Public data empower the development of digital economy.On the basis of conceptual definition and scope framing,the legal attributes of public data should be used to determine the attribution of the right to use,and to...Public data empower the development of digital economy.On the basis of conceptual definition and scope framing,the legal attributes of public data should be used to determine the attribution of the right to use,and to deduce the mechanism of utilization of public data with public nature as the logical starting point.Based on the current situa⁃tion,the use of public data faces multiple difficulties in terms of normative basis,boundary delimitation,procedural rules,and protection policies.Therefore,publicity should be the core principle and the principle of convenience should be deepened,so as to clarify the boundaries between the use of public data and the protection of personal information rights and interests as well as legitimate commercial behavior.As a single platform can hardly meet the requirements of use,it should optimize the service procedures to adapt to different stages of use,standardize the way of data use super⁃vision,and clarify the supervisory responsibilities of the government and the platform.展开更多
In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a ...In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a distributed data source trust assessment management framework,a trust quantification model,and a dynamic adjustment mechanism are proposed.Themodel integrates the Analytic Hierarchy Process(AHP)and Dempster-Shafer(D-S)evidence theory to determine attribute weights and calculate direct trust values,while the PageRank algorithm is employed to derive indirect trust values.Thedirect and indirect trust values are then combined to compute the comprehensive trust value of the data source.Furthermore,a dynamic adjustment mechanism is introduced to continuously update the comprehensive trust value based on historical assessment data.By leveraging the collaborative efforts of multiple nodes in the distributed network,the proposed framework enables a comprehensive,dynamic,and objective evaluation of data source trustworthiness.Extensive experimental analyses demonstrate that the trust quantification model effectively handles large-scale data source trust assessments,exhibiting both strong trust differentiation capability and high robustness.展开更多
This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key de...This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.展开更多
This study employed multispectral techniques to evaluate fulvic acid(FA)compositional characteristic and elucidate its biodegradation mechanisms during partial nitritation(PN)process.Results showed that FA removal eff...This study employed multispectral techniques to evaluate fulvic acid(FA)compositional characteristic and elucidate its biodegradation mechanisms during partial nitritation(PN)process.Results showed that FA removal efficiency(FRE)decreased from 90.22 to 23.11%when FA concentrations in the reactor were increased from 0 to 162.30 mg/L,and that molecular size,degree of aromatization and humification of the effluent FA macromolecules all increased after treatment.Microbial population analysis indicated that the proliferation of the Comamonas,OLB12 and Thauera exhibit high FA utilization capacity in lower concentrations(<50.59 mg/L),promoting the degradation and removal of macromolecular FA.In addition,the sustained increase in external FA may decrease the abundance of above functional microorganisms,resulting in a rapid drop in FRE.Furthermore,from the genetic perspective,the elevated FA levels restricted carbohydrate(ko00620,ko00010 and ko00020)and nitrogen(HAO,AMO,NIR and NOR)metabolism-related pathways,thereby impeding FA removal and total nitrogen loss associated with N_(2)O emissions.展开更多
As the number of distributed power supplies increases on the user side,smart grids are becoming larger and more complex.These changes bring new security challenges,especially with the widespread adop-tion of data-driv...As the number of distributed power supplies increases on the user side,smart grids are becoming larger and more complex.These changes bring new security challenges,especially with the widespread adop-tion of data-driven control methods.This paper introduces a novel black-box false data injection attack(FDIA)method that exploits the measurement modules of distributed power supplies within smart grids,highlighting its effectiveness in bypassing conventional security measures.Unlike traditional methods that focus on data manipulation within communication networks,this approach directly injects false data at the point of measurement,using a generative adversarial network(GAN)to generate stealthy attack vectors.This method requires no detailed knowledge of the target system,making it practical for real-world attacks.The attack’s impact on power system stability is demonstrated through experiments,high-lighting the significant cybersecurity risks introduced by data-driven algorithms in smart grids.展开更多
In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong Unive...In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.展开更多
The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on ...The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on the publisher's Internet portal on Mar.27,2023 without open access.展开更多
Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) ...Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.展开更多
Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve ...Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.展开更多
Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the ...Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.展开更多
Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment w...Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment with digital twin.In this paper,a datadriven fast calculation method for the temperature field of resin impregnated paper(RIP)bushing used in converter transformer valve-side is proposed,which combines the data dimensionality reduction technology and the surrogate model.After applying the finite element algorithm to obtain the temperature field distribution of RIP bushing under different operation conditions as the input dataset,the proper orthogonal decomposition(POD)algorithm is adopted to reduce the order and obtain the low-dimensional projection of the temperature data.On this basis,the surrogate model is used to construct the mapping relationship between the sensor monitoring data and the low-dimensional projection,so that it can achieve the fast calculation and reconstruction of temperature field distribution.The results show that this method can effectively and quickly calculate the overall temperature field distribution of the RIP bushing.The maximum relative error and the average relative error are less than 4.5%and 0.25%,respectively.The calculation speed is at the millisecond level,meeting the needs of digitalisation of power equipment.展开更多
Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function...Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.展开更多
1 Introduction Sound event detection(SED)aims to identify and locate specific sound event categories and their corresponding timestamps within continuous audio streams.To overcome the limitations posed by the scarcity...1 Introduction Sound event detection(SED)aims to identify and locate specific sound event categories and their corresponding timestamps within continuous audio streams.To overcome the limitations posed by the scarcity of strongly labeled training data,researchers have increasingly turned to semi-supervised learning(SSL)[1],which leverages unlabeled data to augment training and improve detection performance.Among many SSL methods[2-4].展开更多
Objective To explore the medication rules of traditional Chinese medicine(TCM)and mechanism of action of hub herb pairs for treating insomnia.Methods Totally 104 prescriptions were statistically analyzed.The associati...Objective To explore the medication rules of traditional Chinese medicine(TCM)and mechanism of action of hub herb pairs for treating insomnia.Methods Totally 104 prescriptions were statistically analyzed.The association rule algorithm was applied to mine the hub herb pairs.Network pharmacology was utilized to analyze the mechanism of the hub herb pairs,while molecular docking was applied to simulate the interaction between receptors and herb molecules,thereby predicting their binding affinities.Results The most frequently used herbs in TCM prescriptions for treating insomnia included Semen Ziziphi Spinosae,Radix Glycyrrhizae,Radix et Rhizoma Ginseng,and Poria cum Radix Pini.Among them,the most commonly used were the supplementing herbs,followed by heat-clearing,mind-calming,and exterior-releasing ones,with their properties of warm and cold,flavors of sweet,Pungent,and bitter,and meridian tropisms of liver,lungs,spleen,kidneys,heart,and stomach.The hub herb pairs based on the association rules included Radix Astragali-Radix et Rhizoma Ginseng,Rhizoma Chuanxiong-Radix Glycyrrhizae,Seman Platycladi-Semen Ziziphi Spinosae,Pericarpium Citri Reticulatae-Radix Glycyrrhizae,Radix Polygalae-Semen Ziziphi Spinosae,and Radix Astragali-Semen Ziziphi Spinosae.Network pharmacology revealed that the cAMP signaling pathway might play a key role in treating insomnia synergistically with HIF-1 signaling pathway,prolactin signaling pathway,chemical carcinogenesis receptor activation,and PI3K-Akt signaling pathway.Molecular docking indicated that there was good binding between the active ingredients of the hub herb pairs and the hub targets.Conclusions This study identified six hub herb pairs for treating insomnia in TCM.These hub herb pairs may synergistically treat insomnia with HIF-1 signaling pathway,prolactin signaling pathway,chemical carcinogenesis receptor activation,and PI3K-Akt signaling pathway through the cAMP signaling pathway.展开更多
Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity ...Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity reversible data hiding scheme.Based on the advantage of the multipredictor mechanism,we combine two effective prediction schemes to improve prediction accuracy.In addition,the multihistogram technique is utilized to further improve the image quality of the stego image.Moreover,a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram.Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases.展开更多
Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.Th...Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.展开更多
The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for data-drive...The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for data-driven cell array is proposed. Cells can be fired only when the needed data arrives, and cell array can be worked on two modes: fixed execution and reconfiguration. On reconfiguration mode, cell function and data flow direction are changed automatically at run time according to contexts. Simultaneously using an H-tree interconnection network, through pre-storing multiple application mapping contexts in reconfiguration buffer, multiple applications can execute concurrently and context switching time is the minimal. For verifying system performance, some algorithms are selected for mapping onto the proposed structure, and the amount of configuration contexts and execution time are recorded for statistical analysis. The results show that the proposed self-reconfigurable mechanism can reduce the number of contexts efficiently, and has a low computing time.展开更多
In the transition of China’s economy from high-speed growth to high-quality growth in the new era,economic practices are oriented to fostering new growth drivers,developing new industries,and forming new models.Based...In the transition of China’s economy from high-speed growth to high-quality growth in the new era,economic practices are oriented to fostering new growth drivers,developing new industries,and forming new models.Based on the data flow,big data effectively integrates technology,material,fund,and human resource flows and reveals new paths for the development of new growth drivers,new industries and new models.Adopting an analytical framework with"macro-meso-micro"levels,this paper elaborates on the theoretical mechanisms by which big data drives high-quality growth through efficiency improvements,upgrades of industrial structures,and business model innovations.It also explores the practical foundations for big data driven high-quality growth including technological advancements of big data,the development of big data industries,and the formulation of big data strategies.Finally,this paper proposes policy options for big data promoting high-quality growth in terms of developing digital economy,consolidating the infrastructure construction of big data,expediting convergence of big data and the real economy,advocating for a big data culture,and expanding financing options for big data.展开更多
In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely...In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.展开更多
With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests...With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.展开更多
基金Key Project of Scientific Research in Universities in Anhui Province"Legislation Research on Intellectual Property Protection of Data"(2022AH050023)。
文摘Public data empower the development of digital economy.On the basis of conceptual definition and scope framing,the legal attributes of public data should be used to determine the attribution of the right to use,and to deduce the mechanism of utilization of public data with public nature as the logical starting point.Based on the current situa⁃tion,the use of public data faces multiple difficulties in terms of normative basis,boundary delimitation,procedural rules,and protection policies.Therefore,publicity should be the core principle and the principle of convenience should be deepened,so as to clarify the boundaries between the use of public data and the protection of personal information rights and interests as well as legitimate commercial behavior.As a single platform can hardly meet the requirements of use,it should optimize the service procedures to adapt to different stages of use,standardize the way of data use super⁃vision,and clarify the supervisory responsibilities of the government and the platform.
基金funded by Haikou Science and Technology Plan Project(2022-007),in part by key Laboratory of PK System Technologies Research of Hainan,China.
文摘In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a distributed data source trust assessment management framework,a trust quantification model,and a dynamic adjustment mechanism are proposed.Themodel integrates the Analytic Hierarchy Process(AHP)and Dempster-Shafer(D-S)evidence theory to determine attribute weights and calculate direct trust values,while the PageRank algorithm is employed to derive indirect trust values.Thedirect and indirect trust values are then combined to compute the comprehensive trust value of the data source.Furthermore,a dynamic adjustment mechanism is introduced to continuously update the comprehensive trust value based on historical assessment data.By leveraging the collaborative efforts of multiple nodes in the distributed network,the proposed framework enables a comprehensive,dynamic,and objective evaluation of data source trustworthiness.Extensive experimental analyses demonstrate that the trust quantification model effectively handles large-scale data source trust assessments,exhibiting both strong trust differentiation capability and high robustness.
基金supported by Poongsan-KAIST Future Research Center Projectthe fund support provided by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(Grant No.2023R1A2C2005661)。
文摘This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.
基金supported by the Key Research and Development Project of Shandong (Nos.2021CXGC011202,2020CXGC011404,and 2022CXGC021002)the National Natural Science Foundation of China (No.22276006)。
文摘This study employed multispectral techniques to evaluate fulvic acid(FA)compositional characteristic and elucidate its biodegradation mechanisms during partial nitritation(PN)process.Results showed that FA removal efficiency(FRE)decreased from 90.22 to 23.11%when FA concentrations in the reactor were increased from 0 to 162.30 mg/L,and that molecular size,degree of aromatization and humification of the effluent FA macromolecules all increased after treatment.Microbial population analysis indicated that the proliferation of the Comamonas,OLB12 and Thauera exhibit high FA utilization capacity in lower concentrations(<50.59 mg/L),promoting the degradation and removal of macromolecular FA.In addition,the sustained increase in external FA may decrease the abundance of above functional microorganisms,resulting in a rapid drop in FRE.Furthermore,from the genetic perspective,the elevated FA levels restricted carbohydrate(ko00620,ko00010 and ko00020)and nitrogen(HAO,AMO,NIR and NOR)metabolism-related pathways,thereby impeding FA removal and total nitrogen loss associated with N_(2)O emissions.
基金supported by the National Natural Science Foundation of China(62302234).
文摘As the number of distributed power supplies increases on the user side,smart grids are becoming larger and more complex.These changes bring new security challenges,especially with the widespread adop-tion of data-driven control methods.This paper introduces a novel black-box false data injection attack(FDIA)method that exploits the measurement modules of distributed power supplies within smart grids,highlighting its effectiveness in bypassing conventional security measures.Unlike traditional methods that focus on data manipulation within communication networks,this approach directly injects false data at the point of measurement,using a generative adversarial network(GAN)to generate stealthy attack vectors.This method requires no detailed knowledge of the target system,making it practical for real-world attacks.The attack’s impact on power system stability is demonstrated through experiments,high-lighting the significant cybersecurity risks introduced by data-driven algorithms in smart grids.
文摘In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.
文摘The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on the publisher's Internet portal on Mar.27,2023 without open access.
基金supported by the National Natural Science Foundation of China(62373224,62333013,U23A20327)the Natural Science Foundation of Shandong Province(ZR2024JQ021)
文摘Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.
基金the National Natural Science Foundation of China (Grants No. 12072090 and No.12302056) to provide fund for conducting experiments。
文摘Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.
基金supported by the National Natural Science Foundation of China(62325304,U22B2046,62073079,62376029)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)the China Postdoctoral Science Foundation(2023M730255,2024T171123)
文摘Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.
基金supported by China Postdoctoral Science Foundation,Grant 2024M753544Science and Technology Project of CSG,Grant GDKJXM2022106.
文摘Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment with digital twin.In this paper,a datadriven fast calculation method for the temperature field of resin impregnated paper(RIP)bushing used in converter transformer valve-side is proposed,which combines the data dimensionality reduction technology and the surrogate model.After applying the finite element algorithm to obtain the temperature field distribution of RIP bushing under different operation conditions as the input dataset,the proper orthogonal decomposition(POD)algorithm is adopted to reduce the order and obtain the low-dimensional projection of the temperature data.On this basis,the surrogate model is used to construct the mapping relationship between the sensor monitoring data and the low-dimensional projection,so that it can achieve the fast calculation and reconstruction of temperature field distribution.The results show that this method can effectively and quickly calculate the overall temperature field distribution of the RIP bushing.The maximum relative error and the average relative error are less than 4.5%and 0.25%,respectively.The calculation speed is at the millisecond level,meeting the needs of digitalisation of power equipment.
文摘Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.
基金supported by the Zhejiang Provincial Key R&D Program(Nos.2024C01108,2023C01030,2023C01034)the Hangzhou Key R&D Program(Nos.2023SZD0046,2024SZD1A03)the Ningbo Key R&D Program(No.2024Z114).
文摘1 Introduction Sound event detection(SED)aims to identify and locate specific sound event categories and their corresponding timestamps within continuous audio streams.To overcome the limitations posed by the scarcity of strongly labeled training data,researchers have increasingly turned to semi-supervised learning(SSL)[1],which leverages unlabeled data to augment training and improve detection performance.Among many SSL methods[2-4].
基金National Natural Science Foundation of China(82360905)Gansu Provincial University Teachers'Innovation Fund Projects(2023A-092 and 2024B-109).
文摘Objective To explore the medication rules of traditional Chinese medicine(TCM)and mechanism of action of hub herb pairs for treating insomnia.Methods Totally 104 prescriptions were statistically analyzed.The association rule algorithm was applied to mine the hub herb pairs.Network pharmacology was utilized to analyze the mechanism of the hub herb pairs,while molecular docking was applied to simulate the interaction between receptors and herb molecules,thereby predicting their binding affinities.Results The most frequently used herbs in TCM prescriptions for treating insomnia included Semen Ziziphi Spinosae,Radix Glycyrrhizae,Radix et Rhizoma Ginseng,and Poria cum Radix Pini.Among them,the most commonly used were the supplementing herbs,followed by heat-clearing,mind-calming,and exterior-releasing ones,with their properties of warm and cold,flavors of sweet,Pungent,and bitter,and meridian tropisms of liver,lungs,spleen,kidneys,heart,and stomach.The hub herb pairs based on the association rules included Radix Astragali-Radix et Rhizoma Ginseng,Rhizoma Chuanxiong-Radix Glycyrrhizae,Seman Platycladi-Semen Ziziphi Spinosae,Pericarpium Citri Reticulatae-Radix Glycyrrhizae,Radix Polygalae-Semen Ziziphi Spinosae,and Radix Astragali-Semen Ziziphi Spinosae.Network pharmacology revealed that the cAMP signaling pathway might play a key role in treating insomnia synergistically with HIF-1 signaling pathway,prolactin signaling pathway,chemical carcinogenesis receptor activation,and PI3K-Akt signaling pathway.Molecular docking indicated that there was good binding between the active ingredients of the hub herb pairs and the hub targets.Conclusions This study identified six hub herb pairs for treating insomnia in TCM.These hub herb pairs may synergistically treat insomnia with HIF-1 signaling pathway,prolactin signaling pathway,chemical carcinogenesis receptor activation,and PI3K-Akt signaling pathway through the cAMP signaling pathway.
基金funded by National Science Council,Taiwan,the Grant Number is NSC 111-2410-H-167-005-MY2.
文摘Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity reversible data hiding scheme.Based on the advantage of the multipredictor mechanism,we combine two effective prediction schemes to improve prediction accuracy.In addition,the multihistogram technique is utilized to further improve the image quality of the stego image.Moreover,a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram.Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases.
基金supported by the National Natural Science Foundation of China(Grant No.82104701)Science Fund Program for Outstanding Young Scholars in Universities of Anhui Province(Grant No.2022AH030064)+3 种基金Key Project at Central Government Level:the Ability Establishment of Sustainable Use for Valuable Chinese Medicine Resources(Grant No.2060302)Foundation of Anhui Province Key Laboratory of Pharmaceutical Preparation Technology and Application(Grant No.2021KFKT10)China Agriculture Research System of MOF and MARA(Grant No.CARS-21)Talent Support Program of Anhui University of Chinese Medicine(Grant No.2020rcyb007).
文摘Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.
基金the National Natural Science Foundation of China (Nos. 61802304, 61834005, 61772417, 61634004, and 61602377)the Shaanxi Provincial Co-ordination Innovation Project of Science and Technology (No. 2016KTZDGY02-04-02)。
文摘The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for data-driven cell array is proposed. Cells can be fired only when the needed data arrives, and cell array can be worked on two modes: fixed execution and reconfiguration. On reconfiguration mode, cell function and data flow direction are changed automatically at run time according to contexts. Simultaneously using an H-tree interconnection network, through pre-storing multiple application mapping contexts in reconfiguration buffer, multiple applications can execute concurrently and context switching time is the minimal. For verifying system performance, some algorithms are selected for mapping onto the proposed structure, and the amount of configuration contexts and execution time are recorded for statistical analysis. The results show that the proposed self-reconfigurable mechanism can reduce the number of contexts efficiently, and has a low computing time.
基金funded by the Program for “Sanqin Scholar Innovation Teams in Shanxi Province”(SZTZ [2018] No.34)“the Research on the Mechanism,Effect Evaluation,and Policy Support of Replacing Business Tax with VAT In Promoting the Industrial Structure Upgrade of China” funded by the Humanity and Social Science Youth Foundation of the Ministry of Education of China(18YJC790078)“the Evaluation and Study of the Effect of Promoting Industrial Transformation and Upgrade of Shaanxi by Replacing Business Tax with Value-added Tax” funded by the Social Science Foundation Project of Shanxi Province(2017D037)
文摘In the transition of China’s economy from high-speed growth to high-quality growth in the new era,economic practices are oriented to fostering new growth drivers,developing new industries,and forming new models.Based on the data flow,big data effectively integrates technology,material,fund,and human resource flows and reveals new paths for the development of new growth drivers,new industries and new models.Adopting an analytical framework with"macro-meso-micro"levels,this paper elaborates on the theoretical mechanisms by which big data drives high-quality growth through efficiency improvements,upgrades of industrial structures,and business model innovations.It also explores the practical foundations for big data driven high-quality growth including technological advancements of big data,the development of big data industries,and the formulation of big data strategies.Finally,this paper proposes policy options for big data promoting high-quality growth in terms of developing digital economy,consolidating the infrastructure construction of big data,expediting convergence of big data and the real economy,advocating for a big data culture,and expanding financing options for big data.
基金partially supported by the Science and Technology Development Fund,Macao SAR (0050/2020/A1)the National Natural Science Foundation of China (62103411, 72171230)。
文摘In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.
基金supported by the National Natural Science Foundation of China (62272078)。
文摘With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.
