Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision...Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.展开更多
With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates...With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates.Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads.Meanwhile,because of the coupling between consensus and execution,a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution.However,we observe that transaction partitioning and subsequent consensus do not require actual account data but only need to determine the relative partition order between shards.Therefore,we propose a novel sharded blockchain,called HATLedger,based on Hybrid Account and Transaction partitioning.First,HATLedger proposes building a future transaction graph to detect upcoming hotspot accounts and making more precise account partitioning to reduce transaction cross-shard rates.In the event of an impending overload,the source shard employs simulated partition transactions to specify the partition order across multiple target shards,thereby rapidly partitioning the pending transactions.The target shards can reach consensus on received transactions without waiting for account data.The source shard subsequently sends the account data to the corresponding target shards in the order specified by the previously simulated partition transactions.Based on real transaction history from Ethereum,we conducted extensive sharding scalability experiments.By maintaining low cross-shard rates and a relatively balanced load distribution,HATLedger achieves throughput improvements of 2.2x,1.9x,and 1.8x over SharPer,Shard Scheduler,and TxAllo,respectively,significantly enhancing efficiency and scalability.展开更多
Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps oft...Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.展开更多
Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the constru...Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the construction and optimization of a prediction model for deformation prediction,a multipoint ultrahigh arch dam deformation prediction model,namely,the CEEMDAN-KPCA-GSWOA-KELM,which is based on a clustering partition,is pro-posed.First,the monitoring data are preprocessed via variational mode decomposition(VMD)and wavelet denoising(WT),which effectively filters out noise and improves the signal-to-noise ratio of the data,providing high-quality input data for subsequent prediction models.Second,scientific cluster partitioning is performed via the K-means++algorithm to precisely capture the spatial distribution characteristics of extra-high arch dams and ensure the consistency of deformation trends at measurement points within each partition.Finally,CEEMDAN is used to separate monitoring data,predict and analyze each component,combine the KPCA(Kernel Principal Component Analysis)and the KELM(Kernel Extreme Learning Machine)optimized by the GSWOA(Global Search Whale Optimization Algorithm),integrate the predictions of each component via reconstruction methods,and precisely predict the overall trend of ultrahigh arch dam deformation.An extra high arch dam project is taken as an example and validated via a comparative analysis of multiple models.The results show that the multipoint deformation prediction model in this paper can combine data from different measurement points,achieve a comprehensive,precise prediction of the deformation situation of extra high arch dams,and provide strong technical support for safe operation.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
Hydrogen partitioning between liquid iron alloys and silicate melts governs its distribution and cycling in Earth’s deep interior.Existing models based on simplified Fe-H systems predict strong hydrogen sequestration...Hydrogen partitioning between liquid iron alloys and silicate melts governs its distribution and cycling in Earth’s deep interior.Existing models based on simplified Fe-H systems predict strong hydrogen sequestration into the core.However,these models do not account for the modulating effects of major light elements such as oxygen and silicon in the core during Earth’s primordial differentiation.In this study,we use first-principles molecular dynamics simulations,augmented by machine learning techniques,to quantify hydrogen chemical potentials in quaternary Fe-O-Si-H systems under early core-mantle boundary conditions(135 GPa,5000 K).Our results demonstrate that the presence of 5.2 wt%oxygen and 4.8 wt%silicon reduces the siderophile affinity of hydrogen by 35%,decreasing its alloy-silicate partition coefficient from 18.2(in the case of Fe-H)to 11.8(in the case of Fe-O-Si-H).These findings suggest that previous estimates of the core hydrogen content derived from binary system models require downward revision.Our study underscores the critical role of multicomponent interactions in core formation models and provides first-principles-derived constraints to reconcile Earth’s present-day hydrogen reservoirs with its accretionary history.展开更多
In this paper,we examine the functions a(n)and b(n),which respectively represent the number of cubic partitions and cubic partition pairs.Our work leads to the derivation of asymptotic formulas for both a(n)and b(n).A...In this paper,we examine the functions a(n)and b(n),which respectively represent the number of cubic partitions and cubic partition pairs.Our work leads to the derivation of asymptotic formulas for both a(n)and b(n).Additionally,we establish the upper and lower bounds of these functions,factoring in the explicit error terms involved.Crucially,our findings reveal that a(n)and b(n)both satisfy several inequalities such as log-concavity,third-order Turan inequalities,and strict log-subadditivity.展开更多
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
For a positive integer s,a partition is said to be s-core if its hook length set avoids hook length s.The theory of s-core partitions has intriguing applications in representation theory,number theory,and combinatoric...For a positive integer s,a partition is said to be s-core if its hook length set avoids hook length s.The theory of s-core partitions has intriguing applications in representation theory,number theory,and combinatorics.Analogous to the work of Xiong on the largest size of an(s,s+1,…,s+k)-core partition,we evaluate the largest size of a self-conjugate(s,s+1,…,s+k)-core partition for given positive integers s and k.This generalizes the result on the largest size of a self-conjugate(s,s+1,…,s+k)-core partition,which is obtained by Baek,Nam,and Yu by employing Johnson’s bijection.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
To obtain materials capable of efficiently separating acetylene(C_(2)H_(2))from carbon dioxide(CO_(2))and eth-ylene(C_(2)H_(4)),In this work,based on the pore space partition strategy,a pacs-metal-organic framework(MO...To obtain materials capable of efficiently separating acetylene(C_(2)H_(2))from carbon dioxide(CO_(2))and eth-ylene(C_(2)H_(4)),In this work,based on the pore space partition strategy,a pacs-metal-organic framework(MOF):(NH_(2)Me_(2))_(2)[Fe_(3)(μ_(3)-O)(bdc)_(3)][In(FA)_(3)Cl_(3)](Fe‑FAIn‑bdc)was synthesized successfully by using the metal-formate com-plex[In(FA)_(3)Cl_(3)]^(3-)as the pore partition units,where bdc^(2-)=terephthalate,FA-=formate.Owing to the pore partition effect of this metal-organic building block,fruitful confined spaces are formed in the network of Fe‑FAIn‑bdc,endowing this MOF with superior separation performance of acetylene and carbon dioxide.According to the adsorp-tion test,this MOF exhibited a high adsorption capacity for C_(2)H_(2)(50.79 cm^(3)·g^(-1))at 298 K and 100 kPa,which was much higher than that for CO_(2)(29.99 cm^(3)·g^(-1))and C_(2)H_(4)(30.94 cm^(3)·g^(-1))under the same conditions.Ideal adsorbed solution theory(IAST)calculations demonstrate that the adsorption selectivity of Fe‑FAIn‑bdc for the mixture of C_(2)H_(2)/CO_(2)and C_(2)H_(2)/C_(2)H_(4)in a volume ratio of 50∶50 was 3.08 and 3.65,respectively,which was higher than some reported MOFs such as NUM-11 and SNNU-18.CCDC:_(2)453954.展开更多
Two-phase partitioning bioreactors(TPPBs)have been widely used because they overcome the mass-transfer limitation of hydrophobic volatile organic compounds(VOCs)in waste gas biological treatments.Understanding the mec...Two-phase partitioning bioreactors(TPPBs)have been widely used because they overcome the mass-transfer limitation of hydrophobic volatile organic compounds(VOCs)in waste gas biological treatments.Understanding the mechanisms of mass-transfer enhancement in TPPBs would enable efficient predictions for further industrial applications.In this study,influences of gradually increasing silicone oil ratio on the TPPB was explored,and a 94.35%reduction of the n-hexane partition coefficient was observed with 0.1 vol.%silicone,which increased to 80.7%along with a 40-fold removal efficiency enhancement in the stabilised removal period.The elimination capacity increased from 1.47 to 148.35 g/(m^(3)·h),i.e.a 101-fold increase compared with that of the single-phase reactors,when 10 vol.%(3 Critical Micelle Concentration)silicone oil was added.The significantly promoted partition coefficient was the main reason for the mass transfer enhancement,which covered the negative influences of the decreased total mass-transfer coefficient with increasing silicone oil volume ratio.The gradually rising stirring rate was benefit to the n-hexane removal,which became negative when the dominant resistance shifted from mass transfer to biodegradation.Moreover,a mass-transfer-reaction kinetic model of the TPPB was constructed based on the balance of n-hexane concentration,dissolved oxygen and biomass.Similar to the mechanism,the partition factor was predicted sensitive to the removal performance,and another five sensitive parameters were found simultaneously.This forecasting method enables the optimisation of TPPB performance and provides theoretical support for hydrophobic VOCs degradation.展开更多
We studied the energy partition between two well-separated fission fragments associated with the partition of nucleons owing to quantum entanglement.This is different from most fission models that invoke an explicit s...We studied the energy partition between two well-separated fission fragments associated with the partition of nucleons owing to quantum entanglement.This is different from most fission models that invoke an explicit statistical partition of excitation energies.The dynamical fission evolution is described within the time-dependent Hartree-Fock+BCS framework.Excitation energies of isotopic fission fragments were obtained using the particle number projection method after the dynamical splitting of^(238)U.The resulting excitation energies of the light and heavy fragments are consistent with the appearance of sawtooth structures.We found that the pairing correlation strengths have a significant influence on the partition of the excitation energies.Furthermore,the excitation energies of isotopic fragments increase with increasing neutron number,implying the suppression of the production of neutron-rich beams in rare-isotope beam facilities.展开更多
This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the i...This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the impact of wakes on turbine effective wind speed is analyzed,leading to a quantitative method for assessing wake interactions.Based on these interactions,a partitioning method divides the wind farm into smaller,computationally manageable zones.Subsequently,a heuristic control algorithm is developed for yaw optimization within each partition,reducing the overall computational burden associated with multi-turbine optimization.The algorithm’s effectiveness is evaluated through case studies on 11-turbine and 28-turbine wind farms,demonstrating power generation increases of 9.78%and 1.78%,respectively,compared to baseline operation.The primary innovation lies in coupling the higher-fidelity dynamic FLORIDyn wake model with a graph-based partitioning strategy and a computationally efficient heuristic optimization,enabling scalable and accurate yaw control for large wind farms,overcoming limitations associated with simplified models or centralized optimization approaches.展开更多
Strong and ductile Al alloys and their suitable design strategy have long been desired in selective laser melting(SLM).This work reports a non-equilibrium partitioning model and a correspondingly designed Al–7.5Mg–0...Strong and ductile Al alloys and their suitable design strategy have long been desired in selective laser melting(SLM).This work reports a non-equilibrium partitioning model and a correspondingly designed Al–7.5Mg–0.5Sc–0.3Zr–0.6Si alloy.This model effectively quantifies the influence of Mg and Si on hot cracking in aluminum alloy by considering the non-equilibrium partitioning under high cooling rates in SLM.The designed Al–7.5Mg–0.5Sc–0.3Zr–0.6Si alloy exhibits no hot cracks and achieves a remarkably enhanced strength–ductility synergy(a yield strength of(412±8)MPa and a uniform elongation of(15.6±0.6)%),superior to previously reported Al–Mg–Sc–Zr and Al–Mn alloys.A tensile cracking model is proposed to explore the origin of the improved ductility.Both the non-equilibrium partitioning model and the novel Al–7.5Mg–0.5Sc–0.3Zr–0.6Si alloy offers a promising opportunity for producing highly reliable aluminum parts through SLM.展开更多
In large-scaleWireless Rechargeable SensorNetworks(WRSN),traditional forward routingmechanisms often lead to reduced energy efficiency.To address this issue,this paper proposes a WRSN node energy optimization algorith...In large-scaleWireless Rechargeable SensorNetworks(WRSN),traditional forward routingmechanisms often lead to reduced energy efficiency.To address this issue,this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing.The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area.Then,the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission.Relay nodes are selected layer by layer,starting from the outermost cluster heads.Finally,the inter-layer routing mechanism is integrated with regional partitioning and clustering methods to develop the WRSN energy optimization algorithm.To further optimize the algorithm’s performance,we conduct parameter optimization experiments on the relay routing selection function,cluster head rotation energy threshold,and inter-layer relay structure selection,ensuring the best configurations for energy efficiency and network lifespan.Based on these optimizations,simulation results demonstrate that the proposed algorithm outperforms traditional forward routing,K-CHRA,and K-CLP algorithms in terms of node mortality rate and energy consumption,extending the number of rounds to 50%node death by 11.9%,19.3%,and 8.3%in a 500-node network,respectively.展开更多
The correlation between the microstructure,properties,and strain partitioning behavior in a medium-carbon carbide-free bainitic steel was investigated through a combination of experiments and representative volume ele...The correlation between the microstructure,properties,and strain partitioning behavior in a medium-carbon carbide-free bainitic steel was investigated through a combination of experiments and representative volume element simulations.The results reveal that as the austempering temperature increases from low to intermediate,the optimal balance of properties shifts from strength-toughness to plasticity-toughness.The formation of fine bainitic ferrite plates and bainite sheaves under low austempering temperature(270℃)enhances both strength and toughness.Conversely,the wide size and shape distribution of the retained austenite(RA)obtained through austempering at intermediate temperature(350℃)contribute to increased work-hardening capacity,resulting in enhanced plasticity.The volume fraction of the ductile film-like RA plays a crucial role in enhancing impact toughness under relatively higher austempering temperatures.In the simulations of tensile deformation,the concentration of equivalent plastic strain predominantly manifests in the bainitic ferrite neighboring the martensite,whereas the equivalent plastic strain evenly spreads between the thin film-like retained austenite and bainitic ferrite.It is predicted that the cracks will occur at the interface between martensite and bainitic ferrite where the strain is concentrated,and eventually propagate along the strain failure zone.展开更多
In the current noisy intermediate-scale quantum(NISQ)era,a single quantum processing unit(QPU)is insufficient to implement large-scale quantum algorithms;this has driven extensive research into distributed quantum com...In the current noisy intermediate-scale quantum(NISQ)era,a single quantum processing unit(QPU)is insufficient to implement large-scale quantum algorithms;this has driven extensive research into distributed quantum computing(DQC).DQC involves the cooperative operation of multiple QPUs but is concurrently challenged by excessive communication complexity.To address this issue,this paper proposes a quantum circuit partitioning method based on spectral clustering.The approach transforms quantum circuits into weighted graphs and,through computation of the Laplacian matrix and clustering techniques,identifies candidate partition schemes that minimize the total weight of the cut.Additionally,a global gate search tree strategy is introduced to meticulously explore opportunities for merged transfer of global gates,thereby minimizing the transmission cost of distributed quantum circuits and selecting the optimal partition scheme from the candidates.Finally,the proposed method is evaluated through various comparative experiments.The experimental results demonstrate that spectral clustering-based partitioning exhibits robust stability and efficiency in runtime in quantum circuits of different scales.In experiments involving the quantum Fourier transform algorithm and Revlib quantum circuits,the transmission cost achieved by the global gate search tree strategy is significantly optimized.展开更多
Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaboratio...Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.展开更多
Martensitic-based microstructures in low-density steels offer high strength and improved specific strength,combined with the lightweight effect of aluminum(Al).However,while Al effectively reduces density,it simultane...Martensitic-based microstructures in low-density steels offer high strength and improved specific strength,combined with the lightweight effect of aluminum(Al).However,while Al effectively reduces density,it simultaneously promotes the formation of coarse ferrite and expands the two-phase(α+γ)intercritical temperature range.Thus,increasing the Al content for higher weight reduction inevitably leads to ferrite formation and impedes further strengthening.To achieve both high strength and duc-tility while incorporating ferrite,it is crucial to elucidate the effects of ferrite fraction,size,and dis-tribution on mechanical properties and deformation behavior,particularly in relation to phase interac-tions.In this study,three model steels were developed through controlled annealing temperatures,pro-ducing distinct triplex microstructures comprising ferrite,martensite,and retained austenite(RA).The role of each phase in strain partitioning was investigated using ex-situ microscopic digital image cor-relation and electron back-scattered diffraction analysis.Key findings reveal that the martensitic matrix ensures an ultrahigh strength level(1758 MPa),while a moderate fraction(∼17%)and homogeneous dis-tribution of intercritical-ferrite(IC-ferrite)enable sustainable strain-hardening behavior by delaying the transformation-induced plasticity(TRIP)effect.Strain partitioning into IC-ferrite reduces local strains in the martensitic matrix,preventing early exhaustion of the TRIP effect and facilitating ductile fracture behavior.This strategy leverages the presence of ferrite,offering significant advantages for applications requiring both ultrahigh strength and ductility.展开更多
基金funded by the Beijing Engineering Research Center of Electric Rail Transportation.
文摘Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.
基金funded by the National Key Research and Development Program of China(Grant No.2024YFE0209000)the NSFC(Grant No.U23B2019)。
文摘With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates.Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads.Meanwhile,because of the coupling between consensus and execution,a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution.However,we observe that transaction partitioning and subsequent consensus do not require actual account data but only need to determine the relative partition order between shards.Therefore,we propose a novel sharded blockchain,called HATLedger,based on Hybrid Account and Transaction partitioning.First,HATLedger proposes building a future transaction graph to detect upcoming hotspot accounts and making more precise account partitioning to reduce transaction cross-shard rates.In the event of an impending overload,the source shard employs simulated partition transactions to specify the partition order across multiple target shards,thereby rapidly partitioning the pending transactions.The target shards can reach consensus on received transactions without waiting for account data.The source shard subsequently sends the account data to the corresponding target shards in the order specified by the previously simulated partition transactions.Based on real transaction history from Ethereum,we conducted extensive sharding scalability experiments.By maintaining low cross-shard rates and a relatively balanced load distribution,HATLedger achieves throughput improvements of 2.2x,1.9x,and 1.8x over SharPer,Shard Scheduler,and TxAllo,respectively,significantly enhancing efficiency and scalability.
基金National Natural Science Foundation of China(62402020,62303022)Beijing Nova Program(20240484720)+1 种基金Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions(BPHR202203043)BTBU Digital Business Platform Project byBMEC.
文摘Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.
基金supported by the National Natural Science Foundation of China(Grant Nos.52069029,52369026)the Belt and Road Special Foundation of National Key Laboratory of Water Disaster Preven-tion(Grant No.2023490411)+2 种基金the Yunnan Agricultural Basic Research Joint Special General Project(Grant Nos.202501BD070001-060,202401BD070001-071)Construction Project of the Yunnan Key Laboratory of Water Security(No.20254916CE340051)the Youth Talent Project of“Xingdian Talent Support Plan”in Yunnan Province(Grant No.XDYC-QNRC-2023-0412).
文摘Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the construction and optimization of a prediction model for deformation prediction,a multipoint ultrahigh arch dam deformation prediction model,namely,the CEEMDAN-KPCA-GSWOA-KELM,which is based on a clustering partition,is pro-posed.First,the monitoring data are preprocessed via variational mode decomposition(VMD)and wavelet denoising(WT),which effectively filters out noise and improves the signal-to-noise ratio of the data,providing high-quality input data for subsequent prediction models.Second,scientific cluster partitioning is performed via the K-means++algorithm to precisely capture the spatial distribution characteristics of extra-high arch dams and ensure the consistency of deformation trends at measurement points within each partition.Finally,CEEMDAN is used to separate monitoring data,predict and analyze each component,combine the KPCA(Kernel Principal Component Analysis)and the KELM(Kernel Extreme Learning Machine)optimized by the GSWOA(Global Search Whale Optimization Algorithm),integrate the predictions of each component via reconstruction methods,and precisely predict the overall trend of ultrahigh arch dam deformation.An extra high arch dam project is taken as an example and validated via a comparative analysis of multiple models.The results show that the multipoint deformation prediction model in this paper can combine data from different measurement points,achieve a comprehensive,precise prediction of the deformation situation of extra high arch dams,and provide strong technical support for safe operation.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金supported by the National Key R&D Program of China(Grant No.2022YFF0503203)National Natural Science Foundation of China(NSFC)projects(Grant Nos.42441826 and 42173041)+1 种基金the Key Research Program of the Institute of Geology and Geophysics,Chinese Academy of Sciences(Grant No.IGGCAS-202204)the computational facilities of the Computer Simulation Laboratory at IGGCAS and the Beijing Super Cloud Computing Center(BSCC).
文摘Hydrogen partitioning between liquid iron alloys and silicate melts governs its distribution and cycling in Earth’s deep interior.Existing models based on simplified Fe-H systems predict strong hydrogen sequestration into the core.However,these models do not account for the modulating effects of major light elements such as oxygen and silicon in the core during Earth’s primordial differentiation.In this study,we use first-principles molecular dynamics simulations,augmented by machine learning techniques,to quantify hydrogen chemical potentials in quaternary Fe-O-Si-H systems under early core-mantle boundary conditions(135 GPa,5000 K).Our results demonstrate that the presence of 5.2 wt%oxygen and 4.8 wt%silicon reduces the siderophile affinity of hydrogen by 35%,decreasing its alloy-silicate partition coefficient from 18.2(in the case of Fe-H)to 11.8(in the case of Fe-O-Si-H).These findings suggest that previous estimates of the core hydrogen content derived from binary system models require downward revision.Our study underscores the critical role of multicomponent interactions in core formation models and provides first-principles-derived constraints to reconcile Earth’s present-day hydrogen reservoirs with its accretionary history.
基金supported by the National Natural Science Foundation of China(12371327)the Natural Science Foundation of Chongqing(cstc2021jcyj-msxmX0107).
文摘In this paper,we examine the functions a(n)and b(n),which respectively represent the number of cubic partitions and cubic partition pairs.Our work leads to the derivation of asymptotic formulas for both a(n)and b(n).Additionally,we establish the upper and lower bounds of these functions,factoring in the explicit error terms involved.Crucially,our findings reveal that a(n)and b(n)both satisfy several inequalities such as log-concavity,third-order Turan inequalities,and strict log-subadditivity.
基金supported by the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金Internal Doctoral Fund of Anhui Xinhua University,bs2025kyqd006。
文摘For a positive integer s,a partition is said to be s-core if its hook length set avoids hook length s.The theory of s-core partitions has intriguing applications in representation theory,number theory,and combinatorics.Analogous to the work of Xiong on the largest size of an(s,s+1,…,s+k)-core partition,we evaluate the largest size of a self-conjugate(s,s+1,…,s+k)-core partition for given positive integers s and k.This generalizes the result on the largest size of a self-conjugate(s,s+1,…,s+k)-core partition,which is obtained by Baek,Nam,and Yu by employing Johnson’s bijection.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
文摘To obtain materials capable of efficiently separating acetylene(C_(2)H_(2))from carbon dioxide(CO_(2))and eth-ylene(C_(2)H_(4)),In this work,based on the pore space partition strategy,a pacs-metal-organic framework(MOF):(NH_(2)Me_(2))_(2)[Fe_(3)(μ_(3)-O)(bdc)_(3)][In(FA)_(3)Cl_(3)](Fe‑FAIn‑bdc)was synthesized successfully by using the metal-formate com-plex[In(FA)_(3)Cl_(3)]^(3-)as the pore partition units,where bdc^(2-)=terephthalate,FA-=formate.Owing to the pore partition effect of this metal-organic building block,fruitful confined spaces are formed in the network of Fe‑FAIn‑bdc,endowing this MOF with superior separation performance of acetylene and carbon dioxide.According to the adsorp-tion test,this MOF exhibited a high adsorption capacity for C_(2)H_(2)(50.79 cm^(3)·g^(-1))at 298 K and 100 kPa,which was much higher than that for CO_(2)(29.99 cm^(3)·g^(-1))and C_(2)H_(4)(30.94 cm^(3)·g^(-1))under the same conditions.Ideal adsorbed solution theory(IAST)calculations demonstrate that the adsorption selectivity of Fe‑FAIn‑bdc for the mixture of C_(2)H_(2)/CO_(2)and C_(2)H_(2)/C_(2)H_(4)in a volume ratio of 50∶50 was 3.08 and 3.65,respectively,which was higher than some reported MOFs such as NUM-11 and SNNU-18.CCDC:_(2)453954.
基金supported by the National Key Research and Development Program of China(No.2022YFC3702000)the National Natural Science Foundation of China(No.52070169)the Project of Bureau of Science and Technology of Zhoushan,China(No.2022C41013).
文摘Two-phase partitioning bioreactors(TPPBs)have been widely used because they overcome the mass-transfer limitation of hydrophobic volatile organic compounds(VOCs)in waste gas biological treatments.Understanding the mechanisms of mass-transfer enhancement in TPPBs would enable efficient predictions for further industrial applications.In this study,influences of gradually increasing silicone oil ratio on the TPPB was explored,and a 94.35%reduction of the n-hexane partition coefficient was observed with 0.1 vol.%silicone,which increased to 80.7%along with a 40-fold removal efficiency enhancement in the stabilised removal period.The elimination capacity increased from 1.47 to 148.35 g/(m^(3)·h),i.e.a 101-fold increase compared with that of the single-phase reactors,when 10 vol.%(3 Critical Micelle Concentration)silicone oil was added.The significantly promoted partition coefficient was the main reason for the mass transfer enhancement,which covered the negative influences of the decreased total mass-transfer coefficient with increasing silicone oil volume ratio.The gradually rising stirring rate was benefit to the n-hexane removal,which became negative when the dominant resistance shifted from mass transfer to biodegradation.Moreover,a mass-transfer-reaction kinetic model of the TPPB was constructed based on the balance of n-hexane concentration,dissolved oxygen and biomass.Similar to the mechanism,the partition factor was predicted sensitive to the removal performance,and another five sensitive parameters were found simultaneously.This forecasting method enables the optimisation of TPPB performance and provides theoretical support for hydrophobic VOCs degradation.
基金supported by the National Key R&D Program of China(Nos.2023YFE0101500,2023YFA1606403)the National Natural Science Foundation of China(Nos.12475118,12335007)the State Key Laboratory of Nuclear Physics and Technology,Peking University(No.NPT2023ZX01)。
文摘We studied the energy partition between two well-separated fission fragments associated with the partition of nucleons owing to quantum entanglement.This is different from most fission models that invoke an explicit statistical partition of excitation energies.The dynamical fission evolution is described within the time-dependent Hartree-Fock+BCS framework.Excitation energies of isotopic fission fragments were obtained using the particle number projection method after the dynamical splitting of^(238)U.The resulting excitation energies of the light and heavy fragments are consistent with the appearance of sawtooth structures.We found that the pairing correlation strengths have a significant influence on the partition of the excitation energies.Furthermore,the excitation energies of isotopic fragments increase with increasing neutron number,implying the suppression of the production of neutron-rich beams in rare-isotope beam facilities.
基金supported by the Science and Technology Project of China South Power Grid Co.,Ltd.under Grant No.036000KK52222044(GDKJXM20222430).
文摘This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the impact of wakes on turbine effective wind speed is analyzed,leading to a quantitative method for assessing wake interactions.Based on these interactions,a partitioning method divides the wind farm into smaller,computationally manageable zones.Subsequently,a heuristic control algorithm is developed for yaw optimization within each partition,reducing the overall computational burden associated with multi-turbine optimization.The algorithm’s effectiveness is evaluated through case studies on 11-turbine and 28-turbine wind farms,demonstrating power generation increases of 9.78%and 1.78%,respectively,compared to baseline operation.The primary innovation lies in coupling the higher-fidelity dynamic FLORIDyn wake model with a graph-based partitioning strategy and a computationally efficient heuristic optimization,enabling scalable and accurate yaw control for large wind farms,overcoming limitations associated with simplified models or centralized optimization approaches.
基金financially supported by the National Natural Science Foundation of China(No.52071321)the Science Foundation of Anhui,China(No.2108085QE189)+2 种基金the Major Research Development Program of Wuhu,China(Nos.2023yf107 and 2023yf063)the Major Projects of Anhui Provincial Department of Education,China(Nos.2022AH050956 and 2022AH050974)the Start-up funding of Anhui Polytechnic University,China(No.2022YQQ006)。
文摘Strong and ductile Al alloys and their suitable design strategy have long been desired in selective laser melting(SLM).This work reports a non-equilibrium partitioning model and a correspondingly designed Al–7.5Mg–0.5Sc–0.3Zr–0.6Si alloy.This model effectively quantifies the influence of Mg and Si on hot cracking in aluminum alloy by considering the non-equilibrium partitioning under high cooling rates in SLM.The designed Al–7.5Mg–0.5Sc–0.3Zr–0.6Si alloy exhibits no hot cracks and achieves a remarkably enhanced strength–ductility synergy(a yield strength of(412±8)MPa and a uniform elongation of(15.6±0.6)%),superior to previously reported Al–Mg–Sc–Zr and Al–Mn alloys.A tensile cracking model is proposed to explore the origin of the improved ductility.Both the non-equilibrium partitioning model and the novel Al–7.5Mg–0.5Sc–0.3Zr–0.6Si alloy offers a promising opportunity for producing highly reliable aluminum parts through SLM.
基金funded by National Natural Science Foundation of China(No.61741303)Guangxi Natural Science Foundation(No.2017GXNSFAA198161)the Foundation Project of Guangxi Key Laboratory of Spatial Information and Mapping(No.21-238-21-16).
文摘In large-scaleWireless Rechargeable SensorNetworks(WRSN),traditional forward routingmechanisms often lead to reduced energy efficiency.To address this issue,this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing.The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area.Then,the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission.Relay nodes are selected layer by layer,starting from the outermost cluster heads.Finally,the inter-layer routing mechanism is integrated with regional partitioning and clustering methods to develop the WRSN energy optimization algorithm.To further optimize the algorithm’s performance,we conduct parameter optimization experiments on the relay routing selection function,cluster head rotation energy threshold,and inter-layer relay structure selection,ensuring the best configurations for energy efficiency and network lifespan.Based on these optimizations,simulation results demonstrate that the proposed algorithm outperforms traditional forward routing,K-CHRA,and K-CLP algorithms in terms of node mortality rate and energy consumption,extending the number of rounds to 50%node death by 11.9%,19.3%,and 8.3%in a 500-node network,respectively.
基金supported by the National Key R&D Program Young Scientists Project(2021YFB3703500)National Natural Science Foundation of China(52001110,52122410,52374406),S&T Program of Hebei(23311004D)+1 种基金Natural Science Foundation of Hebei Province(E2023203259)Science and Technology Project of Yantai(2022ZDCX002).
文摘The correlation between the microstructure,properties,and strain partitioning behavior in a medium-carbon carbide-free bainitic steel was investigated through a combination of experiments and representative volume element simulations.The results reveal that as the austempering temperature increases from low to intermediate,the optimal balance of properties shifts from strength-toughness to plasticity-toughness.The formation of fine bainitic ferrite plates and bainite sheaves under low austempering temperature(270℃)enhances both strength and toughness.Conversely,the wide size and shape distribution of the retained austenite(RA)obtained through austempering at intermediate temperature(350℃)contribute to increased work-hardening capacity,resulting in enhanced plasticity.The volume fraction of the ductile film-like RA plays a crucial role in enhancing impact toughness under relatively higher austempering temperatures.In the simulations of tensile deformation,the concentration of equivalent plastic strain predominantly manifests in the bainitic ferrite neighboring the martensite,whereas the equivalent plastic strain evenly spreads between the thin film-like retained austenite and bainitic ferrite.It is predicted that the cracks will occur at the interface between martensite and bainitic ferrite where the strain is concentrated,and eventually propagate along the strain failure zone.
基金supported by the National Natural Science Foundation of China(Grant No.62072259)in part by the Natural Science Foundation of Jiangsu Province(Grant No.BK20221411)+1 种基金the PhD Start-up Fund of Nantong University(Grant No.23B03)the Postgraduate Research&Practice Innovation Program of School of Information Science and Technology,Nantong University(Grant No.NTUSISTPR2405).
文摘In the current noisy intermediate-scale quantum(NISQ)era,a single quantum processing unit(QPU)is insufficient to implement large-scale quantum algorithms;this has driven extensive research into distributed quantum computing(DQC).DQC involves the cooperative operation of multiple QPUs but is concurrently challenged by excessive communication complexity.To address this issue,this paper proposes a quantum circuit partitioning method based on spectral clustering.The approach transforms quantum circuits into weighted graphs and,through computation of the Laplacian matrix and clustering techniques,identifies candidate partition schemes that minimize the total weight of the cut.Additionally,a global gate search tree strategy is introduced to meticulously explore opportunities for merged transfer of global gates,thereby minimizing the transmission cost of distributed quantum circuits and selecting the optimal partition scheme from the candidates.Finally,the proposed method is evaluated through various comparative experiments.The experimental results demonstrate that spectral clustering-based partitioning exhibits robust stability and efficiency in runtime in quantum circuits of different scales.In experiments involving the quantum Fourier transform algorithm and Revlib quantum circuits,the transmission cost achieved by the global gate search tree strategy is significantly optimized.
基金supported by the Beijing Natural Science Foundation(Certificate Number:L234025).
文摘Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.
基金financially supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Ko-rea Government(MOTIE)(HRD Program for Industrial Innova-tion)(P0023676)the National Research Foundation of Ko-rea(NRF)grant funded by the Korea government(MSIT)(NRF-2022R1A5A1030054,RS-2023-00281508,NRF-RS-2024-00345498).
文摘Martensitic-based microstructures in low-density steels offer high strength and improved specific strength,combined with the lightweight effect of aluminum(Al).However,while Al effectively reduces density,it simultaneously promotes the formation of coarse ferrite and expands the two-phase(α+γ)intercritical temperature range.Thus,increasing the Al content for higher weight reduction inevitably leads to ferrite formation and impedes further strengthening.To achieve both high strength and duc-tility while incorporating ferrite,it is crucial to elucidate the effects of ferrite fraction,size,and dis-tribution on mechanical properties and deformation behavior,particularly in relation to phase interac-tions.In this study,three model steels were developed through controlled annealing temperatures,pro-ducing distinct triplex microstructures comprising ferrite,martensite,and retained austenite(RA).The role of each phase in strain partitioning was investigated using ex-situ microscopic digital image cor-relation and electron back-scattered diffraction analysis.Key findings reveal that the martensitic matrix ensures an ultrahigh strength level(1758 MPa),while a moderate fraction(∼17%)and homogeneous dis-tribution of intercritical-ferrite(IC-ferrite)enable sustainable strain-hardening behavior by delaying the transformation-induced plasticity(TRIP)effect.Strain partitioning into IC-ferrite reduces local strains in the martensitic matrix,preventing early exhaustion of the TRIP effect and facilitating ductile fracture behavior.This strategy leverages the presence of ferrite,offering significant advantages for applications requiring both ultrahigh strength and ductility.
