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the influence of international trade.Based on big data technology,this paper builds an industry chain with cross-border e-commerce members'participation,and analyzes the specific application of big data in the product support,internal operation,external marketing,logistics service and service evaluation of cross-border e-commerce industry chain.The purpose is to effectively promote the healthy development of cross-border e-commerce and improve China's trade and economic level.展开更多
Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a n...Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.展开更多
Debris flows are the one type of natural disaster that is most closely associated with hu- man activities. Debris flows are characterized as being widely distributed and frequently activated. Rainfall is an important ...Debris flows are the one type of natural disaster that is most closely associated with hu- man activities. Debris flows are characterized as being widely distributed and frequently activated. Rainfall is an important component of debris flows and is the most active factor when debris flows oc- cur. Rainfall also determines the temporal and spatial distribution characteristics of the hazards. A reasonable rainfall threshold target is essential to ensuring the accuracy of debris flow pre-warning. Such a threshold is important for the study of the mechanisms of debris flow formation, predicting the characteristics of future activities and the design of prevention and engineering control measures. Most mountainous areas have little data regarding rainfall and hazards, especially in debris flow forming re- gions. Therefore, both the traditional demonstration method and frequency calculated method cannot satisfy the debris flow pre-warning requirements. This study presents the characteristics of pre-warning regions, included the rainfall, hydrologic and topographic conditions. An analogous area with abundant data and the same conditions as the pre-warning region was selected, and the rainfall threshold was calculated by proxy. This method resolved the problem of debris flow pre-warning in ar- eas lacking data and provided a new approach for debris flow pre-warning in mountainous areas.展开更多
Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate s...Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper means.Nevertheless,effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity.In this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse data.The presented approach consists of two modules,pre-training module and fine-tuning module.The pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training data.In the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the model.We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data.The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.展开更多
Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management depar...Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.展开更多
基金This paper is a phased result of the“Research on the Issue of China’s Data Export System”(24SFB3035)a research project of the Ministry of Justice of China on the construction of the rule of law and the study of legal theories at the ministerial level in 2024.
文摘The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legislation to establish their data sovereignty,they are also actively engaging in the negotiation of cross-border data flow rules within international trade agreements to construct data sovereignty.During these negotiations,countries express differing regulatory claims,with some focusing on safeguarding sovereignty and protecting human rights,some prioritizing economic promotion and security assurance,and others targeting traditional and innovative digital trade barriers.These varied approaches reflect the tension between three pairs of values:collectivism and individualism,freedom and security,and tradition and innovation.Based on their distinct value pursuits,three representative models of data sovereignty construction have emerged globally.At the current juncture,when international rules for digital trade are still in their nascent stages,China should timely establish its data sovereignty rules,actively participate in global data sovereignty competition,and balance its sovereignty interests with other interests.Specifically,China should explore the scope of system-acceptable digital trade barriers through free trade zones;integrate domestic and international legal frameworks to ensure the alignment of China’s data governance legislation with its obligations under international trade agreements;and use the development of the“Digital Silk Road”as a starting point to prioritize the formation of digital trade rules with countries participating in the Belt and Road Initiative,promoting the Chinese solutions internationally.
基金a preliminary result of the Chinese Government Scholarship High-level Graduate Program sponsored by China Scholarship Council(Program No.CSC202206310052)。
文摘The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.
基金This article is funded by National Social Science Foundation’s general project“Theoretical and Practical Research on International Criminal Judicial Assistance in Combating Cybercrime”(Project No.:19BFX073)National Social Science Foundation’s major project“Translation,Research and Database Construction of Cyberspace Policies and Regulations”(Project No.:20&ZD179).
文摘Cross-border data flows not only involve cross-border trade issues,but also severely challenge personal information protection,national data security,and the jurisdiction of justice and enforcement.As the current digital trade negotiations could not accommodate these challenges,China has initiated the concept of secure cross-border data flow and has launched a dual-track multi-level regulatory system,including control system for overseas transfer of important data,system of crossborder provision of personal information,and system of cross-border data request for justice and enforcement.To explore a global regulatory framework for cross-border data flows,legitimate and controllable cross-border data flows should be promoted,supervision should be categorized based on risk concerned,and the rule of law should be coordinated at home and abroad to promote system compatibility.To this end,the key is to build a compatible regulatory framework,which includes clarifying the scope of important data to define the“Negative List”for preventing national security risks,improving the cross-border accountability for protecting personal information rights and interests to ease pre-supervision pressure,and focusing on data access rights instead of data localization for upholding the jurisdiction of justice and enforcement.
基金This article is supported by the National Social Science Fund Project"China's Non-Market Economy Status in WTO Trade Remedies"(Project No.15XFX023)the Human Rights Institute of Southwest University of Political Science and Law(SWUPL HRI)2015 Yearly Research Project"Global Human Rights Governance under the TPP."All mistakes and omissions are my responsibility.
文摘The regulations of cross-border data flows is a growing challenge for the international community.International trade agreements,however,appear to be pioneering legal methods to cope,as they have grappled with this issue since the 1990s.The World Trade Organization(WTO)rules system offers a partial solution under the General Agreement on Trade in Services(GATS),which covers aspects related to cross-border data flows.The Comprehensive and Progressive Agreement for Trans-Pacific Partnership(CPTPP)and the United States-Mexico-Canada Agreement(USMCA)have also been perceived to provide forward-looking resolutions.In this context,this article analyzes why a resolution to this issue may be illusory.While they regulate cross-border data flows in various ways,the structure and wording of exception articles of both the CPTPP and USMCA have the potential to pose significant challenges to the international legal system.The new system,attempting to weigh societal values and economic development,is imbalanced,often valuing free trade more than individual online privacy and cybersecurity.Furthermore,the inclusion of poison-pill clauses is,by nature,antithetical to cooperation.Thus,for the international community generally,and China in particular,cross-border data flows would best be regulated under the WTO-centered multilateral trade law system.
文摘Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.
文摘A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.
基金support from the National Natural Science Foundation of China(Grant No.:71901169)the Shaanxi Province Innovative Talents Promotion Plan-Youth Science and Technology Nova Project(Grant No.:2022KJXX-50).
文摘Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.
基金sponsored by the Natural Science Foundation of China(NSFC)2018 Emergency Management Project“Exchange Rate Market Variation,Cross-Border Capital Flow and Financial Risk Prevention”(Grant No.71850005)the NSFC Youth Program“Dynamic Estimation of Foreign Exchange Market Pressure in the Process of Capital Account Opening and Evaluation of the Central Bank’s Intervention Policy Effects”(Grant No.71803204).
文摘Based on the global asset portfolio model,this paper created a panel threshold model using EPFR fund data to empirically test the non-linear spillover effects of US economic policy uncertainties on cross-border capital flow for emerging economies.Our study led to the following findings:(1)When the level of global investor risk tolerance is high,rising US EPU will induce a capital inflow into emerging economies,as manifested in the“portfolio rebalancing effect.”When the level of global investor risk tolerance is below a critical threshold,this gives rise to risk aversion and emerging economies will experience net capital outflow,i.e.the“flight to quality effect”.(2)Equity fund investors have a lower risk tolerance threshold than bond fund investors.(3)According to our heterogeneity analysis,more attention should be paid to monitoring capital flow through actively managed funds,ETF funds,and retail investor funds.The economy should increase financial efficiency and economic resiliency to mitigate capital outflow pressures from the external environment.
文摘This paper introduces MultiPHydro,an in-house computational solver developed for simulating hydrodynamic and multiphase fluid—body interaction problems,with a specialized focus on multiphase flow dynamics.The solver employs the boundary data immersion method(BDIM)as its core numerical framework for handling fluid—solid interfaces.We briefly outline the governing equations and physical models integrated within MultiPHydro,including weakly-compressible flows,cavitation modeling,and the volume of fluid(VOF)method with piecewise-linear interface reconstruction.The solver’s accuracy and versatility are demonstrated through several numerical benchmarks:single-phase flow past a cylinder shows less than 10%error in vortex shedding frequency and under 4%error in hydrodynamic resistance;cavitating flows around a hydrofoil yield errors below 7%in maximum cavity length;water-entry cases exhibit under 5%error in displacement and velocity;and water-exit simulations predict cavity length within 7.2%deviation.These results confirm the solver’s capability to reliably model complex fluid-body interactions across various regimes.Future developments will focus on refining mathematical models,improving the modeling of phase-interaction mechanisms,and implementing GPU-accelerated parallel algorithms to enhance compatibility with domestically-developed operating systems and deep computing units(DCUs).
文摘In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Firstly,this paper introduces the big data-driven forecast model of scenic spot passenger flow.Based on the traditional autoregressive integral moving average model and artificial neural network model,it builds a big data analysis and forecast model.Through the analysis of data source,model building,scenic spot passenger flow accuracy,and modeling time comparison,it affirms the advantages of big data analysis in forecasting scenic spot passenger flow.Finally,it puts forward four commercial operation optimization strategies:adjusting the ticket pricing of scenic spots,upgrading the catering and accommodation services in scenic spots,planning and designing play projects,and formulating accurate scenic spot marketing strategies,in order to provide references for the optimization and upgrading of smart tourist attractions in the future.
基金funding support from the National Natural Science Foundation of China(No.52204065,No.ZX20230398)supported by a grant from the Human Resources Development Program(No.20216110100070)of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)。
文摘In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.
文摘In section‘Track decoding’of this article,one of the paragraphs was inadvertently missed out after the text'…shows the flow diagram of the Tr2-1121 track mode.'The missed paragraph is provided below.
文摘Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the influence of international trade.Based on big data technology,this paper builds an industry chain with cross-border e-commerce members'participation,and analyzes the specific application of big data in the product support,internal operation,external marketing,logistics service and service evaluation of cross-border e-commerce industry chain.The purpose is to effectively promote the healthy development of cross-border e-commerce and improve China's trade and economic level.
文摘Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.
基金supported by the National Natural Science Foundation of China(Nos.40830742 and 40901007)
文摘Debris flows are the one type of natural disaster that is most closely associated with hu- man activities. Debris flows are characterized as being widely distributed and frequently activated. Rainfall is an important component of debris flows and is the most active factor when debris flows oc- cur. Rainfall also determines the temporal and spatial distribution characteristics of the hazards. A reasonable rainfall threshold target is essential to ensuring the accuracy of debris flow pre-warning. Such a threshold is important for the study of the mechanisms of debris flow formation, predicting the characteristics of future activities and the design of prevention and engineering control measures. Most mountainous areas have little data regarding rainfall and hazards, especially in debris flow forming re- gions. Therefore, both the traditional demonstration method and frequency calculated method cannot satisfy the debris flow pre-warning requirements. This study presents the characteristics of pre-warning regions, included the rainfall, hydrologic and topographic conditions. An analogous area with abundant data and the same conditions as the pre-warning region was selected, and the rainfall threshold was calculated by proxy. This method resolved the problem of debris flow pre-warning in ar- eas lacking data and provided a new approach for debris flow pre-warning in mountainous areas.
基金supported by the funding of the Key Laboratory of Aerodynamic Noise Control(No.ANCL20190103)the State Key Laboratory of Aerodynamics,China(No.SKLA20180102)+1 种基金the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD).
文摘Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper means.Nevertheless,effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity.In this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse data.The presented approach consists of two modules,pre-training module and fine-tuning module.The pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training data.In the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the model.We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data.The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.
基金Project supported by the Program of Humanities and Social Science of the Education Ministry of China(Grant No.20YJA630008)the Natural Science Foundation of Zhejiang Province,China(Grant No.LY20G010004)the K C Wong Magna Fund in Ningbo University,China。
文摘Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.
