Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news text...Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.展开更多
The catalytic hydrogenation of 2-nitro-4-acetylamino anisole(NMA)is a less-polluting and efficient method to produce 2-amino-4-acetamino anisole(AMA).However,the kinetics of catalytic hydrogenation of NMA to AMA remai...The catalytic hydrogenation of 2-nitro-4-acetylamino anisole(NMA)is a less-polluting and efficient method to produce 2-amino-4-acetamino anisole(AMA).However,the kinetics of catalytic hydrogenation of NMA to AMA remains obscure.In this work,the kinetic models including power-law model and Langmuir-Hinshelwood-Hougen-Watson(LHHW)model of NMA hydrogenation to AMA catalyzed by Raney nickel catalyst were investigated.All experiments were carried out under the elimination of mass transfer resistance within the temperature range of 70–100°C and the hydrogen pressure of 0.8–1.5 MPa.The reaction was found to follow 0.52-order kinetics with respect to the NMA concentration and 1.10-order kinetics in terms of hydrogen pressure.Based on the LHHW model,the dual-site dissociation adsorption of hydrogen was analyzed to be the rate determining step.The research of intrinsic kinetics of NMA to AMA provides the guidance for the reactor design and inspires the catalyst modification.展开更多
A Coupling Magneto-Electro-Elastic(MEE)Node-based Smoothed Radial Point Interpolation Method(CM-NS-RPIM)was proposed to solve the free vibration and transient responses of Functionally Graded Magneto-Electro-Elastic(F...A Coupling Magneto-Electro-Elastic(MEE)Node-based Smoothed Radial Point Interpolation Method(CM-NS-RPIM)was proposed to solve the free vibration and transient responses of Functionally Graded Magneto-Electro-Elastic(FGMEE)structures.By introducing the modified Newmark method,the displacement,electrical potential and magnetic potential of the structures under transient mechanical loading were obtained.Based on G space theory and the weakened weak(W2)formulation,the equations of the multi-physics coupling problems were derived.Using triangular background elements,the free vibration and transient responses of three numerical examples were studied.Results proved that CM-NS-RPIM performed better than the standard FEM by reducing the overly-stiff of structures.Moreover,CM-NS-RPIM could reduce the number of nodes while guaranteeing the accuracy.Besides,triangular elements could be generated automatically even for complex geometries.Therefore,the effectiveness and validity of CM-NS-RPIM were demonstrated,which were valuable for the design of intelligence devices,such as energy harvesters and sensors.展开更多
基金supported by National Key R&D Program of China(2022QY2000-02).
文摘Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.
基金the National Natural Science Foun-dation of China(22022802 and 22288102).
文摘The catalytic hydrogenation of 2-nitro-4-acetylamino anisole(NMA)is a less-polluting and efficient method to produce 2-amino-4-acetamino anisole(AMA).However,the kinetics of catalytic hydrogenation of NMA to AMA remains obscure.In this work,the kinetic models including power-law model and Langmuir-Hinshelwood-Hougen-Watson(LHHW)model of NMA hydrogenation to AMA catalyzed by Raney nickel catalyst were investigated.All experiments were carried out under the elimination of mass transfer resistance within the temperature range of 70–100°C and the hydrogen pressure of 0.8–1.5 MPa.The reaction was found to follow 0.52-order kinetics with respect to the NMA concentration and 1.10-order kinetics in terms of hydrogen pressure.Based on the LHHW model,the dual-site dissociation adsorption of hydrogen was analyzed to be the rate determining step.The research of intrinsic kinetics of NMA to AMA provides the guidance for the reactor design and inspires the catalyst modification.
基金co-supported by the National Key R&D Program of China(Nos.2018YFF01012401-05)the National Natural Science Foundation of China(No.51975243)+2 种基金Jilin Provincial Department of Education(No.JJKH20180084KJ),Chinathe Fundamental Research Funds for the Central Universities and Jilin Provincial Department of Science&Technology Fund Project,China(Nos.20170101043JC and 20180520072JH)Graduate Innovation Fund of Jilin University,China(No.101832018C184).
文摘A Coupling Magneto-Electro-Elastic(MEE)Node-based Smoothed Radial Point Interpolation Method(CM-NS-RPIM)was proposed to solve the free vibration and transient responses of Functionally Graded Magneto-Electro-Elastic(FGMEE)structures.By introducing the modified Newmark method,the displacement,electrical potential and magnetic potential of the structures under transient mechanical loading were obtained.Based on G space theory and the weakened weak(W2)formulation,the equations of the multi-physics coupling problems were derived.Using triangular background elements,the free vibration and transient responses of three numerical examples were studied.Results proved that CM-NS-RPIM performed better than the standard FEM by reducing the overly-stiff of structures.Moreover,CM-NS-RPIM could reduce the number of nodes while guaranteeing the accuracy.Besides,triangular elements could be generated automatically even for complex geometries.Therefore,the effectiveness and validity of CM-NS-RPIM were demonstrated,which were valuable for the design of intelligence devices,such as energy harvesters and sensors.
