The kagome metals AV_(3)Sb_(5)(A=K,Rb,Cs)feature intertwined Dirac fermions,topological flat bands,and van Hove singularities(vHS)near the Fermi level,which give rise to a range of exotic,strongly correlated phenomena...The kagome metals AV_(3)Sb_(5)(A=K,Rb,Cs)feature intertwined Dirac fermions,topological flat bands,and van Hove singularities(vHS)near the Fermi level,which give rise to a range of exotic,strongly correlated phenomena such as charge density waves(CDW)and superconductivity.Although the vHS from V 3d states have been implicated in CDW formation,their three-dimensional nature and temperature evolution remain poorly understood.In this study,we used high-resolution angle-resolved photoemission spectroscopy and density functional theory to reveal pronounced out-of-plane dispersion of vHS and their temperature dependence in KV_(3)Sb_(5).The identified c-axis band folding and scattering channels were directly linked to the CDW order.These results demonstrate that the CDW transition in this family involves cooperative coupling between electron correlations and structural modulation along the c axis.This offers new insights into the interplay of topology,correlations,and lattice instabilities in kagome metals.展开更多
Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has bec...Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has become a mainstream solution but losing focus on the inter-sample class information.This paper proposes a new Credible Local Context Representation approach for SFDA.Our main idea is to exploit the credible local context for more discriminative representation.Specifically,we enhance the source model's discrimination by information regulating.To capture the context,a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context.In the epoch-wise adaptation,deep clustering-like training is conducted with two major updates.First,the context for all target data is constructed and then the context-fused pseudo-labels providing semantic guidance are generated.Second,for each target data,a weighting fusion on its context forms the anchored neighbourhood structure;thus,the deep clustering is switched from individual-based to coarse-grained.Also,a new regularisation building is developed on the anchored neighbourhood to drive the deep coarse-grained learning.Experiments on three benchmarks indicate that the proposed method can achieve stateof-the-art results.展开更多
Methyl methoxyacetate(MMAc)and methyl formate(MF)can be produced directly by heterogeneous zeolite-catalyzed carbonylation and disproportionation of dimethoxymethane(DMM),with near 100%selectivity for each process.Des...Methyl methoxyacetate(MMAc)and methyl formate(MF)can be produced directly by heterogeneous zeolite-catalyzed carbonylation and disproportionation of dimethoxymethane(DMM),with near 100%selectivity for each process.Despite continuous research efforts,the insight into the reaction mechanism and kinetics theory are still in their nascent stage.In this study,ZEO-1 material,a zeolite with up to now the largest cages comprising 16×16-MRs,16×12-MRs,and 12×12-MRs,was explored for DMM carbonylation and disproportionation reactions.The rate of MMAc formation based on accessible Brönsted acid sites is 2.5 times higher for ZEO-1(Si/Al=21)relative to the previously investigated FAU(Si/Al=15),indicating the positive effect of spatial separation of active sites in ZEO-1 on catalytic activity.A higher MF formation rate is also observed over ZEO-1 with lower activation energy(79.94 vs.95.19 kJ/mol)compared with FAU(Si/Al=30).Two types of active sites are proposed within ZEO-1 zeolite:Site 1 located in large cages formed by 16×16-MRs and 16×12-MRs,which is active predominantly for MMAc formation,and Site 2 located in smaller cages for methyl formate/dimethyl ether formation.Kinetics investigation of DMM carbonylation over ZEO-1 exhibit a first-order dependence on CO partial pressure and a slightly inverse-order dependence on DMM partial pressure.The DMM disproportionation is nearly first-order dependence on DMM partial pressure,while it reveals a strongly inverse dependence with increasing CO partial pressure.Furthermore,ZEO-1 exhibits good catalytic stability,and almost no deactivation is observed during the more than 70 h test with high carbonylation selectivity of above 89%,due to the well-enhanced diffusion property demonstrated by intelligent-gravimetric analysis.展开更多
Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is...Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is approximated by the Gaussian distribution and added to experimental capacitance data with various intensities.The equivalent signal strength(Ф)that equals the signal-to-noise ratio of packed beds is used to evaluate noise levels.Results show that the Pearson correlation coefficient,which indicates the similarity of solids fraction distributions over pixels,increases with Ф,and reconstructed images are more deteriorated at lower Ф.Nevertheless,relative errors for average solids fraction and bubble size in each frame are less sensitive to noise,attributed to noise compromise caused by the process of pixel values.These findings provide useful guidance for assessing the accuracy of ECT measurements of multiphase flows.展开更多
Accurately acquiring catalyst size and morphology is essential for supporting catalytic reaction process design and optimal control. We report an intelligent catalyst sizing and morphological classification method bas...Accurately acquiring catalyst size and morphology is essential for supporting catalytic reaction process design and optimal control. We report an intelligent catalyst sizing and morphological classification method based on the Mask-RCNN framework. A dataset of 9880 high-resolution images was captured by using a self-made fiber-optic endoscopic system for 13 kinds of silicoaluminophosphate-34 (SAPO-34) catalyst samples with different coke. Then there were approximately 877881 individual particles extracted from this dataset by our AI-based particle recognition algorithm. To clearly describe the morphology of irregular particles, we proposed a hybrid classification criterion that combines five different parameters, which are deformity, circularity, roundness, aspect ratio, and compactness. Therefore, catalyst morphology can be classified into two categories with four types. The first category includes regular types, such as the spherical, ellipsoidal, and rod-shaped types. And all the irregular types fall into the second category. The experimental results showed that a catalyst particle tends to be larger when its coke deposition increased. Whereas particle morphology remained primarily spherical and ellipsoidal, the ratio of each type varied slightly according to its coke. Our findings illustrate that this is a promising approach to be developing intelligent instruments for catalyst particle sizing and classification.展开更多
BACKGROUND Choledochal cysts(CC)and cystic biliary atresia(CBA)present similarly in early infancy but require different treatment approaches.While CC surgery can be delayed until 3-6 months of age in asymptomatic pati...BACKGROUND Choledochal cysts(CC)and cystic biliary atresia(CBA)present similarly in early infancy but require different treatment approaches.While CC surgery can be delayed until 3-6 months of age in asymptomatic patients,CBA requires intervention within 60 days to prevent cirrhosis.AIM To develop a diagnostic model for early differentiation between these conditions.METHODS A total of 319 patients with hepatic hilar cysts(<60 days old at surgery)were retrospectively analyzed;these patients were treated at three hospitals between 2011 and 2022.Clinical features including biochemical markers and ultrasonographic measurements were compared between CC(n=274)and CBA(n=45)groups.Least absolute shrinkage and selection operator regression identified key diagnostic features,and 11 machine learning models were developed and compared.RESULTS The CBA group showed higher levels of total bile acid,total bilirubin,γ-glutamyl transferase,aspartate aminotransferase,and alanine aminotransferase,and direct bilirubin,while longitudinal diameter of the cysts and transverse diameter of the cysts were larger in the CC group.The multilayer perceptron model demonstrated optimal performance with 95.8% accuracy,92.9% sensitivity,96.3% specificity,and an area under the curve of 0.990.Decision curve analysis confirmed its clinical utility.Based on the model,we developed user-friendly diagnostic software for clinical implementation.CONCLUSION Our machine learning approach differentiates CC from CBA in early infancy using routinely available clinical parameters.Early accurate diagnosis facilitates timely surgical intervention for CBA cases,potentially improving patient outcomes.展开更多
The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by sub...The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.展开更多
基金supported by the National Key R&D Program of China(Grant Nos.2023YFA1406304 and 2024YFA1408103)the National Science Foundation of China(Grant Nos.12494593 and 12004405)+5 种基金the Anhui Provincial Natural Science Foundation(Grant No.2408085J003)the National Key R&D Program of China(Grant No.2023YFA1406100)the open projects of the State Key Laboratory of Functional Materials for Informatics(Grant No.SKL2022)the China National Postdoctoral Program for Innovative Talents(BX20240348)support from the National Natural Science Foundation of China(Grant No.12404186)the Shanghai Sailing Program(Grant No.23YF1426900)。
文摘The kagome metals AV_(3)Sb_(5)(A=K,Rb,Cs)feature intertwined Dirac fermions,topological flat bands,and van Hove singularities(vHS)near the Fermi level,which give rise to a range of exotic,strongly correlated phenomena such as charge density waves(CDW)and superconductivity.Although the vHS from V 3d states have been implicated in CDW formation,their three-dimensional nature and temperature evolution remain poorly understood.In this study,we used high-resolution angle-resolved photoemission spectroscopy and density functional theory to reveal pronounced out-of-plane dispersion of vHS and their temperature dependence in KV_(3)Sb_(5).The identified c-axis band folding and scattering channels were directly linked to the CDW order.These results demonstrate that the CDW transition in this family involves cooperative coupling between electron correlations and structural modulation along the c axis.This offers new insights into the interplay of topology,correlations,and lattice instabilities in kagome metals.
基金National Key R&D Program of China,Grant/Award Numbers:2018YFE0203900,2020YFB1313600German Research Foundation,Hamburg Landesforschungsförderungsprojekt Cross,Grant/Award Number:Sonderforschungsbereich Transregio 169+2 种基金Shanghai Artificial Intelligence Innovation Development Special Support Project,Grant/Award Number:3920365001Horizon2020 RISE project STEP2DYNA,Grant/Award Number:691154National Natural Science Foundation of China,Grant/Award Numbers:61773083,62206168,62276048,U1813202。
文摘Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has become a mainstream solution but losing focus on the inter-sample class information.This paper proposes a new Credible Local Context Representation approach for SFDA.Our main idea is to exploit the credible local context for more discriminative representation.Specifically,we enhance the source model's discrimination by information regulating.To capture the context,a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context.In the epoch-wise adaptation,deep clustering-like training is conducted with two major updates.First,the context for all target data is constructed and then the context-fused pseudo-labels providing semantic guidance are generated.Second,for each target data,a weighting fusion on its context forms the anchored neighbourhood structure;thus,the deep clustering is switched from individual-based to coarse-grained.Also,a new regularisation building is developed on the anchored neighbourhood to drive the deep coarse-grained learning.Experiments on three benchmarks indicate that the proposed method can achieve stateof-the-art results.
文摘Methyl methoxyacetate(MMAc)and methyl formate(MF)can be produced directly by heterogeneous zeolite-catalyzed carbonylation and disproportionation of dimethoxymethane(DMM),with near 100%selectivity for each process.Despite continuous research efforts,the insight into the reaction mechanism and kinetics theory are still in their nascent stage.In this study,ZEO-1 material,a zeolite with up to now the largest cages comprising 16×16-MRs,16×12-MRs,and 12×12-MRs,was explored for DMM carbonylation and disproportionation reactions.The rate of MMAc formation based on accessible Brönsted acid sites is 2.5 times higher for ZEO-1(Si/Al=21)relative to the previously investigated FAU(Si/Al=15),indicating the positive effect of spatial separation of active sites in ZEO-1 on catalytic activity.A higher MF formation rate is also observed over ZEO-1 with lower activation energy(79.94 vs.95.19 kJ/mol)compared with FAU(Si/Al=30).Two types of active sites are proposed within ZEO-1 zeolite:Site 1 located in large cages formed by 16×16-MRs and 16×12-MRs,which is active predominantly for MMAc formation,and Site 2 located in smaller cages for methyl formate/dimethyl ether formation.Kinetics investigation of DMM carbonylation over ZEO-1 exhibit a first-order dependence on CO partial pressure and a slightly inverse-order dependence on DMM partial pressure.The DMM disproportionation is nearly first-order dependence on DMM partial pressure,while it reveals a strongly inverse dependence with increasing CO partial pressure.Furthermore,ZEO-1 exhibits good catalytic stability,and almost no deactivation is observed during the more than 70 h test with high carbonylation selectivity of above 89%,due to the well-enhanced diffusion property demonstrated by intelligent-gravimetric analysis.
基金National Key Research and Development Program of China(2021YFA1501302)the National Natural Science Foundation of China(22121004,22122808)+1 种基金the Haihe Laboratory of Sustainable Chemical Transformations and the Program of Introducing Talents of Discipline to Universities(BP0618007)for financial supportsupported by the XPLORER PRIZE.
文摘Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is approximated by the Gaussian distribution and added to experimental capacitance data with various intensities.The equivalent signal strength(Ф)that equals the signal-to-noise ratio of packed beds is used to evaluate noise levels.Results show that the Pearson correlation coefficient,which indicates the similarity of solids fraction distributions over pixels,increases with Ф,and reconstructed images are more deteriorated at lower Ф.Nevertheless,relative errors for average solids fraction and bubble size in each frame are less sensitive to noise,attributed to noise compromise caused by the process of pixel values.These findings provide useful guidance for assessing the accuracy of ECT measurements of multiphase flows.
基金supported by the National Natural Science Foundation of China(22308348)the Natural Science Foundation of Liaoning Province of China(2024-MSBA-65)+1 种基金the Qin Chuangyuan Project for Introducing High-Level Innovative and Entrepreneurial Talents(QCYRCXM-2023-024)the Energy Revolution S&T Program of Yulin Innovation Institute of Clean Energy(E201041206).
文摘Accurately acquiring catalyst size and morphology is essential for supporting catalytic reaction process design and optimal control. We report an intelligent catalyst sizing and morphological classification method based on the Mask-RCNN framework. A dataset of 9880 high-resolution images was captured by using a self-made fiber-optic endoscopic system for 13 kinds of silicoaluminophosphate-34 (SAPO-34) catalyst samples with different coke. Then there were approximately 877881 individual particles extracted from this dataset by our AI-based particle recognition algorithm. To clearly describe the morphology of irregular particles, we proposed a hybrid classification criterion that combines five different parameters, which are deformity, circularity, roundness, aspect ratio, and compactness. Therefore, catalyst morphology can be classified into two categories with four types. The first category includes regular types, such as the spherical, ellipsoidal, and rod-shaped types. And all the irregular types fall into the second category. The experimental results showed that a catalyst particle tends to be larger when its coke deposition increased. Whereas particle morphology remained primarily spherical and ellipsoidal, the ratio of each type varied slightly according to its coke. Our findings illustrate that this is a promising approach to be developing intelligent instruments for catalyst particle sizing and classification.
基金Supported by the Beijing Municipal Science and Technology Commission,No.Z191100006619002Haiyou Health High-Caliber Talent Project,No.202412the Research Unit of Minimally Invasive Pediatric Surgery on Diagnosis and Treatment,Chinese Academy of Medical Sciences,No.2021RU015.
文摘BACKGROUND Choledochal cysts(CC)and cystic biliary atresia(CBA)present similarly in early infancy but require different treatment approaches.While CC surgery can be delayed until 3-6 months of age in asymptomatic patients,CBA requires intervention within 60 days to prevent cirrhosis.AIM To develop a diagnostic model for early differentiation between these conditions.METHODS A total of 319 patients with hepatic hilar cysts(<60 days old at surgery)were retrospectively analyzed;these patients were treated at three hospitals between 2011 and 2022.Clinical features including biochemical markers and ultrasonographic measurements were compared between CC(n=274)and CBA(n=45)groups.Least absolute shrinkage and selection operator regression identified key diagnostic features,and 11 machine learning models were developed and compared.RESULTS The CBA group showed higher levels of total bile acid,total bilirubin,γ-glutamyl transferase,aspartate aminotransferase,and alanine aminotransferase,and direct bilirubin,while longitudinal diameter of the cysts and transverse diameter of the cysts were larger in the CC group.The multilayer perceptron model demonstrated optimal performance with 95.8% accuracy,92.9% sensitivity,96.3% specificity,and an area under the curve of 0.990.Decision curve analysis confirmed its clinical utility.Based on the model,we developed user-friendly diagnostic software for clinical implementation.CONCLUSION Our machine learning approach differentiates CC from CBA in early infancy using routinely available clinical parameters.Early accurate diagnosis facilitates timely surgical intervention for CBA cases,potentially improving patient outcomes.
文摘The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.
