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Data engineering - solution for the lifetime of chemical compounds
Published:
15 March 2023
by MDPI
in MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
congress CHEMBIO.INFO-09: Cheminfo., Chemom., Comput. Quantum Chem. & Bioinfo. Congress München, GR-Chapel Hill, USA, 2023.
https://doi.org/10.3390/mol2net-09-14213
(registering DOI)
Abstract:
Abstract.
Every year, mankind and the environment are exposed to chemicals. Numerous chemicals may present a risk to health or the environment during production, processing, distribution in commerce, use or end of use.
Through data engineering it is possible to trace chemicals, estimate emissions and identify possible exposure scenarios for the different chemical compounds at the end of life of the industrial processes involved. This mini review identified case studies based on food, pharmaceuticals and N-hexane, concluding that data engineering can help to track chemicals in waste streams generated in industrial activities handled, identifying possible exposure scenarios to a chemical in question.
Keywords: data engineering; EoL (End of Life) ,PAU (Pollution Abatement Unit)
Comments on this paper
Ajit Singh
27 December 2023
The research paper titled "Data Engineering - Solution for the Lifetime of Chemical Compounds" addresses a critical aspect of our modern world, where the exposure to chemicals poses risks to both human health and the environment. The abstract succinctly captures the essence of the paper, emphasizing the potential of data engineering in mitigating these risks throughout the lifecycle of chemical compounds.
The paper makes a compelling argument for the application of data engineering to trace chemicals, estimate emissions, and identify exposure scenarios at various stages of industrial processes. By focusing on case studies related to food, pharmaceuticals, and N-hexane, the authors effectively illustrate the versatility of data engineering in tracking chemical compounds within diverse industrial contexts.
One notable strength of the paper is its interdisciplinary approach, linking data engineering with environmental and health concerns. This holistic perspective is vital for addressing the complex challenges posed by chemical compounds throughout their lifecycle.
The paper makes a compelling argument for the application of data engineering to trace chemicals, estimate emissions, and identify exposure scenarios at various stages of industrial processes. By focusing on case studies related to food, pharmaceuticals, and N-hexane, the authors effectively illustrate the versatility of data engineering in tracking chemical compounds within diverse industrial contexts.
One notable strength of the paper is its interdisciplinary approach, linking data engineering with environmental and health concerns. This holistic perspective is vital for addressing the complex challenges posed by chemical compounds throughout their lifecycle.
Humbert G. Díaz
1 January 2024
Dear author(s), Happy New Year 24, Thank you for your contribution to our conference!!!
We have a question for you, you can read and answer bellow.
Question for Authors:
According to your experience what are the SWOT on using Phyton machine learning libraries vs. user friendly interfaces for drug discovery?
REVIEWWWERS'23 participation:
We also invite you to participate in the REVIEWWWERS Workshop, which is now open, by making questions to other authors.
The steps are very easy. instructions: Step(1), Register/Login here [Register/Login] to Sciforum platform. Step(2), Go to presetations list [MOL2NET'23 Papers List], Step(3), Scroll down papers list and click on one title. Step(4), Scroll down and click on Commenting button, post your comment, and click submit. Step(5), Repeat review process for other papers. Step(6), Request certificate. See details [Reviewers Workshop] or contact us at Email: mol2net.chair@gmail.com.
We have a question for you, you can read and answer bellow.
Question for Authors:
According to your experience what are the SWOT on using Phyton machine learning libraries vs. user friendly interfaces for drug discovery?
REVIEWWWERS'23 participation:
We also invite you to participate in the REVIEWWWERS Workshop, which is now open, by making questions to other authors.
The steps are very easy. instructions: Step(1), Register/Login here [Register/Login] to Sciforum platform. Step(2), Go to presetations list [MOL2NET'23 Papers List], Step(3), Scroll down papers list and click on one title. Step(4), Scroll down and click on Commenting button, post your comment, and click submit. Step(5), Repeat review process for other papers. Step(6), Request certificate. See details [Reviewers Workshop] or contact us at Email: mol2net.chair@gmail.com.
Benjamin Villa
5 January 2024
Dear Author,
Happy 2024!
I have some questions for you:
1. In the paper, it was mentioned that more data shall be collected to increase the reliability of data-driven models. Does this mean that data engineering might not be applicable if the researcher only has limited or less amount of data?
2. What are the possible advantages and disadvantages of using data engineering?
Happy 2024!
I have some questions for you:
1. In the paper, it was mentioned that more data shall be collected to increase the reliability of data-driven models. Does this mean that data engineering might not be applicable if the researcher only has limited or less amount of data?
2. What are the possible advantages and disadvantages of using data engineering?
