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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ObjectiveThrough data collection and analysis, the method of evaluating the operation quality of large medical equipment in children's hospital is explored and suggestions on the use and configuration of large medical equipment is put forward. MethodsCollect the equipment operation data through the Internet of Things, and combine the hospital HIS, RIS, HRP and other information system data to establish the operation evaluation system of large medical equipment of children's hospital. CRITIC method is used to quantitatively evaluate single type of equipment and single equipment. ResultsHospital big data platform realizes the longitudinal analysis of the operation data of a single large equipment and forms a visual chart, which is displayed on the PC and mobile terminals. Municipal platform can conduct horizontal analysis on the equipment operation data to realize the comprehensive quantitative evaluation of the operation level of large equipment of children's hospital and put forward suggestions for use and configuration. ConclusionA large equipment operation evaluation system for children's hospital is established through data collection and analysis, and the fine management level of large medical equipment is improved.

The wide application of advanced information technologies such as big data, Internet of Things and artificial intelligence in agriculture has promoted the modernization of agriculture in rural areas and the development of smart agriculture. This trend has also led to the boost of demands for technology and knowledge from a large amount of agricultural business entities. Faced with problems such as dispersiveness of knowledges, hysteric knowledge update, inadequate agricultural information service and prominent contradiction between supply and demand of knowledge, the agricultural knowledge service has become an important engine for the transformation, upgrading and high-quality development of agriculture. To better facilitate the agriculture modernization in China, the research and application perspectives of agricultural knowledge services were summarized and analyzed. According to the whole life cycle of agricultural data, based on the whole agricultural industry chain, a systematic framework for the construction of agricultural intelligent knowledge service systems towards the requirement of agricultural business entities was proposed. Three layers of techniques in necessity were designed, ranging from AIoT-based agricultural situation perception to big data aggregation and governance, and from agricultural knowledge organization to computation/mining based on knowledge graph and then to multi-scenario-based agricultural intelligent knowledge service. A wide range of key technologies with comprehensive discussion on their applications in agricultural intelligent knowledge service were summarized, including the aerial and ground integrated Artificial Intelligence & Internet-of-Things (AIoT) full-dimensional of agricultural condition perception, multi-source heterogeneous agricultural big data aggregation/governance, knowledge modeling, knowledge extraction, knowledge fusion, knowledge reasoning, cross-media retrieval, intelligent question answering, personalized recommendation, decision support. At the end, the future development trends and countermeasures were discussed, from the aspects of agricultural data acquisition, model construction, knowledge organization, intelligent knowledge service technology and application promotion. It can be concluded that the agricultural intelligent knowledge service is the key to resolve the contradiction between supply and demand of agricultural knowledge service, can provide support in the realization of the advance from agricultural cross-media data analytics to knowledge reasoning, and promote the upgrade of agricultural knowledge service to be more personalized, more precise and more intelligent. Agricultural knowledge service is also an important support for agricultural science and technologies to be more self-reliance, modernized, and facilitates substantial development and upgrading of them in a more effective manner.

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Knowledge graph is a large-scale semantic network that gives machine background knowledge. Using knowledge graph to organize heterogeneous medical information can effectively improve the utilization value of massive medical resources and promote the development of medical intelligence. This paper describes the research, construction and application status of knowledge graph in medical field from three dimensions: the key technology of knowledge graph, the construction of medical knowledge graph and the application of medical knowledge graph, and explores the topics worthy of research in the future. Firstly, the development of knowledge representation, knowledge extraction, knowledge fusion and knowledge inference are systematically summarized, their latest progress is discussed, and the technical difficulties in the construction of Chinese medical knowledge graph are analyzed. Secondly, the existing research on Chinese medical knowledge graph is illustrated from three perspectives of medical ontology, general practice knowledge graph and single disease medical knowledge graph. The research characteristics of Chinese medical knowledge graph are also analyzed. Finally, the application of medical know-ledge graph in semantic search, decision support and intelligent question answering are analyzed, and the new app-lication scenarios are discussed. In view of the challenges faced by Chinese medical knowledge graph, such as low standardization of terminology, lack of annotated corpus, insufficient technical research and limitations of applica-tion scenarios, the future research directions of Chinese medical knowledge graph are prospected.

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, small-scale agriculture is dominating in China.How to develop appropriate smart agriculture for agricultural management with small farmers and small plots is quite challenging.A distributed agricultural AI framework combining federal learning and blockchain technology was proposed, which can achieve the purpose of the training model and establish the incentive mechanism for participants without data sharing.This framework helped make full use of agricultural multi-source heterogeneous data, reducing user data requirements, developing decision-making models according to local conditions, and promoting the connection of production and marketing of small-scale agriculture.

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Agriculture is the property of Smart Agriculture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using Taklimakan desert highway and its sand-breaking system as the research object for complex system management and control, the ACP-based parallel intelligence theory to deal with the problems of the difficulty in modeling, analyzing, and predicting of sand-breaking system was applied, to realize the intelligent decision support for sand-breaking system management and control, support the sustainable development of aeolian environment.The expert experience knowledge to construct the artificial desert highway sand-breaking system was extracted by simulating physical process.The sand-control efficiency index of the artificial system was calculated using equations, then evaluated and modified by comparing and learning with the actual system.Using parallel intelligent theory, the artificial system and the actual system can learn from each other to provide decision support for sand control.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning is a major means to realize artificial intelligence (AI), which can provide support for decision making through exploration of data law and establishment of prediction model. Since the oil and gas pipeline system has the characteristics of numerous equipment, complicated structure and complex technology, machine learning is introduced to solve the problems that are hard to solve with pure mathematical models with AI technology and to replace personnel to complete some boring, tedious and dangerous tasks. Here, the application of 3 types of machine leaning, such as deep learning, reinforcement learning and transfer learning, were reviewed with reference to the production process of oil and gas pipeline system, covering the application scenarios like pipeline leakage, recognition of multi-phase flow pattern, equipment fault diagnosis and tank target detection. The application framework of AI technology in oil and gas pipeline systems was established, and it was also pointed out that the application of deep learning, reinforcement learning and transfer learning in this field was prospective. Finally, the application of machine learning in oil and gas pipelines was prospected, so as to provide references for the intelligent research and development of oil and gas pipeline systems.

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is imperative to prevent interregional transmission in the early stages of an epidemic for both controlling the epidemic and ensuring socioeconomic stability. The premises of such exercise are knowing the present and upcoming spatial distribution of any existing cases. During the coronavirus disease 2019 (CO‐ VID ‐19) epidemic, researchers have used location‐based services data to extract the origins and destinations of travelers and thus analyze the spatial distribution of the epidemic. However, these data can only provide positions of long ‐ term stays of travelers, but not short ‐ term stops and the vehicles they are taking, which are also common spaces of transmission. Hence it is necessary to introduce online transportation data such as route recommendation and train tables to characterize the route taken by interregional travelers when evaluating the distribution of existing cases. We propose an approach to support risk evaluation of regional epidemic spread and regional transportation control, aiming to improve our spatial governing capabilities in face of an epidemic. It involves estimating outflow cases using recent population flows and previous compa‐ rable flows, projecting the probable route they will take using online map route recommendation and flight calendar/train tables, locating short ‐ term stops according to the projected routes, and thus formulating transportation restriction policies to lower further regional transmission. The key and distinct step of this ap‐ proach is to locate potential stops of regional travelers, which is achieved by combining the proportion of transportation mode choice and minimum time strategy. The effectiveness and necessity of introducing probable routes are verified with active cases data, population flow data and transportation data in January, 2020. Results show that introducing anticipated short ‐ term stops significantly improves the fitting perfor‐ mance of population flow data to spatial distribution of active COVID‐19 cases. [ABSTRACT FROM AUTHOR]

The Heihe River Basin is the second largest inland river basin in China. During the last few decades, this region has been faced with a serious of severe ecological and environmental problems caused by rapid economic growth and climate change. Two decades ago, with the increase of population and water consumption in the upper and middle reaches, the ecological water consumption in the lower reaches was greatly squeezed, which eventually led to serious ecological disasters, such as the shrinkage of the natural oasis, the drying up of the lakes, and frequent sandstorms in the lower reaches. The sustainable development of the region has been seriously challenged. To accurately monitor changes in hydrological and meteorological variables at watershed scale and support the wise use of water resources in this region, the Heihe Remote Sensing Experimental Research Station (HRSERS) was founded in 2009. The foundation of HRSERS has greatly improved the level of scientific research and decision support in the Heihe River Basin. The main achievements include: (1) the first watershed scale (with an area of 143000 km2) integrated observation system in China is constructed, and a large number of long-term observation data are obtained. More than 200 papers have been published using the observed dataset; (2) key eco-environmental variables in Heihe River Basin are retrieved from remote sensing data; (3) the theoretical framework and method system for the authenticity inspection of heterogeneous surface remote sensing products have been established, and some related national standards have been approved; (4) the eco-hydrological model is improved by adding glacier, snow and permafrost modular, and the eco-hydrology-economic coupling model is developed for the whole Heihe River Basin to full couple the natural processes and social processes; (5) the United Nations Sustainable Development Goals (SDGs) were monitored at the basin scale and regional scale, and provides scientific support for the sustainable development and decision-making of Heihe River Basin. [ABSTRACT FROM AUTHOR]

In order to make the assistant decision of airport pavement management more accurate and meet the requirements of intelligence，the existing network-level decision making algorithms need to be improved. Based on the concept of information entropy，we present a decision tree model of civil airport cement pavement maintenance plan. The model test and evaluation results show that the model has high accuracy， cross-validation and self-correcting ability. We take Shanghai Pudong International Airport pavement management system as an example. Compared with traditional models，the decision tree model is proved to encompass multi-factors， like pavement performance， structural performance and subjective factors of management needs. The results demosnstrate that the proposed model is more fit the engineering in practice. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The large area and fast development of aeolian sandy lands in the Yarlung Zangbo River basin of Tibet, China has caused immense damages to local social and economic development, and thus, it is necessary to carry out a series of ecological engineering to recover vegetation on moving sand dunes. However, revegetation potentiality of moving sandy land has rarely been reported with integrated studies, and under the harsh conditions of habitat stress, revegetation potentiality of different types of moving sandy lands is still unclear, which is one of most important technical bottlenecks and critical basis for policy making as well as for ecological restoration. In this paper, three types of moving sandy lands (on flood plain, on river bank, on mountain slope, respectively) in alpine valley in the middle reaches of the Yarlung Zangbo River basin were first chosen as the study objects, and then evaluation indicators and criterion systems of revegetation potentiality were established on the ground of the division of vegetation succession stages and the analysis of their habitat characteristics on different moving sandy lands. Thereafter, a comprehensive evaluation model for revegetation potentiality (CEMRP) on moving sandy land in the alpine valley was built using the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method. The results showed that the potentiality of moving sandy land on mountain slope was the strongest at Level I and with the biggest membership degree value (0.2884), versus little strong to moving sandy land on flood plain at Level II and with the biggest membership degree value (0.2869), little poor to moving sandy land on river bank at Level IV and with the biggest membership degree value (0.3296). The congenital conditions which affected the habitat of vegetation growth, such as climate (precipitation, temperature, sunshine duration), topographic (elevation, slope, aspect) and soil conditions (moisture, pH value, organic matter, total nitrogen, texture, temperature of sand dune), may determine what the status of vegetation recovery would be. However, vegetation recovery measures which could be treated as acquired conditions, could be used to adjust revegetation potentiality. In addition, by adopting suitable countermeasures (suitable species, fencing, sand-protecting barrier) for artificial revegetation on different types of moving sandy lands, vegetation potentiality could be promoted directly, while the status of vegetation recovery also could be improved through altering soil conditions indirectly. There were three experimental plots established in 2008 for the observation of vegetation recovery. We integrated the vegetation recovery status of different types of moving sandy lands in 2014, and the accuracy and applicability of CEMRP was validated and discussed. The result indicated that CEMRP fitted very well with vegetation growth on different types of moving sandy lands, and could be used as a decision support system for policy makers to identify the difficulties and the priority of ecological rehabilitation for different types of moving sandy lands, even for different regions under different climatic zones in the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Departure sequencing is an effective means to expedite terminal air traffic flow, improve departure punctuality and alleviate controllers' workload. The framework for dynamic sequencing is built based on receding horizon control （RHC） strategy. And through the branch and bound algorithm, tar- get takeoff time （TTOT） is optimized within the sequencing window. Furthermore, target start-up approval time （TSAT） is obtained depending on the amendment of taxi out time so as to provide the decision support for tower controller. Taking ZSPD airport as an example for simulation, the result indicates that the proposed algorithm can realize decision support for controllers （providing TTOT TSAT）, enhance the runway departure capacity, and reduce the departure delay. [ABSTRACT FROM AUTHOR]

The social-economic-natural system is a complex system for human survival and development, and the data-driven system research provides a new value-added orientation to enhance the cognition of the ecosystem. Under the new data context, the social-economic-natural complex system shows new features. The research object is gradually changing from a single element to a multi-factor coupling direction, which makes the data system more diversified, data sources more extensive, data expression more visualized. The research scale shows the characteristics of gradually expanding, and the research object would be more detailed. In the process of data identification, expression and visualization, it is therefore necessary to strengthen the coupling of time, space, structure, quantity and order, as well as to focus on the integration with decision making and local services. The future research of complex ecosystems in the new era should be carried out in terms of key scientific issues and supporting technologies, the role of scale and multi-factor coupling, as well as scientific and technological support for local and global governance. Under the continuous innovation of data, strengthening the cognition of multi-source data, long-term monitoring and time series still needs to be studied in depth. Carrying out data-driven analysis of complex ecosystems not only provides technical support for ecosystem services and sustainable development and enhances the long-term data sharing mechanism, but also provides more value support for realizing decision making and information dissemination.

Important sporadic events could lead to social chain response and serious consequences, which may endanger social stability. When an sporadic event occurs, the emergency preparedness should be generated in time to launch action, what will reduce losses in great extent. This paper researched on applying case expression and similarity measure methods to the emergency decision support based on Case-Based Reasoning, and proposed a new case retrieving method based on semantic distance synergetic mode. Three approaches are attempted to build the appropriate similarity measure: firstly, case features are extracted under semantic distance; then, case concepts are descripted under Multi-Case Multi-Label by various words with the same meaning; finally, similarity measure by Wordnet, which would reason the most same semantic word. The experiments showed that the proposed method could significantly improve the case retrieving efficiency, which could provide effective and timely decision-making support for the future treatment of important sporadic events. [ABSTRACT FROM AUTHOR]

To solve the suppliers' support problem of online reverse combinatorial multi-attribute auction based on bundling, a bundling confirmation model and suppliers' bidding strategy are proposed based on the scientometrics bundling algorithm and the analysis of suppliers' bidding behavior in order to maximize the revenue of the suppliers. After that, an illustration for this model is presented, and the result shows that this model can significantly improve the suppliers' revenue and reduce the negative effect from the asymmetric in formation during the auction. [ABSTRACT FROM AUTHOR]

Conducted research on the application of earned value method in the management process of type engineering projects. The method was improved to overcome its disadvantage in the analysis on project quality effect factors, adding three quality-cost parameters based on cost limited control method. The three parameters are quality budgeted cost for work scheduled (CQBS), quality actual cost for work performed (CQAP), and quality budgeted cost for work performed (CQBP). By using the three additional parameters, four project quality estimation indexes were further developed, including quality cost variance (VQC), quality schedule variance (VQS), quality cost performed index (IQCP) and quality schedule cost performed index (ISCP). The improved method can monitor project quality, cost and schedule comprehensively. Its efficiency in the application of integrated project monitoring was illustrated with an example of engineering project management process. [ABSTRACT FROM AUTHOR]