Ontology mapping is the bottleneck of handling conflicts among heterogeneous ontologies and of implementing reconfiguration or interoperability of legacy systems. We proposed an ontology mapping method by using machin...Ontology mapping is the bottleneck of handling conflicts among heterogeneous ontologies and of implementing reconfiguration or interoperability of legacy systems. We proposed an ontology mapping method by using machine learning, type constraints and logic mining techniques. This method is able to find concept correspondences through instances and the result is optimized by using an error function; it is able to find attribute correspondence between two equivalent concepts and the mapping accuracy is enhanced by combining together instances learning, type constraints and the logic relations that are imbedded in instances; moreover, it solves the most common kind of categorization conflicts. We then proposed a merging algorithm to generate the shared ontology and proposed a reconfigurable architecture for interoperation based on multi agents. The legacy systems are encapsulated as information agents to participate in the integration system. Finally we give a simplified case study.展开更多
Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were hi...Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.展开更多
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage...Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.展开更多
基金国家高技术研究发展计划(863计划)，国家自然科学基金，Shanghai Commission of Science and Technology Key Project
文摘Ontology mapping is the bottleneck of handling conflicts among heterogeneous ontologies and of implementing reconfiguration or interoperability of legacy systems. We proposed an ontology mapping method by using machine learning, type constraints and logic mining techniques. This method is able to find concept correspondences through instances and the result is optimized by using an error function; it is able to find attribute correspondence between two equivalent concepts and the mapping accuracy is enhanced by combining together instances learning, type constraints and the logic relations that are imbedded in instances; moreover, it solves the most common kind of categorization conflicts. We then proposed a merging algorithm to generate the shared ontology and proposed a reconfigurable architecture for interoperation based on multi agents. The legacy systems are encapsulated as information agents to participate in the integration system. Finally we give a simplified case study.
基金the financial supports from the National Key Research and Development Program of China (No. 2022YFB3707501)the National Natural Science Foundation of China (No. 51701083)+1 种基金the GDAS Project of Science and Technology Development, China (No. 2022GDASZH2022010107)the Guangzhou Basic and Applied Basic Research Foundation, China (No. 202201010686)。
文摘Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.
基金supported by the National Natural Science Foundation of China (21210003 and 81230076 to H.J., 81773634 to M.Z. and 81430084 to K.C.)the “Personalized Medicines-Molecular Signature-based Drug Discovery and Development”, Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12050201 to M.Z.)+1 种基金National Key Research & Development Plan (2016YFC1201003 to M.Z.)the National Basic Research Program (2015CB910304 to X.L.)
文摘Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.
