Field measurements of methane emission from rice paddies were made in Nanjing, China and in Texas, USA, respectively. Soil temperature at approximately 10 cm depth of the flooded soils was automatically recorded. Abov...Field measurements of methane emission from rice paddies were made in Nanjing, China and in Texas, USA, respectively. Soil temperature at approximately 10 cm depth of the flooded soils was automatically recorded. Aboveground biomass of rice crop was measured approximately every 10 days in Nanjing and every other week in Texas. Seasonal variation of soil temperature in Nanjing was quite wide with a magnitude of 15.3°C and that in Texas was narrow with a magnitude of 2.9°C. Analysis of methane emission fluxes against soil temperature and rice biomass production demonstrated that the seasonal course of methane emission in Nanjing was mostly attributed to soil temperature changes, while that in Texas was mainly related to rice biomass production. We concluded that under the permanent flooding condition, the seasonal trend of methane emission would be determined by the soil temperature where there was a wide variation of soil temperature, and the seasonal trend would be mainly determined by rice biomass production if there are no additional organic matter inputs and the variation of soil temperature over the rice growing season is small. Key words CH4 emission - Rice paddies - Rice biomass production - Soil temperature This work was supported by grants from TECO/NASA, the United States, the Hundred Talents Program, Chinese Academy of Sciences and the National Key Basic Research Development Foundation (approved # G1999011805), China.展开更多
One important issue in current condensed matter physics is the search of quantum spin liquid(QSL),an exotic magnetic state with strongly-fluctuating and highly-entangled spins down to zero temperature without static o...One important issue in current condensed matter physics is the search of quantum spin liquid(QSL),an exotic magnetic state with strongly-fluctuating and highly-entangled spins down to zero temperature without static order.However,there is no consensus on the existence of a QSL state in any real material so far,due to inevitable disorder and intricate competing exchange interactions on frustrated spin lattices.Here we report systematic heat transport measurements on a honeycomb-lattice compound BaCo_(2)(AsO_(4))_(2),which manifests magnetic order in zero field.In a narrow in-plane field range after the magnetic order is nearly suppressed,in both perpendicular and parallel to the zigzag direction,a finite residual linear term of thermal conductivity is clearly observed,which is attributed to mobile fermionic excitations.In addition,the spin-phonon scattering rate exhibits a T-linear behavior when the order disappears.These observations suggest a partial QSL state with gapless spinon excitations in BaCo_(2)(AsO_(4))_(2),that emerges when a portion of the spins remains ordered,and vanishes as the spins become progressively polarized.展开更多
Various velocity models have been built for Southeast Qinghai-Xizang Plateau with the purpose of revealing the internal dynamics and estimating local seismic hazards.In this study,we use a 3-D full-waveform modeling p...Various velocity models have been built for Southeast Qinghai-Xizang Plateau with the purpose of revealing the internal dynamics and estimating local seismic hazards.In this study,we use a 3-D full-waveform modeling package to systematically validate three published continental-scale velocity models,that is,Shen2016,FWEA18,and USTClitho1.0,leveraging the ample datasets in Southeast Qinghai-Xizang Plateau region.Travel time residuals and waveform similarities are measured between observed empirical Green’s functions and synthetic waveforms.The results show that the Shen2016 model,derived from traditional surface wave tomography,performs best in fitting Rayleigh waves in the Southeast Qinghai-Xizang Plateau,followed by FWEA18,built from full-waveform inversion of long-period body and surface waves.The USTClitho1.0 model,although inverted from body wave datasets,is comparable with FWEA18 in fitting Rayleigh waves.The results also show that all the models are faster than the ground-truth model and show relatively large travel-time residuals and poor waveform similarities at shorter period bands,possibly caused by small-scale structural heterogeneities in the shallower crust.We further invert the time residuals for spatial velocity residuals and reveal that all three models underestimate the amplitudes of high-and low-velocity anomalies.The underestimated amplitude is up to 4%,which is non-negligible considering that the overall amplitude of anomalies is only 5%−10%in the crust.These results suggest that datasets and the inversion method are both essential to building accurate models and further refinements of these models are necessary.展开更多
The characterization of kerogen nanopores is crucial for predicting the geostorage capacity and recoverability of natural gas in unconventional gas shale reservoirs.Towards this end,a powerful technique is presented w...The characterization of kerogen nanopores is crucial for predicting the geostorage capacity and recoverability of natural gas in unconventional gas shale reservoirs.Towards this end,a powerful technique is presented which integrates 2D NMR T_(1)-T_(2) relaxation measurements with molecular dynamics(MD)simulations of hydrocarbons confined in the nanopores of kerogen.The integrated NMR-MD technique is demonstrated using T_(1)-T_(2) measurements of kerogen isolates and organic-rich chalks saturated with heptane,together with MD simulations of heptane completely dissolved in a realistic kerogen model.The NMR-MD results are used to extract the swelling ratio and nanopore size distribution of kerogen as a function of depth in the reservoir.The effects of organic nanoconfinement on the T_(1) relaxation dispersion and T_(2) residual dipolar coupling of heptane are investigated,as well as the effect of downhole effective stress on the kerogen nanopore size as a function of depth and compaction.Potential applications in partially depleted gas shale reservoirs are discussed,including CO_(2) utilization/geostorage,geostorage of green H_(2),and integration of the NMR-MD technique with thermodynamic models for predicting the competitive sorption of gas mixtures in kerogen.展开更多
In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentba...In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentbasedmethods have limitations in capturing complex,multi-faceted relationships in large-scale,sparse datasets.Recent advances in Graph Neural Networks(GNNs)have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks.However,existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships,leading to reduced recommendation diversity and limited generalization.To address these challenges,this paper proposes a dual multi-relational graph neural network recommendation algorithm based on relational interactions.Our approach constructs two complementary graph structures:a User-Item Interaction Graph(UIIG),which explicitly models direct user behaviors such as clicks and purchases,and a Relational Association Graph(RAG),which uncovers latent associations based on user similarities and item attributes.The proposed Dual Multi-relational Graph Neural Network(DMGNN)features two parallel branches that perform multi-layer graph convolutional operations,followed by an adaptive fusion mechanism to effectively integrate information from both graphs.This design enhances the model’s capacity to capture diverse relationship types and complex relational patterns.Extensive experiments conducted on benchmark datasets—including MovieLens-1M,Amazon-Electronics,and Yelp—demonstrate thatDMGNN outperforms state-of-the-art baselines,achieving improvements of up to 12.3%in Precision,9.7%in Recall,and 11.5%in F1 score.Moreover,DMGNN significantly boosts recommendation diversity by 15.2%,balancing accuracy with exploration.These results highlight the effectiveness of leveraging hierarchical multi-relational information,offering a promising solution to the challenges of data sparsity and relation heterogeneity in recommendation systems.Our work advances the theoretical understanding of multi-relational graph modeling and presents practical insights for developing more personalized,diverse,and robust recommender systems.展开更多
基金supported by grants from TECO/ NASA, the United States, the Hundred TalentsProgram, Chinese Academy of Sciences the Nation
文摘Field measurements of methane emission from rice paddies were made in Nanjing, China and in Texas, USA, respectively. Soil temperature at approximately 10 cm depth of the flooded soils was automatically recorded. Aboveground biomass of rice crop was measured approximately every 10 days in Nanjing and every other week in Texas. Seasonal variation of soil temperature in Nanjing was quite wide with a magnitude of 15.3°C and that in Texas was narrow with a magnitude of 2.9°C. Analysis of methane emission fluxes against soil temperature and rice biomass production demonstrated that the seasonal course of methane emission in Nanjing was mostly attributed to soil temperature changes, while that in Texas was mainly related to rice biomass production. We concluded that under the permanent flooding condition, the seasonal trend of methane emission would be determined by the soil temperature where there was a wide variation of soil temperature, and the seasonal trend would be mainly determined by rice biomass production if there are no additional organic matter inputs and the variation of soil temperature over the rice growing season is small. Key words CH4 emission - Rice paddies - Rice biomass production - Soil temperature This work was supported by grants from TECO/NASA, the United States, the Hundred Talents Program, Chinese Academy of Sciences and the National Key Basic Research Development Foundation (approved # G1999011805), China.
基金funded by the National Natural Science Foundations of China(Grant Nos.12034004 and 12174064)the Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)+2 种基金the Innovation Program for Quantum Science and Technology(Grant No.2024ZD0300104)supported by U.S.DOE BES DE-SC0012311the Robert A.Welch Foundation under Grant No.C-1839,respectively(P.D.)。
文摘One important issue in current condensed matter physics is the search of quantum spin liquid(QSL),an exotic magnetic state with strongly-fluctuating and highly-entangled spins down to zero temperature without static order.However,there is no consensus on the existence of a QSL state in any real material so far,due to inevitable disorder and intricate competing exchange interactions on frustrated spin lattices.Here we report systematic heat transport measurements on a honeycomb-lattice compound BaCo_(2)(AsO_(4))_(2),which manifests magnetic order in zero field.In a narrow in-plane field range after the magnetic order is nearly suppressed,in both perpendicular and parallel to the zigzag direction,a finite residual linear term of thermal conductivity is clearly observed,which is attributed to mobile fermionic excitations.In addition,the spin-phonon scattering rate exhibits a T-linear behavior when the order disappears.These observations suggest a partial QSL state with gapless spinon excitations in BaCo_(2)(AsO_(4))_(2),that emerges when a portion of the spins remains ordered,and vanishes as the spins become progressively polarized.
基金supported by the Special Fund of the Institute of Geophysics,China Earthquake Administration(Nos.DQJB23R28 and DQJB22K40)the National Natural Science Foundation of China(Nos.42304078,U1839210 and 42104043).
文摘Various velocity models have been built for Southeast Qinghai-Xizang Plateau with the purpose of revealing the internal dynamics and estimating local seismic hazards.In this study,we use a 3-D full-waveform modeling package to systematically validate three published continental-scale velocity models,that is,Shen2016,FWEA18,and USTClitho1.0,leveraging the ample datasets in Southeast Qinghai-Xizang Plateau region.Travel time residuals and waveform similarities are measured between observed empirical Green’s functions and synthetic waveforms.The results show that the Shen2016 model,derived from traditional surface wave tomography,performs best in fitting Rayleigh waves in the Southeast Qinghai-Xizang Plateau,followed by FWEA18,built from full-waveform inversion of long-period body and surface waves.The USTClitho1.0 model,although inverted from body wave datasets,is comparable with FWEA18 in fitting Rayleigh waves.The results also show that all the models are faster than the ground-truth model and show relatively large travel-time residuals and poor waveform similarities at shorter period bands,possibly caused by small-scale structural heterogeneities in the shallower crust.We further invert the time residuals for spatial velocity residuals and reveal that all three models underestimate the amplitudes of high-and low-velocity anomalies.The underestimated amplitude is up to 4%,which is non-negligible considering that the overall amplitude of anomalies is only 5%−10%in the crust.These results suggest that datasets and the inversion method are both essential to building accurate models and further refinements of these models are necessary.
基金Vinegar Technologies LLC,Chevron Energy Technology Company,Rice University Consortium for Processes in Porous Media,and the American Chemical Society Petroleum Research Fund(No.ACS PRF 58859-ND6)for their financial support。
文摘The characterization of kerogen nanopores is crucial for predicting the geostorage capacity and recoverability of natural gas in unconventional gas shale reservoirs.Towards this end,a powerful technique is presented which integrates 2D NMR T_(1)-T_(2) relaxation measurements with molecular dynamics(MD)simulations of hydrocarbons confined in the nanopores of kerogen.The integrated NMR-MD technique is demonstrated using T_(1)-T_(2) measurements of kerogen isolates and organic-rich chalks saturated with heptane,together with MD simulations of heptane completely dissolved in a realistic kerogen model.The NMR-MD results are used to extract the swelling ratio and nanopore size distribution of kerogen as a function of depth in the reservoir.The effects of organic nanoconfinement on the T_(1) relaxation dispersion and T_(2) residual dipolar coupling of heptane are investigated,as well as the effect of downhole effective stress on the kerogen nanopore size as a function of depth and compaction.Potential applications in partially depleted gas shale reservoirs are discussed,including CO_(2) utilization/geostorage,geostorage of green H_(2),and integration of the NMR-MD technique with thermodynamic models for predicting the competitive sorption of gas mixtures in kerogen.
文摘In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentbasedmethods have limitations in capturing complex,multi-faceted relationships in large-scale,sparse datasets.Recent advances in Graph Neural Networks(GNNs)have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks.However,existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships,leading to reduced recommendation diversity and limited generalization.To address these challenges,this paper proposes a dual multi-relational graph neural network recommendation algorithm based on relational interactions.Our approach constructs two complementary graph structures:a User-Item Interaction Graph(UIIG),which explicitly models direct user behaviors such as clicks and purchases,and a Relational Association Graph(RAG),which uncovers latent associations based on user similarities and item attributes.The proposed Dual Multi-relational Graph Neural Network(DMGNN)features two parallel branches that perform multi-layer graph convolutional operations,followed by an adaptive fusion mechanism to effectively integrate information from both graphs.This design enhances the model’s capacity to capture diverse relationship types and complex relational patterns.Extensive experiments conducted on benchmark datasets—including MovieLens-1M,Amazon-Electronics,and Yelp—demonstrate thatDMGNN outperforms state-of-the-art baselines,achieving improvements of up to 12.3%in Precision,9.7%in Recall,and 11.5%in F1 score.Moreover,DMGNN significantly boosts recommendation diversity by 15.2%,balancing accuracy with exploration.These results highlight the effectiveness of leveraging hierarchical multi-relational information,offering a promising solution to the challenges of data sparsity and relation heterogeneity in recommendation systems.Our work advances the theoretical understanding of multi-relational graph modeling and presents practical insights for developing more personalized,diverse,and robust recommender systems.
文摘对2010年2月27日智利近海发生的M8.8级巨震,本文反向投影美国地震台网宽频带台站记录到的远震P波辐射能量,得到地震破裂前缘随时间的变化关系.图像表明,智利.M8.8级强震破裂过程是一次不均匀的双向破裂过程,整个破裂过程持续了近150 s,破裂尺度跨越震中南端80 km,北北东向上近200 km.
