Elect rides,which confine"excess anionic electrons"in subnanometer-sized cavities of a lattice,are exotic ionic crystals.We propose a non-stoichiometric strategy to realize intrinsic two-dimensional(2D)super...Elect rides,which confine"excess anionic electrons"in subnanometer-sized cavities of a lattice,are exotic ionic crystals.We propose a non-stoichiometric strategy to realize intrinsic two-dimensional(2D)superconducting elect ride.AlH_(2)monolayer,which is structurally identical to 1H-MoS_(2),possesses zero-dimensionally confined anionic electrons in the interstitial sites of A1 triangles,corresponding to a chemical formula of[AlH_(2)]^(+)e^(-).The interaction between interstitial anionic electrons(IAEs)and host cation lattice mainly accounts for stabilization of 1H-AlH_(2)electride.Impressively,1H-AlH_(2)monolayer is an intrinsic Bardeen-Cooper-Schrieffer superconductor with T_(c)=38 K,which is the direct consequence of strong coupling of the H-dominated high electronic states with Al acoustic branch vibrations and mid-frequency H-derived phonon softening modes caused by Kohn anomalies.Under tensile strain,IAEs transform into itinerant electrons,favoring the formation of stable Cooper pairs.Therefore,T_(c)reaches up to 53 K at a biaxial fracture strain of 5%.Our findings provide valuable insights into the correlation between non-stoichiometric electrides and superconducting microscopic mechanisms at the 2D limit.展开更多
[Objectives] To establish a method for the determination of active components in toad skin. [Methods] HPLC method was used to determine the content of five active components (bufotalin, cinobufotalin, bufalin, cinobuf...[Objectives] To establish a method for the determination of active components in toad skin. [Methods] HPLC method was used to determine the content of five active components (bufotalin, cinobufotalin, bufalin, cinobufagin and resibufogenin) in toad skin. [Results] Chromatographic conditions are as follows: Agilent ZORBAX SB-C 18 chromatographic column was used;acetonitrile (A)-0.3% glacial acetic acid (B) gradient elution (0-15 min, 28%A-54%A;15-35 min, 54%A-54%A) was conducted;the flow rate was 0.6 mL/min;the detection wavelength was 296 nm;the column temperature was 30 ℃;the sample size was 10 μL. Under the above conditions, the determination method of the five components can be established at one time. [Conclusions] The method was stable and reliable, and can provide experimental basis for the development and utilization of active ingredients in toad skin.展开更多
The machine-learning approach was investigated to predict the mechanical properties of Cu–Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new m...The machine-learning approach was investigated to predict the mechanical properties of Cu–Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new materials and provide physical insights into their properties.Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors.The results show that the sequential minimal optimization algorithm for support vector regression with a puk kernel(SMOreg/puk) model demonstrated the best prediction ability. Specifically, its predictions exhibited the highest correlation coefficient and lowest error among the predictions of the six models. The SMOreg/puk model was subsequently applied to predict the tensile strength and hardness of Cu–Al alloys and provide guidance for composition design to achieve the expected values. With the guidance of the SMOreg/puk model, Cu–12Al–6Ni alloy with a tensile strength(390 MPa) and hardness(HB 139) that reached the expected values was developed.展开更多
To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This pape...To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.展开更多
High-velocity compaction is an advanced compaction technique to obtain high-density compacts at a compaction velocity of ≤10 m/s. It was applied to various metallic powders and was verified to achieve a density great...High-velocity compaction is an advanced compaction technique to obtain high-density compacts at a compaction velocity of ≤10 m/s. It was applied to various metallic powders and was verified to achieve a density greater than 7.5 g/cm^3 for the Fe-based powders. The ability to rapidly and accurately predict the green density of compacts is important, especially as an alternative to costly and time-consuming materials design by trial and error. In this paper, we propose a machine-learning approach based on materials informatics to predict the green density of compacts using relevant material descriptors, including chemical composition, powder properties, and compaction energy. We investigated four models using an experimental dataset for appropriate model selection and found the multilayer perceptron model worked well, providing distinguished prediction performance, with a high correlation coefficient and low error values. Applying this model, we predicted the green density of nine materials on the basis of specific processing parameters. The predicted green density agreed very well with the experimental results for each material, with an inaccuracy less than 2%. The prediction accuracy of the developed method was thus confirmed by comparison with experimental results.展开更多
Interference alignment(IA) is one of the promising measures for the multi-user network to manage interference. The rank constraints rank minimization means that interference spans the lowest dimensional subspace and t...Interference alignment(IA) is one of the promising measures for the multi-user network to manage interference. The rank constraints rank minimization means that interference spans the lowest dimensional subspace and the useful signal spans all available spatial dimensions. In order to improve the performance of two-way relay network, we can use rank constrained rank minimization(RCRM) to solve the IA problem. This paper proposes left reweighted nuclear norm minimization-γalgorithm and selective coupling reweighted nuclear norm minimization algorithm to implement interference alignment in two-way relay networks. The left reweighted nuclear norm minimization-γ algorithm is based on reweighted nuclear norm minimization algorithm and has a novel γ choosing rule. The selective coupling reweighted nuclear norm minimization algorithm weighting methods choose according to singular value of interference matrixes. Simulation results show that the proposed algorithms considerably improve the sum rate performance and achieve the higher average achievable multiplexing gain in two-way relay interference networks.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12274050 and 91961204)the Fundamental Research Funds for the Central Universities(Grant Nos.DUT22LAB104 and DUT22ZD103)。
文摘Elect rides,which confine"excess anionic electrons"in subnanometer-sized cavities of a lattice,are exotic ionic crystals.We propose a non-stoichiometric strategy to realize intrinsic two-dimensional(2D)superconducting elect ride.AlH_(2)monolayer,which is structurally identical to 1H-MoS_(2),possesses zero-dimensionally confined anionic electrons in the interstitial sites of A1 triangles,corresponding to a chemical formula of[AlH_(2)]^(+)e^(-).The interaction between interstitial anionic electrons(IAEs)and host cation lattice mainly accounts for stabilization of 1H-AlH_(2)electride.Impressively,1H-AlH_(2)monolayer is an intrinsic Bardeen-Cooper-Schrieffer superconductor with T_(c)=38 K,which is the direct consequence of strong coupling of the H-dominated high electronic states with Al acoustic branch vibrations and mid-frequency H-derived phonon softening modes caused by Kohn anomalies.Under tensile strain,IAEs transform into itinerant electrons,favoring the formation of stable Cooper pairs.Therefore,T_(c)reaches up to 53 K at a biaxial fracture strain of 5%.Our findings provide valuable insights into the correlation between non-stoichiometric electrides and superconducting microscopic mechanisms at the 2D limit.
基金Supported by the Self-funded Research Project of Administration of Traditional Chinese Medicine of Guangxi Zhuang Autonomous Region(GXZYZ20210078)Key Research and Development Project of Guangxi Science and Technology Department(Guike AB19110003).
文摘[Objectives] To establish a method for the determination of active components in toad skin. [Methods] HPLC method was used to determine the content of five active components (bufotalin, cinobufotalin, bufalin, cinobufagin and resibufogenin) in toad skin. [Results] Chromatographic conditions are as follows: Agilent ZORBAX SB-C 18 chromatographic column was used;acetonitrile (A)-0.3% glacial acetic acid (B) gradient elution (0-15 min, 28%A-54%A;15-35 min, 54%A-54%A) was conducted;the flow rate was 0.6 mL/min;the detection wavelength was 296 nm;the column temperature was 30 ℃;the sample size was 10 μL. Under the above conditions, the determination method of the five components can be established at one time. [Conclusions] The method was stable and reliable, and can provide experimental basis for the development and utilization of active ingredients in toad skin.
基金financial support from the National Key Research and Development Program of China(No.2016YFB0700503)the National High Technology Research and Development Program of China(No.2015AA03420)+2 种基金Beijing Science and Technology Plan(No.D16110300240000)National Natural Science Foundation of China(No.51172018)the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJQN201801202)
文摘The machine-learning approach was investigated to predict the mechanical properties of Cu–Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new materials and provide physical insights into their properties.Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors.The results show that the sequential minimal optimization algorithm for support vector regression with a puk kernel(SMOreg/puk) model demonstrated the best prediction ability. Specifically, its predictions exhibited the highest correlation coefficient and lowest error among the predictions of the six models. The SMOreg/puk model was subsequently applied to predict the tensile strength and hardness of Cu–Al alloys and provide guidance for composition design to achieve the expected values. With the guidance of the SMOreg/puk model, Cu–12Al–6Ni alloy with a tensile strength(390 MPa) and hardness(HB 139) that reached the expected values was developed.
基金supported in part by the National Natural Science Foundation of China under grants 61901078,61771082,61871062,and U20A20157in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under grant KJQN201900609+2 种基金in part by the Natural Science Foundation of Chongqing under grant cstc2020jcyj-zdxmX0024in part by University Innovation Research Group of Chongqing under grant CXQT20017in part by the China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)under grant 2021FNA04008.
文摘To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.
基金financially supported by the National Key Research and Development Program of China (No. 2016YFB0700503)the National High Technology Research and Development Program of China (No. 2015AA034201)+2 种基金the Beijing Science and Technology Plan (No. D161100002416001)the National Natural Science Foundation of China (No. 51172018)Kennametal Inc
文摘High-velocity compaction is an advanced compaction technique to obtain high-density compacts at a compaction velocity of ≤10 m/s. It was applied to various metallic powders and was verified to achieve a density greater than 7.5 g/cm^3 for the Fe-based powders. The ability to rapidly and accurately predict the green density of compacts is important, especially as an alternative to costly and time-consuming materials design by trial and error. In this paper, we propose a machine-learning approach based on materials informatics to predict the green density of compacts using relevant material descriptors, including chemical composition, powder properties, and compaction energy. We investigated four models using an experimental dataset for appropriate model selection and found the multilayer perceptron model worked well, providing distinguished prediction performance, with a high correlation coefficient and low error values. Applying this model, we predicted the green density of nine materials on the basis of specific processing parameters. The predicted green density agreed very well with the experimental results for each material, with an inaccuracy less than 2%. The prediction accuracy of the developed method was thus confirmed by comparison with experimental results.
基金supported by the National Science Foundation of China (NO.61271240, 61671253)
文摘Interference alignment(IA) is one of the promising measures for the multi-user network to manage interference. The rank constraints rank minimization means that interference spans the lowest dimensional subspace and the useful signal spans all available spatial dimensions. In order to improve the performance of two-way relay network, we can use rank constrained rank minimization(RCRM) to solve the IA problem. This paper proposes left reweighted nuclear norm minimization-γalgorithm and selective coupling reweighted nuclear norm minimization algorithm to implement interference alignment in two-way relay networks. The left reweighted nuclear norm minimization-γ algorithm is based on reweighted nuclear norm minimization algorithm and has a novel γ choosing rule. The selective coupling reweighted nuclear norm minimization algorithm weighting methods choose according to singular value of interference matrixes. Simulation results show that the proposed algorithms considerably improve the sum rate performance and achieve the higher average achievable multiplexing gain in two-way relay interference networks.
