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Copyright of Chinese Journal of Stroke is the property of Chinese Journal of Stroke 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 Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency 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 World Nuclear Geoscience is the property of World Nuclear Geoscience 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 Zhejiang Electric Power is the property of Zhejiang Electric Power 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 Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) 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 Architectural Journal / Jian Zhu Xue Bao is the property of Architectural 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 Journal of Computer Engineering & Applications 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 Journal of China University of Petroleum is the property of China University of Petroleum 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.)

This paper made a comprehensive literature review on the topic of applying machine learning methods to shield tunnelling parameter predictions, stratum predictions ahead of a tunnel face, surface settlements prediction, shield attitude deviation and tool wear predictions. The selections of machine learning methods and the associated input and output parameters were analyzed, and the shortcomings and challenges of existing research were summarized. Some prospects were given, including the model generalization, multi-source heterogeneous data compression and assimilation, data-physics-based intelligent shield tunnelling, big data in shield tunnelling, in order to provide reference and guidance for the future research and engineering practice. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of things and cloud computing,big data and its related science have become the focus of industry and academia.In this paper,we analyzes big data science from the perspective of paradigm theory and expounds the difference and connection between big data and traditional research.Three major challenges brought by big data are proposed in perspective of machine learning,with the corresponding scientific problems following.In addition,this paper introduces several insights of big data science from the perspective of the forth paradigm and its positive significance.In the end,we summarize and look forward to the challenges of big data science in the future. [ABSTRACT FROM AUTHOR]

临床预测模型可基于从群体获得的效应证据去预测个体的结局或风险，是链接循证医学和精准医学的强有力工具。随着数据测量、存储、互联互通及分析技术的不断进步，临床预测模型的应用前景将越来越明朗，但与此同时，也面临着诸多问题和挑战。本文对临床预测模型的基础概念、应用场景以及现在面临的困境和发展机遇做一个梳理。 Abstract: Clinical prediction models can predict individual outcomes or risks based on effect evidence obtained from the population, serving as a powerful tool linking evidence-based medicine and precision medicine. With the continuous advancement of data measurement, storage, interoperability, and analytical techniques, the application prospect for clinical prediction models is becoming increasingly clear. However, it also faces many problems and challenges. This paper aims to provide an overview of the fundamental concepts, application scenarios, current dilemmas, and development opportunities of clinical prediction models.

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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 spaceborne synthetic aperture radar(SAR) plays an important role in ocean observation owing to its capability of working all-day and being insusceptible to sunlight,cloudiness or rainfall.It has unique advantages in retrieval of ocean surface dynamic parameters and study of multi-scale ocean dynamic processes with high spatial resolution,multi-polarization,and multi-imaging modes.Since the late 1970 s,spaceborne SAR technology has developed rapidly.When combined with big data and machine learning techniques,spaceborne SAR exhibits more powerful vitality in ocean observation.In this paper,the 5‘V’ characteristics of spaceborne SAR big data are elaborated.Then two typical cases,i.e.retrieval of the sea surface wind speed,and scientific recognition of mesoscale dynamic processes of ocean internal waves,are presented to demonstrate the integration of spaceborne SAR,machine learning,and big data in assistance of high-resolution inversion of ocean environmental factors and deep understanding of marine dynamic processes.Finally,the prospective of spaceborne SAR big data for ocean remote sensing is given. [ABSTRACT FROM AUTHOR]

Aiming at the challenges faced by the existing big data-oriented computing framework in the study of extensible machine learning, this paper proposed a distributed naive Bayesian text classification method based on MapReduce and Apache Spark framework. This method explored the Bayesian network classifier by studying the adaptability of MapReduce and Apache Spark frameworks, and studied the existing computing framework for big data. First, it divided the training sample data set into m classes based on the naive Bayes text classification model. In the training phase, it used the output of the previous MapReduce as the input of the next MapReduce, and used four MapReduce jobs to derive the model. This design process made full use of the parallel advantages of MapReduce. Finally, when the classifier was tested, it obtained the value of the class label which the maximum value belonged. Experiments in the Newgroup's dataset show the proposed method achieves more than 99% of the results on all five types of news data sets, and is all higher than the comparison algorithms, which prove the accuracy of the method. [ABSTRACT FROM AUTHOR]

In our previous work, a big data active learning algorithm based on MapReduce was proposed. In this paper, we transplant this algorithm into the Spark environment and propose a Spark based big data active learning algorithm. Furthermore, the two algorithms are experimentally compared on four aspects: running time, number of files, number of synchronizations, and memory cost. Some valuable conclusions are obtained, which can be very helpful to researcher s in the related fields. [ABSTRACT FROM AUTHOR]

With the development of high performance computer and data sharing platform, a large number of high fidelity turbulence data can be obtained. Recently, due to the evolution of artificial intelligence, like deep neural network, data-driven machine learning methods have been adopted to quantify the model uncertainty and improve and construct turbulence models. The combination of big turbulence data and artificial intelligence becomes a new area of turbulence research. Although some encouraging results have been achieved, there are still many difficulties and challenges, such as the generalization ability and robustness of the models, etc. The modeling process involves various aspects including data process, feature selection and selection and optimization of the model framework, etc. This paper analyzes and summarizes the main research progress from two aspects:the implementation methods of machine learning in turbulence modeling and the different model targets. Besides, the challenges and future works in this area are also discussed. [ABSTRACT FROM AUTHOR]