Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new genera...Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.展开更多
BDAR analysis is a kind of very complex analysis technology which can be used to determine the battle damage mode of equipment and relevant decision strategy. BDAR analysis is based on the information about FMEA/DMEA,...BDAR analysis is a kind of very complex analysis technology which can be used to determine the battle damage mode of equipment and relevant decision strategy. BDAR analysis is based on the information about FMEA/DMEA, MA(maintenance analysis), FTA(fault tree analysis) and vulnerability analysis. These information and analysis results are obtained on the basis of the domain expert's experience and knowledge. Upon the basis of the summary of BDARA methods, this paper provides applied knowledge database, puts forward BDARA's integrated thinking, implementing methods, and the key technology for IBDARA knowledge database's development.展开更多
The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to th...The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.展开更多
A method of how to describe expert system using relative data model and the realization of inference using data search in support of database management system is introduced in this article.Thereby,the database system...A method of how to describe expert system using relative data model and the realization of inference using data search in support of database management system is introduced in this article.Thereby,the database system is promoted from data processing up to knowledge processing,and a practical method of how to develop expert system using the popular database developing tools is proposed.展开更多
Following the example of other industrial activities, mining evaluation is now exposed to socio-economical and technological constraints which are unstable in quick evolution. The keys to its success are increasingly ...Following the example of other industrial activities, mining evaluation is now exposed to socio-economical and technological constraints which are unstable in quick evolution. The keys to its success are increasingly related to a methodology of work more scientific than ever. The Systemic Approach has broadly showed its effectiveness in numerous disciplinary fields, both scientific and engineering ones: Biology, Economy, Social and Management Sciences, Quality Management, Information Systems… Helped by technological progress, this approach has especially excelled in the management of spatial information (e.g. GIS). It constitutes therefore an excellent solution to the problems of mining evaluation by the integration of genetic, mining and managerial data within an Information System, thus optimizing scientific and economic valuation of mineral resources.展开更多
In this paper, we conduct research on the new English language teaching approach based on sharing of the knowledge economy under the MOOC background. College English teaching is the core part of education in colleges ...In this paper, we conduct research on the new English language teaching approach based on sharing of the knowledge economy under the MOOC background. College English teaching is the core part of education in colleges and universities that are leading an indicator of college teaching can reveal most times the imprinting to teaching in colleges and universities. College English teaching from scratch, since the childhood, beyond the difficulty of the teaching area, through the teaching of confusion, but always insist on teaching idea of development as the center, continuously innovative fusion of gesture to the new development of English teaching to maximize the realization of the value of English teaching and cultivate highly qualified English talents for the society. Our research uses the MOOC platform to optimize the traditional education pattern which is innovative.展开更多
Emerging technologies are now initiating new industries and transforming old ones with tremendous power. They are different games compared with established technologies with distinctive characteristics of knowledge ma...Emerging technologies are now initiating new industries and transforming old ones with tremendous power. They are different games compared with established technologies with distinctive characteristics of knowledge management in knowledge-based and technological-innovation-based competition. How to obtain knowledge advantage and enhance competences by knowledge sharing for emerging-technology-based strategic alliances （ETBSA） is what we concern in this paper. On the basis of our previous work on emerging technologies＇ distinctive attributes, we counter the wide spread presumption that the primary purpose of strategic alliances is knowledge acquiring by means of learning. We offers new insight into the knowledge sharing approaches of ETBSAs - the knowledge integrating approach by which each member firm integrates its partner＇s complementary knowledge base into the products and services and maintains its own knowledge specialization at the same time. So that ETBSAs should plan and practice their knowledge sharing strategies from the angle of knowledge integrating rather than knowledge acquiring. A four-dimensional framework is developed to analyze the advantages and disadvantages of these two knowledge sharing approaches. Some cases in electronic industry are introduced to illustrate our point of view.展开更多
The continuously updated database of failures and censored data of numerous products has become large, and on some covariates, information regarding the failure times is missing in the database. As the dataset is larg...The continuously updated database of failures and censored data of numerous products has become large, and on some covariates, information regarding the failure times is missing in the database. As the dataset is large and has missing information, the analysis tasks become complicated and a long time is required to execute the programming codes. In such situations, the divide and recombine (D&R) approach, which has a practical computational performance for big data analysis, can be applied. In this study, the D&R approach was applied to analyze the real field data of an automobile component with incomplete information on covariates using the Weibull regression model. Model parameters were estimated using the expectation maximization algorithm. The results of the data analysis and simulation demonstrated that the D&R approach is applicable for analyzing such datasets. Further, the percentiles and reliability functions of the distribution under different covariate conditions were estimated to evaluate the component performance of these covariates. The findings of this study have managerial implications regarding design decisions, safety, and reliability of automobile components.展开更多
Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and...Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.展开更多
文摘Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.
文摘BDAR analysis is a kind of very complex analysis technology which can be used to determine the battle damage mode of equipment and relevant decision strategy. BDAR analysis is based on the information about FMEA/DMEA, MA(maintenance analysis), FTA(fault tree analysis) and vulnerability analysis. These information and analysis results are obtained on the basis of the domain expert's experience and knowledge. Upon the basis of the summary of BDARA methods, this paper provides applied knowledge database, puts forward BDARA's integrated thinking, implementing methods, and the key technology for IBDARA knowledge database's development.
文摘The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.
文摘A method of how to describe expert system using relative data model and the realization of inference using data search in support of database management system is introduced in this article.Thereby,the database system is promoted from data processing up to knowledge processing,and a practical method of how to develop expert system using the popular database developing tools is proposed.
文摘Following the example of other industrial activities, mining evaluation is now exposed to socio-economical and technological constraints which are unstable in quick evolution. The keys to its success are increasingly related to a methodology of work more scientific than ever. The Systemic Approach has broadly showed its effectiveness in numerous disciplinary fields, both scientific and engineering ones: Biology, Economy, Social and Management Sciences, Quality Management, Information Systems… Helped by technological progress, this approach has especially excelled in the management of spatial information (e.g. GIS). It constitutes therefore an excellent solution to the problems of mining evaluation by the integration of genetic, mining and managerial data within an Information System, thus optimizing scientific and economic valuation of mineral resources.
文摘In this paper, we conduct research on the new English language teaching approach based on sharing of the knowledge economy under the MOOC background. College English teaching is the core part of education in colleges and universities that are leading an indicator of college teaching can reveal most times the imprinting to teaching in colleges and universities. College English teaching from scratch, since the childhood, beyond the difficulty of the teaching area, through the teaching of confusion, but always insist on teaching idea of development as the center, continuously innovative fusion of gesture to the new development of English teaching to maximize the realization of the value of English teaching and cultivate highly qualified English talents for the society. Our research uses the MOOC platform to optimize the traditional education pattern which is innovative.
文摘Emerging technologies are now initiating new industries and transforming old ones with tremendous power. They are different games compared with established technologies with distinctive characteristics of knowledge management in knowledge-based and technological-innovation-based competition. How to obtain knowledge advantage and enhance competences by knowledge sharing for emerging-technology-based strategic alliances （ETBSA） is what we concern in this paper. On the basis of our previous work on emerging technologies＇ distinctive attributes, we counter the wide spread presumption that the primary purpose of strategic alliances is knowledge acquiring by means of learning. We offers new insight into the knowledge sharing approaches of ETBSAs - the knowledge integrating approach by which each member firm integrates its partner＇s complementary knowledge base into the products and services and maintains its own knowledge specialization at the same time. So that ETBSAs should plan and practice their knowledge sharing strategies from the angle of knowledge integrating rather than knowledge acquiring. A four-dimensional framework is developed to analyze the advantages and disadvantages of these two knowledge sharing approaches. Some cases in electronic industry are introduced to illustrate our point of view.
文摘The continuously updated database of failures and censored data of numerous products has become large, and on some covariates, information regarding the failure times is missing in the database. As the dataset is large and has missing information, the analysis tasks become complicated and a long time is required to execute the programming codes. In such situations, the divide and recombine (D&R) approach, which has a practical computational performance for big data analysis, can be applied. In this study, the D&R approach was applied to analyze the real field data of an automobile component with incomplete information on covariates using the Weibull regression model. Model parameters were estimated using the expectation maximization algorithm. The results of the data analysis and simulation demonstrated that the D&R approach is applicable for analyzing such datasets. Further, the percentiles and reliability functions of the distribution under different covariate conditions were estimated to evaluate the component performance of these covariates. The findings of this study have managerial implications regarding design decisions, safety, and reliability of automobile components.
基金supported by the Innovation Fund for Medical Sciences of the Chinese Academy of Medical Sciences(2021-I2M-1-033)the National Key Research and Development Program of China(2022YFF0711900).
文摘Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.
