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      Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms

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      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          The aims are to improve the efficiency in analyzing the regional economic changes in China’s high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, and talents in regional economic structural changes. According to previous works, the economic efficiency and impact mechanism of China’s high-tech IDZ are analyzed profoundly. The machine learning (ML)-based Data Envelopment Analysis (DEA) and Malmquist index measurement algorithms are adopted to analyze the dynamic and static characteristics of high-tech IDZ’s economic data from 2009 to 2019. Furthermore, a high-tech IDZ economic efficiency influencing factor model is built. Based on the detailed data of a high-tech IDZ, the regional economic changes are analyzed from the following dimensions: economic environment, economic structure, number of talents, capital investment, and high-tech IDZ’s regional scale, which verifies the effectiveness of the proposed model further. Results demonstrate that the comprehensive economic efficiency of all national high-tech IDZs in China is relatively high. However, there are huge differences among different regions. The economic efficiency of the eastern region is significantly lower than the national average. The economic structure, number of talents, capital investment, and economic efficiency of the high-tech IDZs show a significant positive correlation. The economic changes in high-tech IDZs can be improved through the secondary industry, employee value, and funding input. The ML technology applied can make data processing more efficient, providing proper suggestions for developing China’s high-tech industrial parks.

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          How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China

          Haitao Wu, Yu Hao, Siyu Ren (2020)
          Article 0 comments Cited 232 times – based on 0 reviews     Review now
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            The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6

            Tongwen Wu, Yixiong Lu, Yongjie Fang … (2019)
            Abstract. The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameterizations and model performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric quasi-biennial oscillation (QBO), the Madden–Julian Oscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2-MR shows significant improvements in many aspects including the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle of precipitation, interannual variations of SST in the equatorial Pacific, and the long-term trend of surface air temperature.
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              Development of a targeted client communication intervention to women using an electronic maternal and child health registry: a qualitative study

              Binyam Bogale, Kjersti Mørkrid, Brian O’Donnell … (2020)
              Background Targeted client communication (TCC) using text messages can inform, motivate and remind pregnant and postpartum women of timely utilization of care. The mixed results of the effectiveness of TCC interventions points to the importance of theory based interventions that are co-design with users. The aim of this paper is to describe the planning, development, and evaluation of a theory led TCC intervention, tailored to pregnant and postpartum women and automated from the Palestinian electronic maternal and child health registry. Methods We used the Health Belief Model to develop interview guides to explore women’s perceptions of antenatal care (ANC), with a focus on high-risk pregnancy conditions (anemia, hypertensive disorders in pregnancy, gestational diabetes mellitus, and fetal growth restriction), and untimely ANC attendance, issues predefined by a national expert panel as being of high interest. We performed 18 in-depth interviews with women, and eight with healthcare providers in public primary healthcare clinics in the West Bank and Gaza. Grounding on the results of the in-depth interviews, we used concepts from the Model of Actionable Feedback, social nudging and Enhanced Active Choice to compose the TCC content to be sent as text messages. We assessed the acceptability and understandability of the draft text messages through unstructured interviews with local health promotion experts, healthcare providers, and pregnant women. Results We found low awareness of the importance of timely attendance to ANC, and the benefits of ANC for pregnancy outcomes. We identified knowledge gaps and beliefs in the domains of low awareness of susceptibility to, and severity of, anemia, hypertension, and diabetes complications in pregnancy. To increase the utilization of ANC and bridge the identified gaps, we iteratively composed actionable text messages with users, using recommended message framing models. We developed algorithms to trigger tailored text messages with higher intensity for women with a higher risk profile documented in the electronic health registry. Conclusions We developed an optimized TCC intervention underpinned by behavior change theory and concepts, and co-designed with users following an iterative process. The electronic maternal and child health registry can serve as a unique platform for TCC interventions using text messages.
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                Author and article information

                Contributors
                ErLe Du:
                ORCID: https://orcid.org/0000-0002-2260-5890
                Role: Funding acquisitionRole: MethodologyRole: Project administrationRole: Resources
                Meng Ji: Role: Data curationRole: SoftwareRole: Writing – original draft
                Zhihan Lv: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 22 June 2021
                Publication date Collection: 2021
                Volume: 16
                Issue: 6
                Electronic Location Identifier: e0250802
                Affiliations
                [001]Department of Strategic Development, Harbin Bank, Harbin, China
                Qingdao University, CHINA
                Author notes

                Competing Interests: We declare this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc. This commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                https://orcid.org/0000-0002-2260-5890
                Article
                Publisher ID: PONE-D-20-37182
                DOI: 10.1371/journal.pone.0250802
                PMC ID: 8219165
                PubMed ID: 34157015
                SO-VID: b6ee8601-3891-41a1-9305-2e549339b1b1
                Copyright © © 2021 Du, Ji
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 25 November 2020
                Date accepted : 26 March 2021
                Page count
                Figures: 11, Tables: 2, Pages: 18
                Funding
                Funded by: the Special Fund for Postdoctoral of HeiLongJiang Province, China.
                Award ID: LBH-Z20027
                Award Recipient :
                ORCID: https://orcid.org/0000-0002-2260-5890
                This work was supported in part by the Special Fund for Postdoctoral of HeiLongJiang Province, China.LBH-Z20027. The Harbin Bank provided support in the form of salaries for authors ErLe Du and Meng Ji, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
                Categories
                Subject: Research Article
                Subject: Social Sciences
                Subject: Economics
                Subject: Social Sciences
                Subject: Economics
                Subject: Development Economics
                Subject: Economic Development
                Subject: Social Sciences
                Subject: Economics
                Subject: Economic Analysis
                Subject: People and Places
                Subject: Geographical Locations
                Subject: Asia
                Subject: China
                Subject: Social Sciences
                Subject: Economics
                Subject: Labor Economics
                Subject: Social Sciences
                Subject: Economics
                Subject: Industrial Organization
                Subject: Economics of Technical Change
                Subject: Social Sciences
                Subject: Economics
                Subject: Economic Analysis
                Subject: Economic Impact Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Machine Learning Algorithms
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Algorithms
                Subject: Machine Learning Algorithms
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Machine Learning Algorithms
                Custom metadata
                Data Availability All relevant data are within the paper and its Supporting Information files.

                ScienceOpen disciplines: Uncategorized
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