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On the potential of iPhone significant location data to characterize individual mobility for air pollution

《环境科学与工程前沿（英文）》 2022年 第16卷 第5期 doi: 10.1007/s11783-022-1542-7

摘要：

● We evaluated the accuracy of iPhone data in capturing time-activity patterns.

关键词： Air pollution exposure     Human mobility     iPhone     Significant Location     Smartphone data    

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智能手机成像的晶片上基于逆转录环介导等温扩增(RT-LAMP)技术的全血中HIV-1检测 Article

Gregory L. Damhorst,Carlos Duarte-Guevara,Weili Chen,Tanmay Ghonge,Brian T. Cunningham,Rashid Bashir

《工程（英文）》 2015年 第1卷 第3期   页码 324-335 doi: 10.15302/J-ENG-2015072

摘要：

病毒载量测量对于人类免疫缺陷病毒 (HIV) 阳性患者长期临床护理来说是一个必不可少的工具。然而，考虑到病毒载量测量所需的仪器体积、成本和操作的复杂性，在医疗基础设施较差的偏远地区 (尤其是在被HIV感染人群比例较高的地区) 普及标准的病毒载量测量仪器是较为困难的。为提高该检测方法的普及性，人们已经开始开发可以进行即时检测的病毒载量检测平台，然而尚没有解决办法能够同时满足低成本、便携、易于操作等多种实际要求。本文通过运用微流体和微型硅晶片平台，对经过最低程度处理的含有HIV的全血样本进行了逆转录环介导等温扩增 (RT-LAMP)，并利用智能手机进行了荧光检测。集成实验检测结果表明，一滴约60 nL的反应液滴中仅有的3个病毒依然可以通过RT-LAMP技术被检测到，这相当于每微升全血样品中只有670个病毒。该技术在数字化RT-LAMP方法上具有重要意义，扩展该技术能够实现对HIV阳性患者在临床护理中采集指血进行病毒载量检测。研究结果显示，病毒载量检测过程所需的各个步骤，从血滴的准备到RT-LAMP反应的成像，都可以集成为晶片实验并且可以和移动设备兼容。

关键词： 人类免疫缺陷病毒(HIV)     病毒载量     环介导等温扩增     智能手机     即时检测    

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5G应用下的MIMO阵列天线去耦方法综述 Review Articles

Xiao-xi ZHANG, Ai-di REN, Ying LIU

《信息与电子工程前沿（英文）》 2020年 第21卷 第1期   页码 62-71 doi: 10.1631/FITEE.1900466

摘要： 提出一种面向微纳卫星的自主汽化管理液氨推进系统。相比常规冷气或液化气推进系统，提出多路平行筛孔式汽化装置和对应汽化控制方法。所提方案有效解决了液氨高汽化潜热和不易完全汽化的问题，从而发挥液氨推进剂高贮存密度和高比冲性能优势。此外，重点分析自主汽化管理液氨推进系统的工作流程及其涉及的物理化学过程和数学模型。综合考虑推力表现和能源效率，提出最优系统推力控制策略。地面测试表明，自主汽化管理液氨推进系统总重1.8 kg（包含0.34 kg液氨推进剂），比冲达到100 s，系统功耗在10 W以下。自主汽化管理液氨推进系统具有高比冲、低功耗、可实现性强、推力输出均一稳定等特点，适合微纳卫星在轨应用。

关键词： MIMO阵列；5G手机；去耦方法    

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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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Data quality assessment for studies investigating microplastics and nanoplastics in food products: Arecurrent data reliable?

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1694-0

摘要：

● Data quality assessment criteria for MP/NPs in food products were developed.

关键词： Microplastic     Nanoplastic     Food products     Data quality     Human health risk    

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Blockchain application in healthcare service mode based on Health Data Bank

Jianxia GONG, Lindu ZHAO

《工程管理前沿（英文）》 2020年 第7卷 第4期   页码 605-614 doi: 10.1007/s42524-020-0138-9

摘要： Blockchain is commonly considered a potential disruptive technology. Moreover, the healthcare industry has experienced rapid growth in the adoption of health information technology, such as electronic health records and electronic medical records. To guarantee data privacy and data security as well as to harness the value of health data, the concept of Health Data Bank (HDB) is proposed. In this study, HDB is defined as an integrated health data service institution, which bears no “ownership” of health data and operates health data under the principal–agent model. This study first comprehensively reviews the main characters of blockchain and identifies the blockchain-based healthcare industry projects and startups in the areas of health insurance, pharmacy, and medical treatment. Then, we analyze the fundamental principles of HDB and point out four challenges faced by HDB’s sustainable development: (1) privacy protection and interoperability of health data; (2) data rights; (3) health data supervision; (4) and willingness to share health data. We also analyze the important benefits of blockchain adoption in HDB. Furthermore, three application scenarios including distributed storage of health data, smart-contract-based healthcare service mode, and consensus-algorithm-based incentive policy are proposed to shed light on HDB-based healthcare service mode. In the end, this study offers insights into potential research directions and challenges.

关键词： Health Data Bank     blockchain     data assets     smart contract     incentive mechanism    

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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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Intelligent data analytics is here to change engineering management

Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG

《工程管理前沿（英文）》 2017年 第4卷 第1期   页码 41-48 doi: 10.15302/J-FEM-2017003

摘要： A great deal of scientific research in the world aims at discovering the facts about the world so that we understand it better and find solutions to problems. Data enabling technology plays an important role in modern scientific discovery and technologic advancement. The importance of good information was long recognized by prominent leaders such as Sun Tzu and Napoleon. Factual data enables managers to measure, to understand their businesses, and to directly translate that knowledge into improved decision making and performance. This position paper argues that data analytics is ready to change engineering management in the following areas: 1) by making relevant historical data available to the manager at the time when it’s needed; 2) by filtering out actionable intelligence from the ocean of data; and 3) by integrating useful data from multiple sources to support quantitative decision-making. Considering the unique need for engineering management, the paper proposes researchable topics in the two broad areas of data acquisition and data analytics. The purpose of the paper is to provoke discussion from peers and to encourage research activity.

关键词： engineering management     project management     big data     data analytics     planning     execution    

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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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Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

《信息与电子工程前沿（英文）》 2015年 第16卷 第12期   页码 1059-1068 doi: 10.1631/FITEE.1400398

摘要： One recent area of interest in computer science is data stream management and processing. By ‘data stream’, we refer to continuous and rapidly generated packages of data. Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classification of input data. A novel ensemble classifier is proposed in this paper. The classifier uses base classifiers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifiers based on their quality. Implementation of a weighting mechanism to the base classifiers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifiers with higher efficiency. Furthermore, the proposed method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble classifiers and single classifiers. In addition, in some cases the proposed classifier is faster and needs less storage space.

关键词： Data stream     Classificaion     Ensemble classifiers     Concept drift    

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Methodological considerations for redesigning sustainable cropping systems: the value of data-mininglarge and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN, Martin LECHENET

《农业科学与工程前沿（英文）》 2020年 第7卷 第1期   页码 21-27 doi: 10.15302/J-FASE-2019292

摘要：

Redesigning cropping and farming systems to enhance their sustainability is mainly addressed in scientific studies using experimental and modeling approaches. Large data sets collected from real farms allow for the development of innovative methods to produce generic knowledge. Data mining methods allow for the diversity of systems to be considered holistically and can take into account the diversity of production contexts to produce site-specific results. Based on the very few known studies using such methods to analyze the crop management strategies affecting pesticide use and their effect on farm performance, we advocate further investment in the development of large data sets that can support future research programs on farming system design.

关键词： data mining     holistic     Integrated Pest Management     economics     DEPHY network.    

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Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

《能源前沿（英文）》 doi: 10.1007/s11708-023-0912-6

摘要： With the promotion of “dual carbon” strategy, data center (DC) access to high-penetration renewable energy sources (RESs) has become a trend in the industry. However, the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids. In this paper, a multi-timescale optimal scheduling model is established for interconnected data centers (IDCs) based on model predictive control (MPC), including day-ahead optimization, intraday rolling optimization, and intraday real-time correction. The day-ahead optimization stage aims at the lowest operating cost, the rolling optimization stage aims at the lowest intraday economic cost, and the real-time correction aims at the lowest power fluctuation, eliminating the impact of prediction errors through coordinated multi-timescale optimization. The simulation results show that the economic loss is reduced by 19.6%, and the power fluctuation is decreased by 15.23%.

关键词： model predictive control     interconnected data center     multi-timescale     optimized scheduling     distributed power supply     landscape uncertainty    

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Big data and machine learning: A roadmap towards smart plants

《工程管理前沿（英文）》   页码 623-639 doi: 10.1007/s42524-022-0218-0

摘要： Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

关键词： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0     intelligent system     digitalization    

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Clinical research of traditional Chinese medicine in big data era

null

《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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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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标题 作者 时间 类型 操作

On the potential of iPhone significant location data to characterize individual mobility for air pollution

期刊论文

智能手机成像的晶片上基于逆转录环介导等温扩增(RT-LAMP)技术的全血中HIV-1检测

Gregory L. Damhorst,Carlos Duarte-Guevara,Weili Chen,Tanmay Ghonge,Brian T. Cunningham,Rashid Bashir

期刊论文

5G应用下的MIMO阵列天线去耦方法综述

Xiao-xi ZHANG, Ai-di REN, Ying LIU

期刊论文

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

Feng YANG, Manman WANG

期刊论文

Data quality assessment for studies investigating microplastics and nanoplastics in food products: Arecurrent data reliable?

期刊论文

Blockchain application in healthcare service mode based on Health Data Bank

Jianxia GONG, Lindu ZHAO

期刊论文

Challenges to Engineering Management in the Big Data Era

Yong Shi

期刊论文

Intelligent data analytics is here to change engineering management

Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG

期刊论文

Special issue: Innovative applications of big data and artificial intelligence

期刊论文

Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

期刊论文

Methodological considerations for redesigning sustainable cropping systems: the value of data-mininglarge and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN, Martin LECHENET

期刊论文

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

期刊论文

Big data and machine learning: A roadmap towards smart plants

期刊论文

Clinical research of traditional Chinese medicine in big data era

null

期刊论文

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

期刊论文