Fast growing demands for electric vehicles require better longevity,safety and reliability for next-generation high-energy battery technologies.A data-centered battery management system is thus desired to interpret co...Fast growing demands for electric vehicles require better longevity,safety and reliability for next-generation high-energy battery technologies.A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics.Nowadays,Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance,health and safety,but is hurdled by a scarcity of data.To mitigate this issue,this study presents one of the first studies for data development through both experimental studies and three-dimensional(3-D)multi-physics modeling to underpin a deep learning framework with indepth examination for battery performance and thermal risk prediction.Specifically,PartⅠfocused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model ac-curacy by two steps:firstly,we validated the multi-physics model against two commercial Lithium-ion batteries,i.e.,Panasonic NCR18650B and 18650BD;Then,the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model,such as voltage evolution and maximum local temperature(hot spot).The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory(CNN-LSTM)in partⅡ.展开更多
In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ...In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.展开更多
On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th Nation...On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.展开更多
Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain ...Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.展开更多
Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing...Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.展开更多
Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial impor...Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial importance to farmers, researchers, and decision makers while utilizing and managing red soil resources. SIS created by using ARC/INPO was used to provide data acquisition, systematic model parameter assignment, and visual display of analytic results. Topography, temperature, soil component (e.g., organic matter and pH) and condition of agricultural production were selected as parameters of ISODATA model. Taking Longyou County, Zhejiang Province as the case study area, the effect of the integration and recommendations are discussed for future research.展开更多
Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring th...Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register(ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.展开更多
Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by...Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms： ① a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding ＂Made-in-China＂ tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.展开更多
The test floor effects seen in standardised tests lead to a standardised score of 1 or less with a flat profile that hides a child’s individual strengths and needs. The Griffiths III community of practitioners reques...The test floor effects seen in standardised tests lead to a standardised score of 1 or less with a flat profile that hides a child’s individual strengths and needs. The Griffiths III community of practitioners requested advice on the reporting of children’s development below the floor of the test, so that individual strengths and needs can be described. This paper reports the third phase of research following an earlier Scoping Review and a wider literature review. To confirm quality control, Phase 3 was conducted in a retrospective manner using the same methodology as the earlier phases but in a reverse direction. Peer reviewer comments and key elements from the Scoping Review and keywords from the publications were tabulated. Data analysis included a change of perspective to that of the child and their individual rights with respect to the literature themes already described in Phase 2. These confirmed that there is little specific guidance in the literature, but that computational advances for homogeneous populations and especially disaggregated data offer some solutions. A greater balance between broad biopsychosocial models and standardised models of assessment should be sought by practitioners together with the use of disaggregated data to highlight issues that pertain to individual subsets of results. This will ensure that the child’s right for their individual strengths and needs to be described together with a plan for management, may be met.展开更多
End-host address mutation is one of the key network moving target defense mechanisms to defend against reconnaissance.However,frequently changing host addresses increases the transmission de-lay of active sessions,whi...End-host address mutation is one of the key network moving target defense mechanisms to defend against reconnaissance.However,frequently changing host addresses increases the transmission de-lay of active sessions,which may cause serious ram-ifications.In this paper,by leveraging the advanced DPDK technology,we proposed a high-performance MTD gateway framework,called HPMG,which can not only prevent adversaries from reconnaissance ef-fectively,but also retain high-speed data packet pro-cessing capabilities.Firstly,every moving target host is assigned three different IP addresses,called real IP,virtual IP,and external IP,to realize multi-level net-work address architecture.To delay the scanning tech-niques of adversaries,HPMG mutates virtual IP and virtual MAC addresses,and replies with fake host re-sponses.Besides,to be transparent to the end-hosts,HPMG keeps real IP and real MAC unchanged.Fi-nally,we optimized the forwarding and processing performance of the HPMG based on the fast path framework of DPDK.Our theoretical analysis,imple-mentation,and evaluation show that HPMG can effec-tively defend against reconnaissance attacks and de-crease the processing delay caused by address muta-tion.展开更多
文摘Fast growing demands for electric vehicles require better longevity,safety and reliability for next-generation high-energy battery technologies.A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics.Nowadays,Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance,health and safety,but is hurdled by a scarcity of data.To mitigate this issue,this study presents one of the first studies for data development through both experimental studies and three-dimensional(3-D)multi-physics modeling to underpin a deep learning framework with indepth examination for battery performance and thermal risk prediction.Specifically,PartⅠfocused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model ac-curacy by two steps:firstly,we validated the multi-physics model against two commercial Lithium-ion batteries,i.e.,Panasonic NCR18650B and 18650BD;Then,the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model,such as voltage evolution and maximum local temperature(hot spot).The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory(CNN-LSTM)in partⅡ.
文摘In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.
文摘On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.
文摘Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.
文摘Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.
文摘Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial importance to farmers, researchers, and decision makers while utilizing and managing red soil resources. SIS created by using ARC/INPO was used to provide data acquisition, systematic model parameter assignment, and visual display of analytic results. Topography, temperature, soil component (e.g., organic matter and pH) and condition of agricultural production were selected as parameters of ISODATA model. Taking Longyou County, Zhejiang Province as the case study area, the effect of the integration and recommendations are discussed for future research.
基金support of the European Commission ETER Project (No. 934533-2017-AO8-CH)H2020 RISIS 2 project (No. 824091)。
文摘Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register(ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.
基金We are grateful to the support from the National Natural Science Foundation of China （NSFC） （31425002, 91231205, 81430011, 61303161, 31470220, and 31327001）, and the Frontier Science Research Program, the Soil-Microbe System Function and Regulation Program, and the Science and Technology Service Network Initiative （STS） from the Chinese Academy of Sciences （CAS）.
文摘Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms： ① a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding ＂Made-in-China＂ tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.
文摘The test floor effects seen in standardised tests lead to a standardised score of 1 or less with a flat profile that hides a child’s individual strengths and needs. The Griffiths III community of practitioners requested advice on the reporting of children’s development below the floor of the test, so that individual strengths and needs can be described. This paper reports the third phase of research following an earlier Scoping Review and a wider literature review. To confirm quality control, Phase 3 was conducted in a retrospective manner using the same methodology as the earlier phases but in a reverse direction. Peer reviewer comments and key elements from the Scoping Review and keywords from the publications were tabulated. Data analysis included a change of perspective to that of the child and their individual rights with respect to the literature themes already described in Phase 2. These confirmed that there is little specific guidance in the literature, but that computational advances for homogeneous populations and especially disaggregated data offer some solutions. A greater balance between broad biopsychosocial models and standardised models of assessment should be sought by practitioners together with the use of disaggregated data to highlight issues that pertain to individual subsets of results. This will ensure that the child’s right for their individual strengths and needs to be described together with a plan for management, may be met.
基金supported by National Natural Science Foundation of China(No.61821001)Science and Tech-nology Key Project of Guangdong Province,China(2019B010157001).
文摘End-host address mutation is one of the key network moving target defense mechanisms to defend against reconnaissance.However,frequently changing host addresses increases the transmission de-lay of active sessions,which may cause serious ram-ifications.In this paper,by leveraging the advanced DPDK technology,we proposed a high-performance MTD gateway framework,called HPMG,which can not only prevent adversaries from reconnaissance ef-fectively,but also retain high-speed data packet pro-cessing capabilities.Firstly,every moving target host is assigned three different IP addresses,called real IP,virtual IP,and external IP,to realize multi-level net-work address architecture.To delay the scanning tech-niques of adversaries,HPMG mutates virtual IP and virtual MAC addresses,and replies with fake host re-sponses.Besides,to be transparent to the end-hosts,HPMG keeps real IP and real MAC unchanged.Fi-nally,we optimized the forwarding and processing performance of the HPMG based on the fast path framework of DPDK.Our theoretical analysis,imple-mentation,and evaluation show that HPMG can effec-tively defend against reconnaissance attacks and de-crease the processing delay caused by address muta-tion.
