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Big Geodata Reveals Spatial Patterns of Built Environment Stocks Across and Within Cities in China

Zhou Huang,Yi Bao,Ruichang Mao,Han Wang,Ganmin Yin,Lin Wan,Houji Qi,Qiaoxuan Li,Hongzhao Tang,Qiance Liu,Linna Li,Bailang Yu,Qinghua Guo,Yu Liu,Huadong Guo,Gang Liu,

《工程（英文）》 doi: 10.1016/j.eng.2023.05.015

摘要： The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important, yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources, waste, and climate strategies. However, our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited, largely owing to the lack of sufficient high spatial resolution data. This study leveraged multi-source big geodata, machine learning, and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels. The per capita built environment stock of many cities (240 tonnes per capita on average) is close to that in western cities, despite considerable disparities across cities owing to their varying socioeconomic, geomorphology, and urban form characteristics. This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades. China’s urban expansion tends to be more “vertical” (with high-rise buildings) than “horizontal” (with expanded road networks). It trades skylines for space, and reflects a concentration–dispersion–concentration pathway for spatialized built environment stocks development within cities in China. These results shed light on future urbanization in developing cities, inform spatial planning, and support circular and low-carbon transitions in cities.

关键词： urban system engineering built environment stock spatial pattern urban sustainability big geodata

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Special issue: Innovative applications of big data and artificial intelligence

《工程管理前沿（英文）》 2022年第9卷第4期页码 517-519 doi: 10.1007/s42524-022-0234-0

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Clinical research of traditional Chinese medicine in big data era

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《医学前沿（英文）》 2014年第8卷第3期页码 321-327 doi: 10.1007/s11684-014-0370-y

摘要：

With the advent of big data era, our thinking, technology and methodology are being transformed. Data-intensive scientific discovery based on big data, named “The Fourth Paradigm,” has become a new paradigm of scientific research. Along with the development and application of the Internet information technology in the field of healthcare, individual health records, clinical data of diagnosis and treatment, and genomic data have been accumulated dramatically, which generates big data in medical field for clinical research and assessment. With the support of big data, the defects and weakness may be overcome in the methodology of the conventional clinical evaluation based on sampling. Our research target shifts from the “causality inference” to “correlativity analysis.” This not only facilitates the evaluation of individualized treatment, disease prediction, prevention and prognosis, but also is suitable for the practice of preventive healthcare and symptom pattern differentiation for treatment in terms of traditional Chinese medicine (TCM), and for the post-marketing evaluation of Chinese patent medicines. To conduct clinical studies involved in big data in TCM domain, top level design is needed and should be performed orderly. The fundamental construction and innovation studies should be strengthened in the sections of data platform creation, data analysis technology and big-data professionals fostering and training.

关键词： big data traditional Chinese medicine clinical evaluation evidence based medicine

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A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

《工程管理前沿（英文）》 2020年第7卷第1期页码 27-46 doi: 10.1007/s42524-020-0092-6

摘要： The era of big data brings unprecedented opportunities and challenges to management research. As one of the important functions of management decision-making, evaluation has been given more functions and application space. Exploring the applicable evaluation methods in the big data environment has become an important subject of research. The purpose of this paper is to provide an overview and discussion of systematic evaluation and improvement in the big data environment. We first review the evaluation methods based on the main analytic techniques of big data such as data mining, statistical methods, optimization and simulation, and deep learning. Focused on the characteristics of big data (association feature, data loss, data noise, and visualization), the relevant evaluation methods are given. Furthermore, we explore the systematic improvement studies and application fields. Finally, we analyze the new application areas of evaluation methods and give the future directions of evaluation method research in a big data environment from six aspects. We hope our research could provide meaningful insights for subsequent research.

关键词： big data evaluation methods systematic improvement big data analytic techniques data mining

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A study on specialist or special disease clinics based on big data

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《医学前沿（英文）》 2014年第8卷第3期页码 376-381 doi: 10.1007/s11684-014-0356-9

摘要：

Correlation analysis and processing of massive medical information can be implemented through big data technology to find the relevance of different factors in the life cycle of a disease and to provide the basis for scientific research and clinical practice. This paper explores the concept of constructing a big medical data platform and introduces the clinical model construction. Medical data can be collected and consolidated by distributed computing technology. Through analysis technology, such as artificial neural network and grey model, a medical model can be built. Big data analysis, such as Hadoop, can be used to construct early prediction and intervention models as well as clinical decision-making model for specialist and special disease clinics. It establishes a new model for common clinical research for specialist and special disease clinics.

关键词： big data correlation analysis medical information integration data analysis clinical model

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IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

《工程管理前沿（英文）》 2017年第4卷第4期页码 428-436 doi: 10.15302/J-FEM-2017048

摘要： In the EU Horizon 2020 Shift2Rail Multi-Annual Action Plan, the challenge of railway maintenance is generating knowledge from data and/or information. Therefore, we promote a novel concept called “IN2CLOUD,” which comprises three sub-concepts, to address this challenge: 1) A hybrid cloud, 2) an intelligent cloud with hybrid cloud learning, and 3) collaborative management using asset-related data acquired from the intelligent hybrid cloud. The concept is developed under the assumption that organizations want/need to learn from each other (including domain knowledge and experience) but do not want to share their raw data or information. IN2CLOUD will help the movement of railway industry systems from “local” to “global” optimization in a collaborative way. The development of cutting-edge intelligent hybrid cloud-based solutions, including information technology (IT) solutions and related methodologies, will enhance business security, economic sustainability, and decision support in the field of intelligent asset management of railway assets.

关键词： railway intelligent asset management collaborative learning big data hybrid cloud Bayesian

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Challenges to Engineering Management in the Big Data Era

Yong Shi

《工程管理前沿（英文）》 2015年第2卷第3期页码 293-303 doi: 10.15302/J-FEM-2015042

摘要： This paper presents a review of the challenges to engineering management in the Big Data Era as well as the Big Data applications. First, it outlines the definitions of big data, data science and intelligent knowledge and the history of big data. Second, the paper reviews the academic activities about big data in China. Then, it elaborates a number of challenging big data problems, including transforming semi-structured and non-structured data into “structured format” and explores the relationship of data heterogeneity, knowledge heterogeneity and decision heterogeneity. Furthermore, the paper reports various real-life applications of big data, such as financial and petroleum engineering and internet business.

关键词： big data data science intelligent knowledge engineering management real-life applications

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Appreciating the role of big data in the modernization of environmental governance

《工程管理前沿（英文）》 2022年第9卷第1期页码 163-169 doi: 10.1007/s42524-021-0185-x

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Scientific computation of big data in real-world clinical research

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《医学前沿（英文）》 2014年第8卷第3期页码 310-315 doi: 10.1007/s11684-014-0358-7

摘要：

The advent of the big data era creates both opportunities and challenges for traditional Chinese medicine (TCM). This study describes the origin, concept, connotation, and value of studies regarding the scientific computation of TCM. It also discusses the integration of science, technology, and medicine under the guidance of the paradigm of real-world, clinical scientific research. TCM clinical diagnosis, treatment, and knowledge were traditionally limited to literature and sensation levels; however, primary methods are used to convert them into statistics, such as the methods of feature subset optimizing, multi-label learning, and complex networks based on complexity, intelligence, data, and computing sciences. Furthermore, these methods are applied in the modeling and analysis of the various complex relationships in individualized clinical diagnosis and treatment, as well as in decision-making related to such diagnosis and treatment. Thus, these methods strongly support the real-world clinical research paradigm of TCM.

关键词： big data real world clinical research Chinese medicine medical computing

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Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

《结构与土木工程前沿（英文）》 2022年第16卷第7期页码 882-895 doi: 10.1007/s11709-022-0859-4

摘要： The Limit Equilibrium Method (LEM) is commonly used in traditional slope stability analyses, but it is time-consuming and complicated. Due to its complexity and nonlinearity involved in the evaluation process, it cannot provide a quick stability estimation when facing a large number of slopes. In this case, the convolutional neural network (CNN) provides a better alternative. A CNN model can process data quickly and complete a large amount of data analysis in a specific situation, while it needs a large number of training samples. It is difficult to get enough slope data samples in practical engineering. This study proposes a slope database generation method based on the LEM. Samples were amplified from 40 typical slopes, and a sample database consisting of 20000 slope samples was established. The sample database for slopes covered a wide range of slope geometries and soil layers’ physical and mechanical properties. The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model. The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers, and it can quickly obtain the safety factor and stability state of the slopes. Moreover, the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation. Therefore, the CNN can predict the safety factor of real slopes. In particular, the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN, which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

关键词： slope stability limit equilibrium method convolutional neural network database for slopes big data

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Study on Big Data-based Behavior Modification in Metro Construction

Lie-yun Ding,Sheng-yu Guo

《工程管理前沿（英文）》 2015年第2卷第2期页码 131-136 doi: 10.15302/J-FEM-2015037

摘要： With the rapid development of metro construction in China, construction accidents frequently happen, which are significantly attributable to workers’ unsafe behavior. Behavior-based safety (BBS) is an effective method to modify workers’ unsafe behavior. This paper introduces the study on big data-based metro construction behavior modification, aiming to solve the problem of current research without consideration of workers’ personal characters. First, the behavior modification pushing mechanism based on content-based personalized recommendation is studied. Secondly, the development of behavior modification system of metro construction (BMSMC) is introduced. Thirdly, BMSMC practical applications using the unsafe behavior rate, as a measuring indicator is implemented. Observations at one metro construction site in Wuhan indicate that the unsafe behavior rate of modified scaffolders at this work place decreased by 69.3%. At the same time, as of unmodified scaffolders at another work place for comparison, the unsafe behavior rate decreased by 56.9%, which validates the effectiveness of this system.

关键词： big data unsafe behavior behavior modification behavior-based safety (BBS) unsafe behavior rate

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Utilizing big data to build personalized technology and system of diagnosis and treatment in traditional

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《医学前沿（英文）》 2014年第8卷第3期页码 272-278 doi: 10.1007/s11684-014-0364-9

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prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

《结构与土木工程前沿（英文）》 2022年第16卷第4期页码 401-413 doi: 10.1007/s11709-022-0823-3

摘要： Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

关键词： hard rock tunnel tunnel bore machine advance rate prediction temporal convolutional networks soft computing construction big data

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A case of big substernal goiter resection

SONG Jiangping, LIAO Hongying, YU Chao, ZHANG Jian, Li Yun, GU Lijia

《医学前沿（英文）》 2008年第2卷第4期页码 410-413 doi: 10.1007/s11684-008-0079-x

摘要： Substernal goiter is considered to be a diagnostic differential for all anterosuperior mediastinal masses. For a substernal goiter, surgical removal is suggested when it is large in size, with a possibility for malignancy or has local compression of adjacent structures. This can be performed through the neck or by the addition of a partial or complete sternotomy if necessary. A 58-year-old Chinese man from Guangdong Province had had dry cough for a year. A subsequent CT scan in our hospital suggested “ectopic intrathoracic thyroid”. Fine needle percutaneous mass biopsy also suggested “ectopic intrathoracic thyroid”. We performed a standard median sternotomy to remove the mass. It measured 13 cm × 8 cm × 6 cm in size, and weighed 2.8 kg. The pathological diagnosis confirmed a benign thyroid adenoma. The patient had no hoarseness, dyspnea or hypocalcemia and was quickly extubated in the operating room. Recently, we encountered a big substernal goiter and had performed successful resection, which is reported here for reference.

关键词： percutaneous diagnostic differential necessary compression intrathoracic

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Robotized machining of big work pieces: Localization of supporting heads

Wojciech SZYNKIEWICZ, Teresa ZIELIŃSKA, Włodzimierz KASPRZAK

《机械工程前沿（英文）》 2010年第5卷第4期页码 357-369 doi: 10.1007/s11465-010-0103-0

摘要： A planner for a self adaptable and reconfigurable fixture system is proposed. The system is composed of mobile support agents that support thin sheet metal parts to minimize part dimensional deformation during drilling and milling operations. Compliant sheet metal parts are widely used in various manufacturing processes including automotive and aerospace industries. The main role of the planner is to generate an admissible plan of relocation of the mobile agents. It has to find the admissible locations for the supporting heads that provide continuous support in close proximity to the tool and trajectories of the mobile bases characterized by very high speeds during the relocation phases.

关键词： fixture robot milling drilling

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标题作者时间类型操作

Big Geodata Reveals Spatial Patterns of Built Environment Stocks Across and Within Cities in China

Zhou Huang,Yi Bao,Ruichang Mao,Han Wang,Ganmin Yin,Lin Wan,Houji Qi,Qiaoxuan Li,Hongzhao Tang,Qiance Liu,Linna Li,Bailang Yu,Qinghua Guo,Yu Liu,Huadong Guo,Gang Liu,

期刊论文

Special issue: Innovative applications of big data and artificial intelligence

期刊论文

Clinical research of traditional Chinese medicine in big data era

null

期刊论文

A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

期刊论文

A study on specialist or special disease clinics based on big data

null

期刊论文