Ju Dong Park | Logistics | Innovative Research Award

Published on by Global CAD Awards

Innovative Research Award

Ju Dong Park
Gyeongsang National University,South Korea

                Ju Dong Park
Affiliation Gyeongsang National University
Country South Korea
Scopus ID 57219533922
Documents 4
Citations 103
h-index 3
Subject Area Logistics
Event Global Cad Awards
Google Scholar ID vmG89cYAAAAJ

Ju Dong Park is a researcher associated with Gyeongsang National University, South Korea, whose supplied publication record spans transportation, environmental efficiency, healthcare performance, predictive analytics, and infrastructure materials. His listed work applies quantitative and computational approaches to practical problems, including data envelopment analysis, statistical modeling, and machine learning. This research profile presents his documented scholarly contributions in the context of the Innovative Research Award at the Global Cad Awards. [1]

Abstract

Ju Dong Park’s supplied publication record illustrates research addressing efficiency measurement, public health response, transportation-sector environmental performance, predictive workforce analytics, and railway materials. The studies use quantitative decision-support methods to investigate operational and societal challenges. Collectively, these topics demonstrate the relevance of interdisciplinary analytical research to logistics and related systems. The Innovative Research Award provides a context for evaluating methodological contribution, research relevance, and documented scholarly engagement. [2]

Keywords

Ju Dong Park; logistics; data envelopment analysis; environmental efficiency; transportation systems; healthcare analytics; machine learning; predictive modeling; railway materials; research impact; Innovative Research Award.

Introduction

Research into complex operational systems increasingly combines statistical techniques with computational models to support evidence-based decisions. Efficiency assessment can compare performance across organizations, while predictive analytics can help investigate patterns in health services and workforce outcomes. Park’s listed publications reflect these approaches across several application areas. The available bibliographic information provides a basis for describing the topics and methods represented in his record, although it does not independently establish the full extent of his individual contribution to each collaborative study. [3]

Research Profile

Park’s supplied publications indicate engagement with quantitative analysis and interdisciplinary problem-solving. His co-authored research addresses environmental efficiency in aviation, the performance of US states during the COVID-19 pandemic, transportation-related air-quality measurement, nurse-turnover prediction, and asphalt binder and mixture characterization for railway applications. These topics connect operational research with transportation, healthcare, and infrastructure. The available record identifies him as a co-author rather than establishing lead responsibility for every study. [2]

Research Contributions

The listed studies represent several complementary methodological contributions:

  • Efficiency measurement: Directional distance function data envelopment analysis is used to evaluate environmental efficiency in the US airline industry.
  • Public-sector performance: An integrated DEA, classification and regression tree, and logistic regression approach examines state responses to COVID-19.
  • Environmental indicators: Principal component analysis supports the development of an aggregate air-quality index for the US transportation sector.
  • Predictive analytics: Synthetic minority over-sampling and machine-learning algorithms address nurse-turnover prediction with highly imbalanced data.
  • Railway materials: A 2025 article examines asphalt binder and mixture characterization for enhanced railway applications. [4]

Publications

The supplied bibliography identifies the following selected publications. Author order and publication details are reproduced from the provided records and should be checked against the publishers’ versions before formal citation.

  1. Xu, Y., Park, Y. S., Park, J. D., & Cho, W. (2021). Evaluating the environmental efficiency of the US airline industry using a directional distance function DEA approach. Journal of Management Analytics, 8(1), 1–18.
  2. Xu, Y., Park, Y. S., & Park, J. D. (2021). Measuring the response performance of US states against COVID-19 using an integrated DEA, CART, and logistic regression approach. Healthcare, 9(3), 268.
  3. Choi, J., Park, Y. S., & Park, J. D. (2015). Development of an aggregate air quality index using a PCA-based method: A case study of the US transportation sector. American Journal of Industrial and Business Management, 5(2), 53–65.
  4. Xu, Y., Park, Y., Park, J. D., & Sun, B. (2023). Predicting nurse turnover for highly imbalanced data using the synthetic minority over-sampling technique and machine learning algorithms. Healthcare, 11(24), 3173.
  5. Na, I., Park, H., Yun, J., Park, J., & Kim, H. (2025). Characterization of asphalt binder and mixture for enhanced railway applications. Materials. The supplied record reports three citations for this article. [5]

Research Impact

The supplied profile figures report four documents, 103 citations, and an h-index of 3. These metrics offer a quantitative snapshot of the indexed record but can change as databases are updated. They should be interpreted alongside publication quality, methodological rigor, research context, and the nature of each contribution. Because the figures have not been independently verified here, they are presented as supplied rather than as a live database assessment. [1]

Award Suitability

For consideration under the Innovative Research Award at the Global Cad Awards, the listed research offers relevant examples of analytical methods applied to real-world systems. The combination of efficiency modeling, environmental measurement, predictive algorithms, and infrastructure-related investigation provides a basis for assessing methodological breadth and practical relevance. A complete evaluation should additionally examine the original papers, Park’s specific role in collaborative work, evidence of novelty, and independently verified research metrics. The publication record alone does not establish an award outcome.

Conclusion

Ju Dong Park’s supplied research record covers several fields in which quantitative methods can inform operational, environmental, healthcare, and infrastructure decisions. The selected publications demonstrate a range of analytical applications and provide a foundation for a research-focused recognition profile. Further verification of bibliographic details, individual contributions, and current citation indicators would strengthen a formal assessment for the Innovative Research Award.

References

  1. Elsevier. (n.d.). Scopus author details: Ju Dong Park, Author ID 57219533922. Scopus. Profile and citation figures should be verified against the current record.
    https://www.scopus.com/authid/detail.uri?authorId=57219533922
  2. Xu, Y., Park, Y. S., Park, J. D., & Cho, W. (2021). Evaluating the environmental efficiency of the US airline industry using a directional distance function DEA approach. Journal of Management Analytics, 8(1), 1–18. A DOI was not supplied; no DOI is asserted here.
  3. Xu, Y., Park, Y. S., & Park, J. D. (2021). Measuring the response performance of US states against COVID-19 using an integrated DEA, CART, and logistic regression approach. Healthcare, 9(3), 268. https://doi.org/10.3390/healthcare9030268
  4. Xu, Y., Park, Y., Park, J. D., & Sun, B. (2023). Predicting nurse turnover for highly imbalanced data using the synthetic minority over-sampling technique and machine learning algorithms. Healthcare, 11(24), 3173. https://doi.org/10.3390/healthcare11243173
  5. Na, I., Park, H., Yun, J., Park, J., & Kim, H. (2025). Characterization of asphalt binder and mixture for enhanced railway applications. Materials. The supplied bibliographic record does not include a DOI or volume and page information; consult the publisher record for verification.
  6. Global Cad Awards. (n.d.). Official award website. Event and award information.
    https://globalcadawards.com/