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SHIFTING TO A RECOMMENDED DIETARY PATTERN COULD PROMOTE SUSTAINABLE DEVELOPMENT OF THE ENVIRONMENT AND HUMAN HEALTH

《农业科学与工程前沿（英文）》 2023年第10卷第1期页码 73-82 doi: 10.15302/J-FASE-2023489

摘要：

● Shifting from the existing dietary patterns to the alternative recommended dietary pattern could enhance the sustainable development of environment and human health.

关键词： CHNS data cluster analysis dietary patterns sustainable development

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J-shaped association between dietary zinc intake and new-onset hypertension: a nationwide cohort study in China

《医学前沿（英文）》 2023年第17卷第1期页码 156-164 doi: 10.1007/s11684-022-0932-3

摘要： We aimed to investigate the relationship of dietary zinc intake with new-onset hypertension among Chinese adults. A total of 12,177 participants who were free of hypertension at baseline from the China Health and Nutrition Survey were included. Dietary intake was assessed by three consecutive 24-h dietary recalls combined with a household food inventory. Participants with systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg or diagnosed by a physician or under antihypertensive treatment during the follow-up were defined as having new-onset hypertension. During a median follow-up duration of 6.1 years, 4269 participants developed new-onset hypertension. Overall, the association between dietary zinc intake and new-onset hypertension followed a J-shape (P for non-linearity < 0.001). The risk of new-onset hypertension significantly decreased with the increment of dietary zinc intake (per mg/day: hazard ratio (HR) 0.93; 95% confidence interval (CI) 0.88–0.98) in participants with zinc intake < 10.9 mg/day, and increased with the increment of zinc intake (per mg/day: HR 1.14; 95% CI 1.11–1.16) in participants with zinc intake ≥ 10.9 mg/day. In conclusion, there was a J-shaped association between dietary zinc intake and new-onset hypertension in general Chinese adults, with an inflection point at about 10.9 mg/day.

关键词： dietary zinc intake new-onset hypertension general population CHNS

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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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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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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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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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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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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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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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Scientific computation of big data in real-world clinical research

null

《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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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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Effectiveness of state incentives for promoting wind energy: A panel data examination

Deepak SANGROYA,Jogendra NAYAK

《能源前沿（英文）》 2015年第9卷第3期页码 247-258 doi: 10.1007/s11708-015-0364-8

摘要： Over the last decade, India has started to concentrate earnestly on renewable energy. The Indian government, as well as different state governments, are adopting policy instruments such as feed in tariff, captive consumption, renewable purchase obligation and generation based incentive etc. aimed at renewable energy development. This paper evaluates the effectiveness of state level incentives for the development of wind energy in India. Fixed effect panel data modelling technique of econometric analysis is used to analyse the data of 26 Indian states in 11 years. The results show that feed in tariff and captive consumption are the significant predictors of wind energy development. However, renewable purchase obligation does not affect wind energy significantly.

关键词： India wind energy development state incentives econometric analysis panel data

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标题作者时间类型操作

SHIFTING TO A RECOMMENDED DIETARY PATTERN COULD PROMOTE SUSTAINABLE DEVELOPMENT OF THE ENVIRONMENT AND HUMAN HEALTH

期刊论文

J-shaped association between dietary zinc intake and new-onset hypertension: a nationwide cohort study in China

期刊论文

Blockchain application in healthcare service mode based on Health Data Bank

Jianxia GONG, Lindu ZHAO

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

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

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

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

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

期刊论文

Big data and machine learning: A roadmap towards smart plants

期刊论文

Scientific computation of big data in real-world clinical research