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  • Economics  (169)
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  • 1
    Online Resource
    Online Resource
    Dropout Rate Prediction of Massive Open Online Courses Based on Convolutional Neural Networks and Long Short-Term Memory Network
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-5-16), p. 1-11
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-5-16), p. 1-11
    Abstract: Massive open online courses (MOOC) is characterized by large scale, openness, autonomy, and personalization, attracting increasingly students to participate in learning and gaining recognition from more and more people. This paper proposes a network model based on convolutional neural networks and long short-term memory network (CNN-LSTM) for MOOC dropout prediction task. The model selects 43-dimensional behavioral features as input from students’ learning activity logs and adopts the CNN model to automatically extract continuous features over a period of time from students’ learning activity logs. At the same time, considering the time sequence of students’ learning behavior characteristics, a MOOC dropout prediction model was established by using long short-term memory network to obtain students’ learning status at different time steps. The algorithm proposed in this chapter was trained and evaluated on the public dataset provided by the KDD Cup 2015 competition. Compared with the dropout prediction methods based on LSTM and CNN-RNN, the model improved the AUC by 2.7% and 1.4%, respectively. The result in this paper is a good predictor of dropout rates and is expected to provide teaching aid to teachers.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2022/8255965
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Joint Estimation of Treatment and Placebo Effects in Clinical Trials With Longitudinal Blinding Assessments
    Informa UK Limited ; 2016
    In:  Journal of the American Statistical Association Vol. 111, No. 514 ( 2016-04-02), p. 538-548
    Details
    In: Journal of the American Statistical Association, Informa UK Limited, Vol. 111, No. 514 ( 2016-04-02), p. 538-548
    Type of Medium: Online Resource
    ISSN: 0162-1459 , 1537-274X
    URL: Article
    DOI: 10.1080/01621459.2015.1130633
    RVK:
    QA 10000
    RVK:
    SA 2455
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2016
    detail.hit.zdb_id: 2064981-2
    detail.hit.zdb_id: 207602-0
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    Online Resource
  • 3
    Online Resource
    Online Resource
    A public transport network design using a hidden Markov model and an optimization algorithm
    Elsevier BV ; 2022
    In:  Research in Transportation Economics Vol. 92 ( 2022-05), p. 101095-
    Details
    In: Research in Transportation Economics, Elsevier BV, Vol. 92 ( 2022-05), p. 101095-
    Type of Medium: Online Resource
    ISSN: 0739-8859
    URL: Article
    DOI: 10.1016/j.retrec.2021.101095
    RVK:
    QR 800
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 86498-5
    detail.hit.zdb_id: 2403320-0
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Attracting Gen Z workforce: the effects of organizational social media policy
    Emerald ; 2023
    In:  Tourism Review ( 2023-07-24)
    Details
    In: Tourism Review, Emerald, ( 2023-07-24)
    Abstract: Teniendo en cuenta la experiencia formativa única de la Generación Z con los medios sociales, este estudio aplica la teoría de la utilización de señales para investigar los efectos de la política de medios sociales como indicio organizativo para atraer a esta cohorte a las empresas de hostelería y turismo. Diseño/metodología/enfoque Se realizó un experimento de 2x2 (marco de la política: promoción vs prevención) x (límite de la política: distinto vs permeable) con 243 personas de la Generación Z en busca de empleo en hostelería y turismo. Se aplicaron análisis ANCOVA para analizar los efectos del marco de política y el límite en el atractivo percibido de la organización y la intención de búsqueda, respectivamente. Resultados Este estudio descubrió que cuando la política de una empresa promovía el uso de los medios sociales, los solicitantes de empleo de la Generación Z declaraban un mayor atractivo y una mayor intención de búsqueda en la condición de límite diferenciado. Sin embargo, cuando la política de medios sociales de una empresa hacía hincapié en un marco de prevención, los solicitantes de empleo de la Generación Z informaron de un nivel similar de atractivo percibido e intención de búsqueda, independientemente de las condiciones de límite de la política. Originalidad/valor Este estudio abordó las lagunas en la literatura debido a la investigación limitada sobre la fuerza laboral de la Generación Z y los efectos de las políticas de redes sociales en la atracción de talento. Basado en la teoría de utilización de señales, el estudio identificó las combinaciones de cláusulas de política que generaron impactos positivos en la contratación entre miembros de la Generación Z. El estudio proporcionó implicaciones teóricas y prácticas únicas para que los gerentes de hostelería y turismo utilicen las políticas de redes sociales como una herramienta novedosa y rentable para atraer talento de la Generación Z.
    Type of Medium: Online Resource
    ISSN: 1660-5373 , 1660-5373
    URL: Article
    DOI: 10.1108/TR-11-2022-0582
    RVK:
    QA 10000
    Language: English
    Publisher: Emerald
    Publication Date: 2023
    detail.hit.zdb_id: 2412174-5
    SSG: 3,2
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Research on the Mechanism of the Sustainable Development Model of Enterprises Based on Big Data Analysis Model
    Hindawi Limited ; 2021
    In:  Mobile Information Systems Vol. 2021 ( 2021-11-26), p. 1-12
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2021 ( 2021-11-26), p. 1-12
    Abstract: This paper adopts the analysis method of big data to conduct an in-depth analysis and research on the sustainable development mechanism of enterprises, firstly, combing the content and methods of enterprise business performance evaluation, defining enterprise sustainable development, and exploring the integration of enterprise sustainable development and enterprise business performance evaluation by specifically analyzing from different perspectives. Then, we analyze the industry in which the enterprise is located, its business situation, and strategy, and after analyzing the current business performance evaluation system of the enterprise, we point out its problems. The current performance evaluation system is incomplete, focuses only on economic benefits, is not long term, and does not consider the company’s strategy and stakeholders’ needs, which affects its importance and feasibility. Then, the above analysis is integrated to build a performance evaluation system consisting of four dimensions, namely, economic dimension, scientific research and innovation dimension, social dimension, and ecological dimension, from the perspective of sustainable development, and a total of 29 indicators are selected. Then, the two research tools of first value method and fuzzy comprehensive evaluation method were combined, based on the panel data of enterprises from 2016 to 2020; the first value method was used to get the weights of each indicator in the business performance evaluation system. The fuzzy comprehensive evaluation method was used to get a comprehensive evaluation score of enterprise’s business performance in 2019, and then the evaluation results were analyzed in detail and suggestions were made, which confirms that enterprises are based on the sustainable development perspective. The evaluation of business performance is necessary and important. Finally, we propose supporting safeguards, such as establishing a performance evaluation team and a monitoring mechanism, dynamically improving the enterprise performance evaluation system, establishing a sustainable corporate culture, preparing and publishing a sustainable development report, and accelerating the information construction of performance evaluation.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2021/4469255
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2187808-0
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    Online Resource
  • 6
    Online Resource
    Online Resource
    Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid
    Hindawi Limited ; 2021
    In:  Mobile Information Systems Vol. 2021 ( 2021-11-29), p. 1-9
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2021 ( 2021-11-29), p. 1-9
    Abstract: Short-term load forecasting is an important part to support the planning and operation of power grid, but the current load forecasting methods have the problem of poor adaptive ability of model parameters, which are difficult to ensure the demand for efficient and accurate power grid load forecasting. To solve this problem, a short-term load forecasting method for smart grid is proposed based on multilayer network model. This method uses the integrated empirical mode decomposition (IEMD) method to realize the orderly and reliable load state data and provides high-quality data support for the prediction network model. The enhanced network inception module is used to adaptively adjust the parameters of the deep neural network (DNN) prediction model to improve the fitting and tracking ability of the prediction network. At the same time, the introduction of hybrid particle swarm optimization algorithm further enhances the dynamic optimization ability of deep reinforcement learning model parameters and can realize the accurate prediction of short-term load of smart grid. The simulation results show that the mean absolute percentage error e MAPE and root-mean-square error e RMSE of the performance indexes of the prediction model are 10.01% and 2.156 MW, respectively, showing excellent curve fitting ability and load forecasting ability.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2021/8453896
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2187808-0
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    Online Resource
  • 7
    Online Resource
    Online Resource
    Distributed Structured Compressive Sensing-Based Time-Frequency Joint Channel Estimation for Massive MIMO-OFDM Systems
    Hindawi Limited ; 2019
    In:  Mobile Information Systems Vol. 2019 ( 2019-05-02), p. 1-16
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2019 ( 2019-05-02), p. 1-16
    Abstract: In massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, accurate channel state information (CSI) is essential to realize system performance gains such as high spectrum and energy efficiency. However, high-dimensional CSI acquisition requires prohibitively high pilot overhead, which leads to a significant reduction in spectrum efficiency and energy efficiency. In this paper, we propose a more efficient time-frequency joint channel estimation scheme for massive MIMO-OFDM systems to resolve those problems. First, partial channel common support (PCCS) is obtained by using time-domain training. Second, utilizing the spatiotemporal common sparse property of the MIMO channels and the obtained PCCS information, we propose the priori-information aided distributed structured sparsity adaptive matching pursuit (PA-DS-SAMP) algorithm to achieve accurate channel estimation in frequency domain. Third, through performance analysis of the proposed algorithm, two signal power reference thresholds are given, which can ensure that the signal can be recovered accurately under power-limited noise and accurately recovered according to probability under Gaussian noise. Finally, pilot design, computational complexity, spectrum efficiency, and energy efficiency are discussed as well. Simulation results show that the proposed method achieves higher channel estimation accuracy while requiring lower pilot sequence overhead compared with other methods.
    Type of Medium: Online Resource
    ISSN: 1574-017X , 1875-905X
    URL: Article
    DOI: 10.1155/2019/2634361
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2187808-0
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    Online Resource
  • 8
    Online Resource
    Online Resource
    Seeking excess returns under a posted price mechanism: Evidence from a peer‐to‐peer lending market
    Wiley ; 2021
    In:  The Manchester School Vol. 89, No. 5 ( 2021-09), p. 486-506
    Details
    In: The Manchester School, Wiley, Vol. 89, No. 5 ( 2021-09), p. 486-506
    Abstract: This study examines the performance of a new online peer‐to‐peer (P2P) lending platform in China that relies on non‐expert individuals to screen for loans. Using the bank deposit rate as a benchmark, positive excess returns exist under the posted price mechanism, which indicates that P2P markets provide lenders with adequate profit opportunities to compensate for investment risks. Moreover, we find that loans with higher excess returns are more likely to be funded and are bid on more quickly than other loans. Finally, voluntarily disclosed soft information in the listing's description plays a significant moderating role in the lenders’ decision‐making process. Borrowers who promise to repay on time are more likely to be funded and to be funded faster, but those who claim economic hardship have a lower probability of being funded. Our results provide evidence that lenders have the ability to seek excess returns in P2P lending markets and highlight that aggregating the views of peers can improve the market efficiency.
    Type of Medium: Online Resource
    ISSN: 1463-6786 , 1467-9957
    URL: Issue
    URL: Article
    DOI: 10.1111/manc.v89.5
    DOI: 10.1111/manc.12330
    RVK:
    QA 10000
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1418920-3
    detail.hit.zdb_id: 1473781-4
    SSG: 3,4
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    Online Resource
  • 9
    Online Resource
    Online Resource
    Estimating inter-regional payments for ecosystem services: Taking China’s Beijing-Tianjin-Hebei region as an example
    Elsevier BV ; 2020
    In:  Ecological Economics Vol. 168 ( 2020-02), p. 106514-
    Details
    In: Ecological Economics, Elsevier BV, Vol. 168 ( 2020-02), p. 106514-
    Type of Medium: Online Resource
    ISSN: 0921-8009
    URL: Article
    DOI: 10.1016/j.ecolecon.2019.106514
    RVK:
    QA 10000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 1002942-4
    detail.hit.zdb_id: 1500316-4
    SSG: 12
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  • 10
    Online Resource
    Online Resource
    Economic gains and environmental costs from China's exports: Regional inequality and trade heterogeneity
    Elsevier BV ; 2019
    In:  Ecological Economics Vol. 164 ( 2019-10), p. 106340-
    Details
    In: Ecological Economics, Elsevier BV, Vol. 164 ( 2019-10), p. 106340-
    Type of Medium: Online Resource
    ISSN: 0921-8009
    URL: Article
    DOI: 10.1016/j.ecolecon.2019.05.020
    RVK:
    QA 10000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 1002942-4
    detail.hit.zdb_id: 1500316-4
    SSG: 12
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    Online Resource
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