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Article
Peer-Review Record

A Novel Method for General Hierarchical System Modeling via Colored Petri Nets Based on Transition Extractions from Real Datasets

Appl. Sci. 2023, 13(1), 339; https://doi.org/10.3390/app13010339
by Stavros Souravlas 1,2,*,†, Sofia Anastasiadou 2,*,† and Irene Kostoglou 1,*,†
Reviewer 1: Anonymous
Reviewer 2:
Appl. Sci. 2023, 13(1), 339; https://doi.org/10.3390/app13010339
Submission received: 2 November 2022 / Revised: 19 December 2022 / Accepted: 20 December 2022 / Published: 27 December 2022

Round 1

Reviewer 1 Report

The paper is clear and well-written. However, some improvements are necessary.

 

My major concerns:

  1. The introduction lists contributions, but the novelty in the context of the literature is not well defined. A sentence clarifying the novelty should end the “Related Works” section.
  2. It is not clear why six self-references ([7]-[12]) are helpful at line 94. Only the three most recent references would suffice. Otherwise, an explanation of the six references should be added to the section.
  3. When dealing with real datasets, details allowing readers to uniquely identify data and eventually reproduce experiments are crucial. The real name of the company is needed, as it is not sensitive information in cases like this.
  4. The comparison between real data and simulative results should be enriched by simulating one or two different approaches from the literature to prove the effectiveness of the modelling strategy.
  5. Conclusions should highlight the relevance of the paper for the reader as well as future plans for carrying on the research on the topic.

 

Minor comments:

  • There is a wrongly capitalized “An” on line 99.
  • Wrong spacing on line 191.
  • A reference is missing on line 600.

Author Response

Dear reviewer,

 

kindly find attached our responses to your valuable comments. Thank you for your efforts that were really important and we hope that you find our comments sufficient to support our work.

 

Best regards

Stavros Souravlas

Author Response File: Author Response.pdf

Reviewer 2 Report

A modeling apprach is presented in this manuscript. The article is very poorly 

written.

a) The introduction is totally unclear.

b) The aim of the MS is not clear from the introduction.

c) It is better to make a short literarure survey before embarking on the objectives of the reported work. The objectives have to be specified distinctly.

d) The abstract requires full editing. It misses the essence of the article. It has become more narrative.

e) Line no 157, 158; the notation seems to be erroneous.

f) Authors need to justify why there is so much dissimilarity between real data and simulated data.

g) Give there exist considerable biases from rela data and simulated data, how can then author justify the modeling approach used in the work?

There must be some lacunae which need to be addresed.

i) It is recommended that authors should take the help of native authors.

 

Author Response

Dear reviewer,

 

kindly find attached our responses to your valuable comments. Thank you for your efforts that were really important and we hope that you find our comments sufficient to support our work.

 

Best regards

Stavros Souravlas

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors addressed all my comments. I recommend acceptance of the work in its current form.

Author Response

We would like to thank the reviewer for supporting our work

 

Best regards

Prof. Stavros Souravlas

Reviewer 2 Report

The revised MS is still missing certain aspects.

a) The revised abstract is still unclear. Lines 8 to 9 need modification as it yields no meaning.

b) Lines 27 to 28 presents a unclear picture.

c) There is repetition of definitions in Introduction as well as in lines 147 to 170.

d) There are no cited references in section 1 which is newly added.

e) Lines 134 to 135 need modification.

f) Lines 192 to 194, the definitions seem to be totally ambiguous.   

g) Lines 463 to 464, how authors are so sure about paucity of existing approaches? There are plenty of literatures related to generation of models based on real data set. 

Author Response

Dear reviewer,

kindly find attached our response to your valuable comments.

Best regards

Stavros Souravlas

Author Response File: Author Response.pdf

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