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      Exploring Evaluation of Enterprise Economic Benefits Using Big Data

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      1 , 2 ,
      Journal of Environmental and Public Health
      Hindawi

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          Abstract

          The purpose is to improve Chinese enterprises' economic benefit evaluation system based on big data and promote sustainable enterprise production. This paper studies the power supply enterprises-oriented Evaluation Index System (EIS) under the big data environment. Firstly, it expounds on the construction theory of the enterprise economic benefit model. Secondly, the comprehensive Grey Model (GM) based on improved weight and the power consumption prediction model based on Least Mean Square (LMS) neural network (NN) algorithm are introduced. Finally, the comprehensive GM model based on improved weight is used to evaluate the economic benefits of power supply enterprises. The power consumption prediction model based on the LMS-NN algorithm is used to predict the sustainable development of power supply enterprises. The results show that the profitability and solvency of joint-stock power companies are about 90 and 100, respectively, and the social contribution of state-owned power supply enterprises is the strongest. Lastly, it is predicted that the region will have 134.8 billion kWh of electricity and about 137.2 billion kWh of power consumption in 2020. The growth model and trend are consistent, but there are some errors in the specific power consumption data. Therefore, the audit method based on big data has a good evaluation effect on the economic benefits of enterprises. For example, the profits of private and joint-stock power supply enterprises are relatively high. In contrast, state-owned power supply enterprises have outstanding social contribution ability. The big data method is used to predict the power consumption in some areas, and the predicted value is consistent with the actual value. This study provides a reference for the follow-up economic benefit evaluation and sustainable development of enterprises.

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          Most cited references22

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          State-of-the-art in artificial neural network applications: A survey

          This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.
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            Socioeconomic inequalities in childhood and adolescent body-mass index, weight, and height from 1953 to 2015: an analysis of four longitudinal, observational, British birth cohort studies

            Summary Background Socioeconomic inequalities in childhood body-mass index (BMI) have been documented in high-income countries; however, uncertainty exists with regard to how they have changed over time, how inequalities in the composite parts (ie, weight and height) of BMI have changed, and whether inequalities differ in magnitude across the outcome distribution. Therefore, we aimed to investigate how socioeconomic inequalities in childhood and adolescent weight, height, and BMI have changed over time in Britain. Methods We used data from four British longitudinal, observational, birth cohort studies: the 1946 Medical Research Council National Survey of Health and Development (1946 NSHD), 1958 National Child Development Study (1958 NCDS), 1970 British Cohort Study (1970 BCS), and 2001 Millennium Cohort Study (2001 MCS). BMI (kg/m2) was derived in each study from measured weight and height. Childhood socioeconomic position was indicated by the father's occupational social class, measured at the ages of 10–11 years. We examined associations between childhood socioeconomic position and anthropometric outcomes at age 7 years, 11 years, and 15 years to assess socioeconomic inequalities in each cohort using gender-adjusted linear regression models. We also used multilevel models to examine whether these inequalities widened or narrowed from childhood to adolescence, and quantile regression was used to examine whether the magnitude of inequalities differed across the outcome distribution. Findings In England, Scotland, and Wales, 5362 singleton births were enrolled in 1946, 17 202 in 1958, 17 290 in 1970, and 16 404 in 2001. Low socioeconomic position was associated with lower weight at childhood and adolescent in the earlier-born cohorts (1946–70), but with higher weight in the 2001 MCS cohort. Weight disparities became larger from childhood to adolescence in the 2001 MCS but not the earlier-born cohorts (pinteraction=0·001). Low socioeconomic position was also associated with shorter height in all cohorts, yet the absolute magnitude of this difference narrowed across generations. These disparities widened with age in the 2001 MCS (pinteraction=0·002) but not in the earlier-born cohorts. There was little inequality in childhood BMI in the 1946–70 cohorts, whereas inequalities were present in the 2001 cohort and widened from childhood to adolescence in the 1958–2001 cohorts (pinteraction<0·05 in the later three cohorts but not the 1946 NSHD). BMI and weight disparities were larger in the 2001 cohort than in the earlier-born cohorts, and systematically larger at higher quantiles—eg, in the 2001 MCS at age 11 years, a difference of 0·98 kg/m2 (95% CI 0·63–1·33) in the 50th BMI percentile and 2·54 kg/m2 (1·85–3·22) difference at the 90th BMI percentile were observed. Interpretation Over the studied period (1953–2015), socioeconomic-associated inequalities in weight reversed and those in height narrowed, whereas differences in BMI and obesity emerged and widened. These substantial changes highlight the impact of societal changes on child and adolescent growth and the insufficiency of previous policies in preventing obesity and its socioeconomic inequality. As such, new and effective policies are required to reduce BMI inequalities in childhood and adolescence. Funding UK Economic and Social Research Council, Medical Research Council, and Academy of Medical Sciences/the Wellcome Trust.
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              Big Data technologies: A survey

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                Author and article information

                Contributors
                Journal
                J Environ Public Health
                J Environ Public Health
                jeph
                Journal of Environmental and Public Health
                Hindawi
                1687-9805
                1687-9813
                2022
                28 June 2022
                : 2022
                : 1103561
                Affiliations
                1Green Innovation Industrial Institute, School of Economics and Management, Chengdu Technological University, Chengdu 610000, China
                2School of Business College, Changchun Guanghua University, Changchun 130000, China
                Author notes

                Academic Editor: Fu-Sheng Tsai

                Author information
                https://orcid.org/0000-0001-8320-7477
                Article
                10.1155/2022/1103561
                9256405
                d15e4edc-9adc-4908-8754-b00a57dcca85
                Copyright © 2022 Xiaoqian Liu and Yujia Li.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 March 2022
                : 17 May 2022
                Funding
                Funded by: Chengdu Technological University
                Award ID: 2019RC025
                Funded by: Construction of Green Industrial System and High Quality Growth of County Economy in Sichuan Province
                Award ID: QM2021023
                Funded by: Research on the Development Path of Science and Technology Finance in Jilin Province
                Award ID: JJKH20221285SK
                Categories
                Research Article

                Public health
                Public health

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