The synthesis of high-quality heteroepitaxial diamond films on iridium composite substrates is a critical step toward advancing diamond for electronic and optical applications.Microwave plasma chemical vapor depositio...The synthesis of high-quality heteroepitaxial diamond films on iridium composite substrates is a critical step toward advancing diamond for electronic and optical applications.Microwave plasma chemical vapor deposition,combined with in situ optical emission spectroscopy,enables precise control over growth modes through plasma parameter tuning.In this study,we examine how methane concentration,microwave power,and gas pressure influence plasma species and,consequently,the growth modes of heteroepitaxial diamond by optical emission spectroscopy and scanning electron microscope.At low nucleation densities,increased methane concentrations promote the transition from faceted polyhedral to ballas structures,driven by elevated C_(2) radical concentrations in the plasma.Conversely,at higher nucleation densities,gas pressure,and substrate temperature dominate growth mode determination,leading to diverse morphologies,such as planar,polycrystalline,octahedral,and step-flow growth.These findings elucidate the interplay among plasma species,growth parameters,and growth mode,offering critical insights for optimizing growth conditions and preparing heteroepitaxial diamond films in a specific growth mode.展开更多
This paper introduces a framework for modeling random fields,with a particular emphasis on analyzing anisotropic spatial variability.It establishes a clear connection between the correlation function and the Kriging v...This paper introduces a framework for modeling random fields,with a particular emphasis on analyzing anisotropic spatial variability.It establishes a clear connection between the correlation function and the Kriging variogram across various anisotropic modes,providing mathematical models to enhance our understanding of random fields.A new anisotropy index,called LSAI,is introduced to quantify anisotropy based on the autocorrelation length and the orientation of the principal axes within the variogram.An LSAI value closer to one indicates a lower degree of anisotropy.The present study examines how the degree of anisotropy varies with different autocorrelation lengths and angles between the principal axes,providing valuable insights into these relationships.To improve the accuracy of parameter probability distribution estimations,this study integrates limited field test data using a Bayesian inference approach.Additionally,the Markov chain Monte Carlo simulation method is employed to develop a conditional random field(CRF)for the deformation modulus.By incorporating data from field bearing plate tests,the posterior variance data for the deformation modulus are derived.This process facilitates the construction of a detailed and reliable CRF for the deformation modulus.展开更多
Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both g...Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.展开更多
Flexible materials play a crucial role in protecting against behind armour blunt trauma(BABT).However,their compliance complicates the understanding of failure mechanisms and energy absorption.This study used a combin...Flexible materials play a crucial role in protecting against behind armour blunt trauma(BABT).However,their compliance complicates the understanding of failure mechanisms and energy absorption.This study used a combined experimental and numerical approach to investigate the response and failure modes of a flexible ultra-high-molecular-weight polyethylene(UHMWPE)foam protective sandwich structure(UFPSS)under low-velocity impact(LVI).A finite element(FE)model,accounting for nonlinear large deformation and strain-rate-dependent material behavior,was developed for a woven-UFPSS(featuring a plain-woven fabric structure)subjected to a 50 J impact.Experimental and numerical results showed strong agreement in peak force(error<5%),maximum displacement(error<6%),and buffer time(error<8%).The impact's kinetic energy was mainly converted into internal energy of the fabric and foam materials(~50%),viscous dissipation in the foam core(12%-15%),frictional work at the contact interfaces(5%-6%),and work by the pneumatic fixture clamping force(~38%).This study provides the first investigation of the LVI performance of sandwich structures with all soft material layers,offering significant insights for the application of compliant materials in protective fields.展开更多
Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surround...Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.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
The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most exi...The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.展开更多
In 316L austenitic stainless steel,the presence of ferrite phase severely affects the non-magnetic properties.316L austenitic stainless steel with low-alloy type(L-316L)and high-alloy type(H-316L)has been studied.The ...In 316L austenitic stainless steel,the presence of ferrite phase severely affects the non-magnetic properties.316L austenitic stainless steel with low-alloy type(L-316L)and high-alloy type(H-316L)has been studied.The microstructure and solidification kinetics of the two as-cast grades were in situ observed by high temperature confocal laser scanning microscopy(HT-CLSM).There are significant differences in the as-cast microstructures of the two 316L stainless steel compositions.In L-316L steel,ferrite morphology appears as the short rods with a ferrite content of 6.98%,forming a dual-phase microstructure consisting of austenite and ferrite.Conversely,in H-316L steel,the ferrite appears as discontinuous network structures with a content of 4.41%,forming a microstructure composed of austenite and sigma(σ)phase.The alloying elements in H-316L steel exhibit a complex distribution,with Ni and Mo enriching at the austenite grain boundaries.HT-CLSM experiments provide the real-time observation of the solidification processes of both 316L specimens and reveal distinct solidification modes:L-316L steel solidifies in an FA mode,whereas H-316L steel solidifies in an AF mode.These differences result in ferrite and austenite predominantly serving as the nucleation and growth phases,respectively.The solidification mode observed by experiments is similar to the thermodynamic calculation results.The L-316L steel solidified in the FA mode and showed minimal element segregation,which lead to a direct transformation of ferrite to austenite phase(δ→γ)during phase transformation after solidification.Besides,the H-316L steel solidified in the AF mode and showed severe element segregation,which lead to Mo enrichment at grain boundaries and transformation of ferrite into sigma and austenite phases through the eutectoid reaction(δ→σ+γ).展开更多
With the change of the main influencing factors such as structural configuration and impact conditions,reinforced concrete slabs exhibit different mechanical behaviors with different failure patterns,and the failure m...With the change of the main influencing factors such as structural configuration and impact conditions,reinforced concrete slabs exhibit different mechanical behaviors with different failure patterns,and the failure modes are transformed.In order to reveal the failure mode and transformation rule of reinforced concrete slabs under impact loads,a dynamic impact response test was carried out using a drop hammer test device.The dynamic data pertaining to the impact force,support reaction force,structural displacement,and reinforcement strain were obtained through the use of digital image correlation technology(DIC),impact force measurement,and strain measurement.The analysis of the ultimate damage state of the reinforced concrete slab identified four distinct types of impact failure modes:local failure by stamping,overall failure by stamping,local-overall coupling failure,and local failure by punching.Additionally,the influence laws of hammerhead shape,hammer height,and reinforcement ratio on the dynamic response and failure mode transformation of the slab were revealed.The results indicate that:(1)The local damage to the slab by the plane hammer is readily apparent,while the overall damage by the spherical hammer is more pronounced.(2)In comparison to the high reinforcement ratio slabs,the overall bending resistance of the low reinforcement ratio slabs is significantly inferior,and the slab back exhibits further cracks.(3)As the hammer height increases,the slab failure mode undergoes a transformation,shifting from local failure by stamping and overall failure by stamping to local-overall coupling failure and local failure by punching.(4)Three failure mode thresholds have been established,and by comparing the peak impact force with the failure thresholds,the failure mode of the slab can be effectively determined.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
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
The Pacific Meridional Mode(PMM)and the Atlantic Meridional Mode(AMM)are key modes of interannual sea surface temperature(SST)variability in the Pacific and Atlantic Oceans,respectively.Analysis of CMIP6 model outputs...The Pacific Meridional Mode(PMM)and the Atlantic Meridional Mode(AMM)are key modes of interannual sea surface temperature(SST)variability in the Pacific and Atlantic Oceans,respectively.Analysis of CMIP6 model outputs reveals a robust intensification of the PMM under global warming,whereas the AMM exhibits no consensus among models.These different responses are attributed to mid-to-high latitude atmospheric forcing and subtropical feedback mechanisms.Changes in the upper-level westerly jet drive distinct atmospheric variability over the North Pacific and Atlantic,amplifying sea-level pressure variations associated with the PMM but weakening those linked to the AMM.The SST response to atmospheric forcing shows an increase in the Pacific and a decrease in the Atlantic,both of which are significantly positively correlated with the respective changes in each mode.The enhanced wind-evaporation-SST(WES)feedback,primarily driven by rising background SSTs,positively impacts the intensification of both modes.In the subtropical Pacific,the PMM is further strengthened by an increasing latent heat flux response.The enhancement of the PMM is principally connected to intensified atmospheric forcing and strengthened subtropical feedback.Although the WES feedback is enhanced to some extent,wind anomalies that oppose the climatological state reduce latent heat flux.Combined with the weakening of atmospheric forcing over the Atlantic,this phenomenon contributes to the uncertainty in the AMM's response to global warming.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
We apply the WKB approximation method,matrix method,and finite difference method to study the gravitational quasi-normal modes of charged spherically symmetric black holes surrounded by quintessence fluid in Rastall g...We apply the WKB approximation method,matrix method,and finite difference method to study the gravitational quasi-normal modes of charged spherically symmetric black holes surrounded by quintessence fluid in Rastall gravity.By comparing the spherically symmetric spacetime metric of charged black holes surrounded by quintessence fluid in Rastall gravity with that of general relativity,we can find that the modifications to general relativity in this modified gravity theory can be described by parameters such asλ,Q,and C_(a),etc.In four-dimensional spacetime,we investigate the impact of charge Q and parameter C_(a) on the gravitational quasi-normal modes of charged black holes surrounded by quintessence field in Rastall gravity.The aim is to search for observational evidence of such black holes in astrophysical observations and,consequently,test the validity of Rastall theory.In five-dimensional(5D)spacetime,we study the impact of the parameter C_(a) on the gravitational quasi-normal modes of Rastall black holes surrounded by quintessence field and summarize the corresponding variation patterns.展开更多
Utilizing the Standardized Precipitation Evapotranspiration Index(SPEI)and ERA5 reanalysis data,this study investigates the variability of spring drought in southern China from 1979 to 2022 and its associated drivers....Utilizing the Standardized Precipitation Evapotranspiration Index(SPEI)and ERA5 reanalysis data,this study investigates the variability of spring drought in southern China from 1979 to 2022 and its associated drivers.The results indicate that southern China experienced interdecadal changes in SPEI over the past several decades,which can be concluded that drought severity intensified from 1979 to 2010,whereas a transition shift towards increased wetness occurred from 2010 to 2022.The first Empirical Orthogonal Function(EOF1)mode of SPEI variability in southern China accounts for 44.37%of the total variance,reflecting a uniform variation of SPEI across the region.In contrast,the second Empirical Orthogonal Function(EOF2),which explains 24.41%of the total variance,reveals a west-east dipole pattern in SPEI variability.Further analysis indicates that the positive phase of EOF1 is primarily driven by warm sea surface temperature anomalies(SSTAs)in the tropical eastern Pacific.These anomalies induce an anomalous anticyclone over the Philippine Sea,enhancing water vapor transport to southern China during spring.The positive phase of EOF2 is jointly influenced by warm SSTAs in the tropical Indian Ocean and the central Pacific;the latter induces anticyclonic anomalies over the Philippine Sea,which enhance water vapor transport from the western Pacific and increase precipitation in the eastern part of southern China.However,the warm SSTAs in the tropical Indian Ocean trigger an anomalous anticyclone over South Asia,inhibiting water vapor transport from the Bay of Bengal to the western part of southern China and thus reducing precipitation there.展开更多
Controlling topological modes in photonic systems remains a fundamental challenge,as conventional approaches rely on global lattice modifications and lack topological phase engineering of the induced non-trivial state...Controlling topological modes in photonic systems remains a fundamental challenge,as conventional approaches rely on global lattice modifications and lack topological phase engineering of the induced non-trivial states.Here,we reveal that staggered onsite edge potential(SOEP)modulation breaks mirror symmetry in folded edge states,inducing edge-confined Wannier function deviations and thus driving edge bands into distinct topological phases.The emergence of resulting higher-order localized modes is further confirmed.展开更多
To broaden the frequency regulation range of piezoelectric motors,this paper proposes a piezoelectric vibrator that operates in multiple in-plane vibration modes with distinct resonance frequencies.The piezoelectric v...To broaden the frequency regulation range of piezoelectric motors,this paper proposes a piezoelectric vibrator that operates in multiple in-plane vibration modes with distinct resonance frequencies.The piezoelectric vibrator was constructed by reasonably arranging multiple groups of piezoelectric ceramic(PZT)sheets based on the most typical rectangular plate piezoelectric motors.Suitable working modes were selected,and the excitation method of these operating modes was also analyzed.Besides,interactions between selected operating modes were also investigated.The finite element software,ANSYS,was adopted to optimize the structural parameters of the vibrator through modal analysis to match the resonance frequencies of specific modes.After that,whether the selected operating modes can be successfully motivated was verified by harmonic response analysis.Finally,the vibration characteristics of piezoelectric vibrators under conventional vibration modes and multiple modes were acquired by transient analysis,respectively.Simulation results reveal that under dual-frequency excitation scheme 1,response displacements of the driving point are relatively larger.This strategy not only facilitates the excitation of B4 mode but also enables control over the ratio of horizontal to vertical displacements of the driving point.Additionally,incorporating B4 mode expands the frequency adjustment range of piezoelectric vibrators.展开更多
To ensure the safe implementation of underground reservoirs in abandoned coal mines,this study explores the mechanical behavior and failure mechanisms of coal-concrete composite structures under staged cyclic loading....To ensure the safe implementation of underground reservoirs in abandoned coal mines,this study explores the mechanical behavior and failure mechanisms of coal-concrete composite structures under staged cyclic loading.Specimens with coal-to-concrete height ratios ranging from 0.5:1 to 3:1 were tested,with damage evolution continuously monitored using acoustic emission techniques.Results indicate that while the peak strength of pure materials decreases by approximately 1 MPa under cyclic stress compared to uniaxial compression,composite specimens exhibit strength enhancements exceeding 5 MPa.However,the peak strength of composite specimens decreases with increasing coal height,from 30 MPa at CR0.5 to 20 MPa at CR3.0.The damage state was assessed using the dynamic elastic strain energy index and Felicity ratio,which revealed that composite specimens are more prone to early damage accumulation.Spatial acoustic emission localization further reveals distinct failure modes across specimens with varying height ratios.To elucidate these differences,interfacial effects were incorporated into a modified twin-shear unified strength theory.The refined model accurately predicts the internal strength distribution and failure characteristics of the composite structures.These findings provide a theoretical basis for the structural design and safe operation of underground reservoir dams.展开更多
Due to the limitations of widely used energy spectrum and spectral analyses for the determination of trace elements in coal,the modes of occurrence of Li still remains unclear.This study investigated the distribution ...Due to the limitations of widely used energy spectrum and spectral analyses for the determination of trace elements in coal,the modes of occurrence of Li still remains unclear.This study investigated the distribution of Li in selected bulk samples and in-situ kaolinite particles in the No.6 Li-rich coals from the Haerwusu Mine of the Jungar Coalfield using ICP-MS and LA-ICP-MS.The results reveal an elevated Li concentration in the lower section of the No.6 coal with high Sr/Ba ratio compared to the upper section with more terrigenous mudstone along the vertical profile.LA-ICP-MS mapping and spot analyses results showed that Li was concentrated in kaolinite but occur in variations in the concentrations of Li among different types of kaolinite.The concentration of Li in kaolinite is ranked as follows:cryptocrystalline kaolinite(2225.83 ppm)>vermicular kaolinite(651.49 ppm)>altered K-bearing kaolinite(593.44 ppm)>clastic kaolinite(478.68 ppm).The in-situ concentration of Li in kaolinite is much higher than that of the bulk samples,indicating that kaolinite is the dominant host mineral for Li as well.The Al2O3/TiO2 and Nb/Yb-Zr/TiO2 ratios indicate that Li in No.6 coal primarily originated from Paleoproterozoic granite in the Yinshan Mountain and felsic volcanic ash.Seawater leaching has a critical influence on the redistribution of Li in the coal from the Haerwusu Mine or even the whole Jungar Coalfield.展开更多
A numerical and experimental study was conducted to investigate the Laser Ablation(LA)ignition mode in an ethylene-fueled supersonic combustor with a cavity flameholder.Theexperiments were operated under a Mach number...A numerical and experimental study was conducted to investigate the Laser Ablation(LA)ignition mode in an ethylene-fueled supersonic combustor with a cavity flameholder.Theexperiments were operated under a Mach number 2.92 supersonic inflow,with stagnation pressureof 2.4 MPa and stagnation temperature of 1600 K.Reynolds-averaged Navier-Stokes simulationswere conducted to characterize the mixing process and flow field structure.This study identifiedfour distinct LA ignition modes.Under the specified condition,laser ablation in zero and negativedefocusing states manifested two distinct ignition modes termed Laser Ablation Direct Ignition(LADI)mode and Laser Ablation Re-Ignition(LARI)mode,correspondingly.LA ignition in alocal small cavity,created by depressing the flow field regulator,could facilitate the ignition modetransforming from LARI mode to Laser Ablation Transition Ignition(LATI)mode.On the otherhand,the elevation of the flow field regulator effectively inhibited the forward propagation of theinitial flame kernel and reduced the dissipation of LA plasma,further enhancing the LADI mode.Based on these characteristics,the LADI mode was subdivided into strong(LADI-S)and weak(LADI-W)modes.Facilitating the transition of ignition modes through alterations in the local flowfield could contribute to attaining a more effective and stable LA ignition.展开更多
基金funded by the National Key Research and Development Program of China(Grant No.2022YFB3608602)the National Natural Science Foundation of China(Grant Nos.62404215 and 62574199)Instrument and Equipment Development Project of CAS(Grant No.PTYQ2024TD0003)。
文摘The synthesis of high-quality heteroepitaxial diamond films on iridium composite substrates is a critical step toward advancing diamond for electronic and optical applications.Microwave plasma chemical vapor deposition,combined with in situ optical emission spectroscopy,enables precise control over growth modes through plasma parameter tuning.In this study,we examine how methane concentration,microwave power,and gas pressure influence plasma species and,consequently,the growth modes of heteroepitaxial diamond by optical emission spectroscopy and scanning electron microscope.At low nucleation densities,increased methane concentrations promote the transition from faceted polyhedral to ballas structures,driven by elevated C_(2) radical concentrations in the plasma.Conversely,at higher nucleation densities,gas pressure,and substrate temperature dominate growth mode determination,leading to diverse morphologies,such as planar,polycrystalline,octahedral,and step-flow growth.These findings elucidate the interplay among plasma species,growth parameters,and growth mode,offering critical insights for optimizing growth conditions and preparing heteroepitaxial diamond films in a specific growth mode.
基金supported by the Doctoral Research Funds for Nanchang HangKong University,China(Grant No.EA202411211)support is gratefully acknowledged.
文摘This paper introduces a framework for modeling random fields,with a particular emphasis on analyzing anisotropic spatial variability.It establishes a clear connection between the correlation function and the Kriging variogram across various anisotropic modes,providing mathematical models to enhance our understanding of random fields.A new anisotropy index,called LSAI,is introduced to quantify anisotropy based on the autocorrelation length and the orientation of the principal axes within the variogram.An LSAI value closer to one indicates a lower degree of anisotropy.The present study examines how the degree of anisotropy varies with different autocorrelation lengths and angles between the principal axes,providing valuable insights into these relationships.To improve the accuracy of parameter probability distribution estimations,this study integrates limited field test data using a Bayesian inference approach.Additionally,the Markov chain Monte Carlo simulation method is employed to develop a conditional random field(CRF)for the deformation modulus.By incorporating data from field bearing plate tests,the posterior variance data for the deformation modulus are derived.This process facilitates the construction of a detailed and reliable CRF for the deformation modulus.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2342210 and 42275043)the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant Nos.J2223806,ZDJ2024-25 and ZDJ2025-34)。
文摘Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.
基金supported by the Zhenjiang Key R&D Plan(GY2021009)Lianyungang City Major Technology Breakthrough(CGJBGS2104)+2 种基金National Natural Science Foundation of China under Grant(12302456)National Key Laboratory Foundation of Science and Technology on Materials under Shock and Impact under Grant(6142902241601)China Postdoctoral Science Foundation under Grants(2025M774217)。
文摘Flexible materials play a crucial role in protecting against behind armour blunt trauma(BABT).However,their compliance complicates the understanding of failure mechanisms and energy absorption.This study used a combined experimental and numerical approach to investigate the response and failure modes of a flexible ultra-high-molecular-weight polyethylene(UHMWPE)foam protective sandwich structure(UFPSS)under low-velocity impact(LVI).A finite element(FE)model,accounting for nonlinear large deformation and strain-rate-dependent material behavior,was developed for a woven-UFPSS(featuring a plain-woven fabric structure)subjected to a 50 J impact.Experimental and numerical results showed strong agreement in peak force(error<5%),maximum displacement(error<6%),and buffer time(error<8%).The impact's kinetic energy was mainly converted into internal energy of the fabric and foam materials(~50%),viscous dissipation in the foam core(12%-15%),frictional work at the contact interfaces(5%-6%),and work by the pneumatic fixture clamping force(~38%).This study provides the first investigation of the LVI performance of sandwich structures with all soft material layers,offering significant insights for the application of compliant materials in protective fields.
基金supported by the National Key Research and Development Program of China(2018AAA0101005,2018AAA0102404)the Program of the Huawei Technologies Co.Ltd.(FA2018111061SOW12)+1 种基金the National Natural Science Foundation of China(61773054)the Youth Research Fund of the State Key Laboratory of Complex Systems Management and Control(20190213)。
文摘Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.
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partially supported by the National Key Research and Development Program of China(2020YFB2104001)。
文摘The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.
基金support of the Research Project Supported by Shanxi Scholarship Council of China(2022-040)"Chunhui Plan"Collaborative Research Project by the Ministry of Education of China(HZKY20220507)+2 种基金National Natural Science Foundation of China(52104338)Applied Fundamental Research Programs of Shanxi Province(202303021221036)Shandong Postdoctoral Science Foundation(SDCX-ZG-202303027,SDBX2023054).
文摘In 316L austenitic stainless steel,the presence of ferrite phase severely affects the non-magnetic properties.316L austenitic stainless steel with low-alloy type(L-316L)and high-alloy type(H-316L)has been studied.The microstructure and solidification kinetics of the two as-cast grades were in situ observed by high temperature confocal laser scanning microscopy(HT-CLSM).There are significant differences in the as-cast microstructures of the two 316L stainless steel compositions.In L-316L steel,ferrite morphology appears as the short rods with a ferrite content of 6.98%,forming a dual-phase microstructure consisting of austenite and ferrite.Conversely,in H-316L steel,the ferrite appears as discontinuous network structures with a content of 4.41%,forming a microstructure composed of austenite and sigma(σ)phase.The alloying elements in H-316L steel exhibit a complex distribution,with Ni and Mo enriching at the austenite grain boundaries.HT-CLSM experiments provide the real-time observation of the solidification processes of both 316L specimens and reveal distinct solidification modes:L-316L steel solidifies in an FA mode,whereas H-316L steel solidifies in an AF mode.These differences result in ferrite and austenite predominantly serving as the nucleation and growth phases,respectively.The solidification mode observed by experiments is similar to the thermodynamic calculation results.The L-316L steel solidified in the FA mode and showed minimal element segregation,which lead to a direct transformation of ferrite to austenite phase(δ→γ)during phase transformation after solidification.Besides,the H-316L steel solidified in the AF mode and showed severe element segregation,which lead to Mo enrichment at grain boundaries and transformation of ferrite into sigma and austenite phases through the eutectoid reaction(δ→σ+γ).
基金Supported by the National Natural Science Foundation of China(Grant No.52078283)Shandong Provincial Natural Science Foundation(Project No.ZR2024MA094)。
文摘With the change of the main influencing factors such as structural configuration and impact conditions,reinforced concrete slabs exhibit different mechanical behaviors with different failure patterns,and the failure modes are transformed.In order to reveal the failure mode and transformation rule of reinforced concrete slabs under impact loads,a dynamic impact response test was carried out using a drop hammer test device.The dynamic data pertaining to the impact force,support reaction force,structural displacement,and reinforcement strain were obtained through the use of digital image correlation technology(DIC),impact force measurement,and strain measurement.The analysis of the ultimate damage state of the reinforced concrete slab identified four distinct types of impact failure modes:local failure by stamping,overall failure by stamping,local-overall coupling failure,and local failure by punching.Additionally,the influence laws of hammerhead shape,hammer height,and reinforcement ratio on the dynamic response and failure mode transformation of the slab were revealed.The results indicate that:(1)The local damage to the slab by the plane hammer is readily apparent,while the overall damage by the spherical hammer is more pronounced.(2)In comparison to the high reinforcement ratio slabs,the overall bending resistance of the low reinforcement ratio slabs is significantly inferior,and the slab back exhibits further cracks.(3)As the hammer height increases,the slab failure mode undergoes a transformation,shifting from local failure by stamping and overall failure by stamping to local-overall coupling failure and local failure by punching.(4)Three failure mode thresholds have been established,and by comparing the peak impact force with the failure thresholds,the failure mode of the slab can be effectively determined.
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.
基金the National Natural Science Foundation of China(Nos.42230405,41975092)the National Key R&D Program of China(No.2023YFF0805100)+1 种基金the Shandong Natural Science Foundation Project(No.ZR2019ZD12)the Taishan Scholars Project of Shandong Province(No.tsqn202306095)。
文摘The Pacific Meridional Mode(PMM)and the Atlantic Meridional Mode(AMM)are key modes of interannual sea surface temperature(SST)variability in the Pacific and Atlantic Oceans,respectively.Analysis of CMIP6 model outputs reveals a robust intensification of the PMM under global warming,whereas the AMM exhibits no consensus among models.These different responses are attributed to mid-to-high latitude atmospheric forcing and subtropical feedback mechanisms.Changes in the upper-level westerly jet drive distinct atmospheric variability over the North Pacific and Atlantic,amplifying sea-level pressure variations associated with the PMM but weakening those linked to the AMM.The SST response to atmospheric forcing shows an increase in the Pacific and a decrease in the Atlantic,both of which are significantly positively correlated with the respective changes in each mode.The enhanced wind-evaporation-SST(WES)feedback,primarily driven by rising background SSTs,positively impacts the intensification of both modes.In the subtropical Pacific,the PMM is further strengthened by an increasing latent heat flux response.The enhancement of the PMM is principally connected to intensified atmospheric forcing and strengthened subtropical feedback.Although the WES feedback is enhanced to some extent,wind anomalies that oppose the climatological state reduce latent heat flux.Combined with the weakening of atmospheric forcing over the Atlantic,this phenomenon contributes to the uncertainty in the AMM's response to global warming.
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Project supported by the National Natural Science Foundation of China(Grant No.42230207)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Grant No.G1323523064)。
文摘We apply the WKB approximation method,matrix method,and finite difference method to study the gravitational quasi-normal modes of charged spherically symmetric black holes surrounded by quintessence fluid in Rastall gravity.By comparing the spherically symmetric spacetime metric of charged black holes surrounded by quintessence fluid in Rastall gravity with that of general relativity,we can find that the modifications to general relativity in this modified gravity theory can be described by parameters such asλ,Q,and C_(a),etc.In four-dimensional spacetime,we investigate the impact of charge Q and parameter C_(a) on the gravitational quasi-normal modes of charged black holes surrounded by quintessence field in Rastall gravity.The aim is to search for observational evidence of such black holes in astrophysical observations and,consequently,test the validity of Rastall theory.In five-dimensional(5D)spacetime,we study the impact of the parameter C_(a) on the gravitational quasi-normal modes of Rastall black holes surrounded by quintessence field and summarize the corresponding variation patterns.
基金Natural Science Foundation of Guangdong Province,China(2024A1515011352)National Natural Science Founda-tion of China(42275020)+2 种基金Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhu-hai)(311021001)Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies(2020B1212060025)Shanghai Frontiers Science Center of Atmosphere-Ocean Interaction(FDAOS-OP202401)。
文摘Utilizing the Standardized Precipitation Evapotranspiration Index(SPEI)and ERA5 reanalysis data,this study investigates the variability of spring drought in southern China from 1979 to 2022 and its associated drivers.The results indicate that southern China experienced interdecadal changes in SPEI over the past several decades,which can be concluded that drought severity intensified from 1979 to 2010,whereas a transition shift towards increased wetness occurred from 2010 to 2022.The first Empirical Orthogonal Function(EOF1)mode of SPEI variability in southern China accounts for 44.37%of the total variance,reflecting a uniform variation of SPEI across the region.In contrast,the second Empirical Orthogonal Function(EOF2),which explains 24.41%of the total variance,reveals a west-east dipole pattern in SPEI variability.Further analysis indicates that the positive phase of EOF1 is primarily driven by warm sea surface temperature anomalies(SSTAs)in the tropical eastern Pacific.These anomalies induce an anomalous anticyclone over the Philippine Sea,enhancing water vapor transport to southern China during spring.The positive phase of EOF2 is jointly influenced by warm SSTAs in the tropical Indian Ocean and the central Pacific;the latter induces anticyclonic anomalies over the Philippine Sea,which enhance water vapor transport from the western Pacific and increase precipitation in the eastern part of southern China.However,the warm SSTAs in the tropical Indian Ocean trigger an anomalous anticyclone over South Asia,inhibiting water vapor transport from the Bay of Bengal to the western part of southern China and thus reducing precipitation there.
基金National Natural Science Foundation of China(62175180,11874245,12004425)。
文摘Controlling topological modes in photonic systems remains a fundamental challenge,as conventional approaches rely on global lattice modifications and lack topological phase engineering of the induced non-trivial states.Here,we reveal that staggered onsite edge potential(SOEP)modulation breaks mirror symmetry in folded edge states,inducing edge-confined Wannier function deviations and thus driving edge bands into distinct topological phases.The emergence of resulting higher-order localized modes is further confirmed.
基金funded by National Natural Science Foundation of China,grant number 52205292.
文摘To broaden the frequency regulation range of piezoelectric motors,this paper proposes a piezoelectric vibrator that operates in multiple in-plane vibration modes with distinct resonance frequencies.The piezoelectric vibrator was constructed by reasonably arranging multiple groups of piezoelectric ceramic(PZT)sheets based on the most typical rectangular plate piezoelectric motors.Suitable working modes were selected,and the excitation method of these operating modes was also analyzed.Besides,interactions between selected operating modes were also investigated.The finite element software,ANSYS,was adopted to optimize the structural parameters of the vibrator through modal analysis to match the resonance frequencies of specific modes.After that,whether the selected operating modes can be successfully motivated was verified by harmonic response analysis.Finally,the vibration characteristics of piezoelectric vibrators under conventional vibration modes and multiple modes were acquired by transient analysis,respectively.Simulation results reveal that under dual-frequency excitation scheme 1,response displacements of the driving point are relatively larger.This strategy not only facilitates the excitation of B4 mode but also enables control over the ratio of horizontal to vertical displacements of the driving point.Additionally,incorporating B4 mode expands the frequency adjustment range of piezoelectric vibrators.
基金supported by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(No.2024ZD1003903)National Natural Science Foundation of China(Nos.52374078,U24A20616,and 52074043)+1 种基金Sichuan-Chongqing Science and Technology Project Innovation Cooperation Program(No.2024TIAD-CYKJCXX0011)the Fundamental Research Funds for the Central Universities(No.2023CDJKYJH021).
文摘To ensure the safe implementation of underground reservoirs in abandoned coal mines,this study explores the mechanical behavior and failure mechanisms of coal-concrete composite structures under staged cyclic loading.Specimens with coal-to-concrete height ratios ranging from 0.5:1 to 3:1 were tested,with damage evolution continuously monitored using acoustic emission techniques.Results indicate that while the peak strength of pure materials decreases by approximately 1 MPa under cyclic stress compared to uniaxial compression,composite specimens exhibit strength enhancements exceeding 5 MPa.However,the peak strength of composite specimens decreases with increasing coal height,from 30 MPa at CR0.5 to 20 MPa at CR3.0.The damage state was assessed using the dynamic elastic strain energy index and Felicity ratio,which revealed that composite specimens are more prone to early damage accumulation.Spatial acoustic emission localization further reveals distinct failure modes across specimens with varying height ratios.To elucidate these differences,interfacial effects were incorporated into a modified twin-shear unified strength theory.The refined model accurately predicts the internal strength distribution and failure characteristics of the composite structures.These findings provide a theoretical basis for the structural design and safe operation of underground reservoir dams.
基金supported by the National Key R&D Program of China(No.2021YFC2902003)National Natural Science Foundation of China(No.42302193No.42272201).
文摘Due to the limitations of widely used energy spectrum and spectral analyses for the determination of trace elements in coal,the modes of occurrence of Li still remains unclear.This study investigated the distribution of Li in selected bulk samples and in-situ kaolinite particles in the No.6 Li-rich coals from the Haerwusu Mine of the Jungar Coalfield using ICP-MS and LA-ICP-MS.The results reveal an elevated Li concentration in the lower section of the No.6 coal with high Sr/Ba ratio compared to the upper section with more terrigenous mudstone along the vertical profile.LA-ICP-MS mapping and spot analyses results showed that Li was concentrated in kaolinite but occur in variations in the concentrations of Li among different types of kaolinite.The concentration of Li in kaolinite is ranked as follows:cryptocrystalline kaolinite(2225.83 ppm)>vermicular kaolinite(651.49 ppm)>altered K-bearing kaolinite(593.44 ppm)>clastic kaolinite(478.68 ppm).The in-situ concentration of Li in kaolinite is much higher than that of the bulk samples,indicating that kaolinite is the dominant host mineral for Li as well.The Al2O3/TiO2 and Nb/Yb-Zr/TiO2 ratios indicate that Li in No.6 coal primarily originated from Paleoproterozoic granite in the Yinshan Mountain and felsic volcanic ash.Seawater leaching has a critical influence on the redistribution of Li in the coal from the Haerwusu Mine or even the whole Jungar Coalfield.
基金supported by the National Natural Science Foundation of China(Nos.12272408 and 11925207)the Natural Science Foundation for Distinguished Young Scholars of Hunan Province,China(No.2024J12057)。
文摘A numerical and experimental study was conducted to investigate the Laser Ablation(LA)ignition mode in an ethylene-fueled supersonic combustor with a cavity flameholder.Theexperiments were operated under a Mach number 2.92 supersonic inflow,with stagnation pressureof 2.4 MPa and stagnation temperature of 1600 K.Reynolds-averaged Navier-Stokes simulationswere conducted to characterize the mixing process and flow field structure.This study identifiedfour distinct LA ignition modes.Under the specified condition,laser ablation in zero and negativedefocusing states manifested two distinct ignition modes termed Laser Ablation Direct Ignition(LADI)mode and Laser Ablation Re-Ignition(LARI)mode,correspondingly.LA ignition in alocal small cavity,created by depressing the flow field regulator,could facilitate the ignition modetransforming from LARI mode to Laser Ablation Transition Ignition(LATI)mode.On the otherhand,the elevation of the flow field regulator effectively inhibited the forward propagation of theinitial flame kernel and reduced the dissipation of LA plasma,further enhancing the LADI mode.Based on these characteristics,the LADI mode was subdivided into strong(LADI-S)and weak(LADI-W)modes.Facilitating the transition of ignition modes through alterations in the local flowfield could contribute to attaining a more effective and stable LA ignition.
