NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large lan...NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.展开更多
In this paper, we present a framework for the generation and control of an Internet-based 3-dimensional game virtual environment that allows a character to navigate through the environment. Our framework includes 3-di...In this paper, we present a framework for the generation and control of an Internet-based 3-dimensional game virtual environment that allows a character to navigate through the environment. Our framework includes 3-dimensional terrain mesh data processing, a map editor, scene processing, collision processing, and walkthrough control. We also define an environment-specific semantic information editor, which can be applied using specific location obtained from the real world. Users can insert text information related to the characters real position in the real world during navigation in the game virtual environment.展开更多
Customer relationship management systems are gaining importance in today's business environment since customer satisfaction is crucial to the success of an enterprise, and especially so in e-business environment w...Customer relationship management systems are gaining importance in today's business environment since customer satisfaction is crucial to the success of an enterprise, and especially so in e-business environment where customers can find substitute suppliers quite easily. In CRM, the quality of customer information is very important, and the address information even more so. It is because the address information plays a major role for customer contact channel and for timely and effective marketing service. Furthermore, it gives the basic source of geographic information for the offline delivery, the terminal activity of the e-commerce. In this study, we analyze various standards and proposals for the address information, and propose data models for the management of the information focusing on address components, and proto-type systems for management and service.展开更多
基金supported by the Jiangsu Provincial Science and Technology Project Basic Research Program(Natural Science Foundation of Jiangsu Province)(No.BK20211283).
文摘NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.
文摘In this paper, we present a framework for the generation and control of an Internet-based 3-dimensional game virtual environment that allows a character to navigate through the environment. Our framework includes 3-dimensional terrain mesh data processing, a map editor, scene processing, collision processing, and walkthrough control. We also define an environment-specific semantic information editor, which can be applied using specific location obtained from the real world. Users can insert text information related to the characters real position in the real world during navigation in the game virtual environment.
文摘Customer relationship management systems are gaining importance in today's business environment since customer satisfaction is crucial to the success of an enterprise, and especially so in e-business environment where customers can find substitute suppliers quite easily. In CRM, the quality of customer information is very important, and the address information even more so. It is because the address information plays a major role for customer contact channel and for timely and effective marketing service. Furthermore, it gives the basic source of geographic information for the offline delivery, the terminal activity of the e-commerce. In this study, we analyze various standards and proposals for the address information, and propose data models for the management of the information focusing on address components, and proto-type systems for management and service.
