Connecting Cities: Solving Optimal-Resource-Distribution Problem Using Critical Range Radius

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The title of the manuscript closely addresses the hot issues of smart cities and logistics optimization, proposing the MSPP algorithm based on Morphological Mathematics, which provides a new approach for resource distribution path planning in dense urban road networks. The literature review covers classic algorithms (such as Dijkstra and A*) and emerging technologies (such as genetic algorithms and ant colony algorithms), and highlights the advantages of MSPP by comparing the pros and cons of different methods through Table 1. The research applies morphological operators (such as dilation and erosion) to graph search, combines the concept of Critical Radius, optimizes path planning efficiency, and has clear innovation points. The algorithm design is clear, and the pseudo-code (Page 5) demonstrates bidirectional dilation operations from the starting point to the end point, with complete logic. The experimental design is basically reasonable. The research takes Mexico City as a case study, combining real traffic data (such as speed statistics from INEGI and SEMOVI) to verify the practicality of the algorithm. Here are some improvement suggestions: (1) Improve theoretical depth and algorithm details to compensate for the lack of complexity analysis. The paper mentions that MSPP is more efficient than traditional algorithms, but does not provide theoretical proof of time/space complexity or comparative experimental data (such as time consumption comparison with A * or Dijkstra). And the explanation of parameter λ is insufficient: the physical meaning of the structural element λ in the pseudocode is not clear (such as whether it represents the distance between blocks in the actual road network), and it needs to be supplemented with definitions and parameter tuning basis; (2) Optimize the experimental part. Insufficient data transparency and lack of publicly available experimental datasets or code (the Data Availability Statement only mentions' Data is contained within the article '), which is not conducive to reproducibility; The statistical significance is insufficient, as the difference in delivery time in Figure 4 has not been validated by hypothesis testing (such as p-value), and additional statistical analysis is needed; The paper only verifies the effectiveness of MSPP itself, lacking comparison with existing algorithms (such as A *) in the same scenario, and the benchmark for comparison is insufficient; (3) Language and formatting issues: Consistency should be emphasized in the expression of terminology in the paper, for example, "Pathological Shortness Path Planning" should be "Shortest" (multiple spelling errors throughout the text); Geodetic distance "should be" Geodetic distance "; Partial chart annotations are missing, for example, subgraphs (a) and (e) in Figure 3 require the addition of scale bars and area labels; The content of the "Illustration" column in Table 2 (Page 10) is missing; (4) Specific data should be added to the conclusion (such as "MSPP reduces delivery time by X%"), and Page 14 needs to specify the technical route (such as GPU acceleration or distributed framework).

Comments on the Quality of English Language

The language and expression of the manuscript need further refinement.

Author Response

REVIEWER 1

Detailed response to paper evaluation

• In response to the comments received regarding the proposal, the introduction has been revised to enhance clarity, with a focus on emphasizing the contributions outlined in the paper. The abstract and introduction now explicitly refer to the primary contributions and are supplemented with measures of efficiency.
• The results section has been enhanced to include supplementary tests and comparisons, particularly with other widely accepted methodologies, in order to substantiate the strengths and limitations of the proposal. Additionally, it incorporates a table detailing the computational resources required to determine the most reliable path trajectory using various established approaches.
• All figures and materials are upgraded to improve quality and are supplemented with superior element resolution.

Concrete responses to all suggestions and issues

1. Improve theoretical depth and algorithm details to compensate for the lack of complexity analysis. The paper mentions that MSPP is more efficient than traditional algorithms, but does not provide theoretical proof of time/space complexity or comparative experimental data (such as time consumption comparison with A * or Dijkstra). And the explanation of parameter λ is insufficient: the physical meaning of the structural element λ in the pseudocode is not clear (such as whether it represents the distance between blocks in the actual road network), and it needs to be supplemented with definitions and parameter tuning basis;

Thank you for your comment. To supplement the details of the algorithm, we have incorporated a description of its functionality in Section 3.1, where the significance of the structural element is also discussed, as well as its use in navigating the graph search space (Lines 267-278). Additionally, an improved version of Algorithm 1 has been included (Lines 279-295), accompanied by a brief analysis of its computational complexity in Section 3.2 (Lines 329-348). To demonstrate the efficiency of the MSPP approach in comparison to conventional algorithms, a comparison is provided based on the number of integer operations required to compute an optimal trajectory (see Table 3).

2. Optimize the experimental part. Insufficient data transparency and lack of publicly available experimental datasets or code (the Data Availability Statement only mentions' Data is contained within the article '), which is not conducive to reproducibility; The statistical significance is insufficient, as the difference in delivery time in Figure 4 has not been validated by hypothesis testing (such as p-value), and additional statistical analysis is needed; The paper only verifies the effectiveness of MSPP itself, lacking comparison with existing algorithms (such as A *) in the same scenario, and the benchmark for comparison is insufficient;

Thank you for your comment. To ensure reproducibility, we include all tables and maps used in the analysis as supplementary material. To respect the statistical significance of Figure 4, it is indeed necessary to validate it at this juncture. A more comprehensive analysis of the distribution, which we intend to discuss in a subsequent publication, involves modeling the non-parametric distribution and its principal parameters. This is complemented by a comparative study of efficiency based on the analysis in terms of the number of integer operations that can be processed using the MSPP algorithm, the Three A* algorithm, and Dijkstra's algorithm, as summarized in Table 3. This discussion is also addressed and elaborated upon from line 449 to line 467.

3. Language and formatting issues: Consistency should be emphasized in the expression of terminology in the paper, for example, "Pathological Shortness Path Planning" should be "Shortest" (multiple spelling errors throughout the text); Geodetic distance "should be" Geodetic distance "; Partial chart annotations are missing, for example, subgraphs (a) and (e) in Figure 3 require the addition of scale bars and area labels; The content of the "Illustration" column in Table 2 (Page 10) is missing;

Thank you. We have corrected these typographical errors and conducted a comprehensive review of similar mistakes throughout the document. Additionally, errors within the annotations of the graphs and figures have been addressed and rectified. The content of Table 2 has been supplemented with an image to facilitate the identification of specific areas and to establish their correlation with the table.

4. Specific data should be added to the conclusion (such as "MSPP reduces delivery time by X%"), and Page 14 needs to specify the technical route (such as GPU acceleration or distributed framework).

Thank you for your comment. You are correct regarding the recommendation and the inclusion of specific quantitative data derived from the experimental results, as summarized in Table 3.

Reviewer 2 Report

Comments and Suggestions for Authors

This study presents a case study in Mexico city of optimal path problem in the context of delivery context using the so-called MSPP algorithms. I have some major concerns about this study, before clearly addressing these issues, I don't think this study is ready for publication:

1. the motivation of this study is not clear. there are many algorithms to search for the optimal path, so why this study adopts another algorithm (i.e., MSPP)? what it the advantages and disadvantages of this algorithm?
2. the innovation of this study is not clear. The MSPP algorithm is not originally proposed by the this study. to me, this study is just a case study of applying the MSPP algorithm to the Mexico city. So what is the innovation and contribution of this study?
3. I suggest to present a detailed section about the MSPP algorithm before section 4.
4. The context/background of the case study in section 4 is not cleary presented.
5. the results of the case study are not fully discussed. at least, the authors can compare the results from different algorithms to varify the outperformance of MSPP algorithm.

Author Response

REVIEWER 2

Detailed response to paper evaluation

• In response to the comments received regarding the proposal, the introduction has been revised to enhance clarity, with a focus on emphasizing the contributions outlined in the paper. The abstract and introduction now explicitly refer to the primary contributions and are supplemented with measures of efficiency.
• The paper has revised the introduction sections and proposal to enhance precision and detail in the theoretical foundations of the proposal. Furthermore, explanations have been added to enrich the discussion and improve clarity. Additionally, a performance table has been incorporated to illustrate the contribution of the proposal.  
• The theoretical foundations of the algorithms employed (MSPP) are incorporated and examined, elucidating and substantiating their application within the proposal. Furthermore, a performance table has been included to reinforce the utilization of MSPP in this case study.
• The results section has been enhanced to include supplementary tests and comparisons, particularly with other widely accepted methodologies, to substantiate the strengths and limitations of the proposal. Additionally, it incorporates a table detailing the computational resources required to determine the most reliable path trajectory using various established approaches.
• The conclusions and future works sections were revised to enhance clarity and incorporate quantitative data regarding the performance of the proposal when employing the MSPP approach.
• All figures and materials are upgraded to improve quality and are supplemented with superior element resolution.

 

Concrete responses to all suggestions and issues

1. The motivation of this study is not clear. there are many algorithms to search for the optimal path, so why this study adopts another algorithm (i.e., MSPP)? what it the advantages and disadvantages of this algorithm?

You are correct; to enhance clarity regarding the motivation, the explanation is supplemented from line 87 to line 132 by detailing the advantages and disadvantages of the MSPP algorithm in comparison with classical algorithms. Additionally, in the results section, an additional table of performance was added (Table 3) that compares the MSPP performance with other well-accepted approaches.

2. The innovation of this study is not clear. The MSPP algorithm is not originally proposed by the this study. to me, this study is just a case study of applying the MSPP algorithm to the Mexico city. So what is the innovation and contribution of this study?

Thank you for your comment. To clarify this point, it is addressed and elaborated upon in Section 1 (lines 87-108), where the main contributions of the work are also discussed (lines 125-132) as a strategy to compute the time response using the MSPP algorithm.

3. I suggest to present a detailed section about the MSPP algorithm before section 4.

Thank you for your suggestion. To further clarify the operation and significance of the MSPP algorithm, lines 258 to 295 have been added, where the variables and considerations related to the algorithm's implementation are explained in detail, including the enhanced implementation to detect non-decidable situations.

4. The context/background of the case study in section 4 is not cleary presented.

Thanks for your comment. The case study was rewritten to gain clarity about the context and the importance of computing the time of delivery, and we have also appended the primary contributions of this work at the conclusion of Section 1 (lines 87-132).

5. The results of the case study are not fully discussed. at least, the authors can compare the results from different algorithms to varify the outperformance of MSPP algorithm.

To verify the performance of the proposed strategy, which implements the MSPP algorithm, a comparison has been conducted with two well-established algorithms in the literature: A* and Dijkstra's Algorithm, focusing on the number of integer operations required to compute the optimal trajectory. This comparison is presented in lines 449-467 and summarized in Table 3.

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript focused on the optimal routes to a delivery center. The research has some significance for efficient resource delivery in urban areas.There are several comments for improvement. 

1. Suggest providing key quantitative results in the abstract and conclusion to support the effectiveness of the research.
2. The reference number is not shown in line 217.
3. Please explain the software tools used for calculation and analysis.
4. Please provide a compass in Figure 1 and explain the source of the background map.
5. The title of Table 2 should be above the table.
6. Please provide the theoretical models and mathematical formulas used in the research.
7. Finally, summarize and explain the shortcomings and limitations of the research, thus leading to future research prospects.

Author Response

REVIEWER 3

Detailed response to paper evaluation

• In response to the comments received regarding the proposal, the introduction has been revised to enhance clarity, with a focus on emphasizing the contributions outlined in the paper. The abstract and introduction now explicitly refer to the primary contributions and are supplemented with measures of efficiency.
• The paper has revised the introduction sections and proposal to enhance precision and detail in the theoretical foundations of the proposal. Furthermore, explanations have been added to enrich the discussion and improve clarity. Additionally, a performance table has been incorporated to illustrate the contribution of the proposal.  
• The theoretical foundations of the algorithms employed (MSPP) are incorporated and examined, elucidating and substantiating their application within the proposal. Furthermore, a performance table has been included to reinforce the utilization of MSPP in this case study.
• The results section has been enhanced to include supplementary tests and comparisons, particularly with other widely accepted methodologies, to substantiate the strengths and limitations of the proposal. Additionally, it incorporates a table detailing the computational resources required to determine the most reliable path trajectory using various established approaches.
• The conclusions and future works sections were revised to enhance clarity and incorporate quantitative data regarding the performance of the proposal when employing the MSPP approach.
• All figures and materials are upgraded to improve quality and are supplemented with superior element resolution.

 

Concrete responses to all suggestions and issues

 

1. Suggest providing key quantitative results in the abstract and conclusion to support the effectiveness of the research.

We accept the recommendation, add quantitative values to complement the abstract, and also include these values in the conclusions, which clarifies the analysis of the results from the experimentation process.

 

2. The reference number is not shown in line 217.

Thank you for your comment. Added the correct reference.

3. Please explain the software tools used for calculation and analysis.

Thank you for your comment. To complement a paragraph, a section was added (Line 441-448) describing the programming language in which the algorithm was implemented, as well as the necessary libraries, to guarantee the replicability of the experimental process.

4. Please provide a compass in Figure 1 and explain the source of the background map.

You are correct, and the image should be complemented with a compass. Additionally, a scale bar indicating the natural logarithm of the figures should be included, along with a clarification of the source of the maps.

5. The title of Table 2 should be above the table.

Thank you for your observation. The location of the Table 2 label has been corrected to its correct position, above the table, not below it.

6. Please provide the theoretical models and mathematical formulas used in the research.

You are right. To gain clarity, the theoretical model was extended in Section 3.1, where the explanation of the theoretical model used is supplemented with variables related in lines 258-278, and also in the computational complexity analysis of the algorithms discussed in lines 329-348.

7. Finally, summarize and explain the shortcomings and limitations of the research, thus leading to future research prospects.

We concur with the recommendation, which we have incorporated by revising Section 5.1, 'Future Work,' to articulate the strategy's limitations, thereby fostering new avenues for future research.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript has proposed a resource delivery optimization strategy based on morphological shortest path planning (MSPP) for path planning and delivery time analysis in high-density urban environments. The optimization problem of logistics distribution paths in high-density cities, combined with practical constraints such as traffic flow and time windows, has a clear engineering application background, and the selected research problem has great practical significance. The research method used in the paper is relatively novel, introducing the dilation operator in mathematical morphology for graph search and proposing the MSPP algorithm. Compared with traditional Dijkstra, A and other methods, it significantly improves the computational efficiency by about 60%. Meanwhile, taking Mexico City as a case study, multiple distribution centers were selected for analysis, combined with real geographic data and traffic speed, enhancing the credibility and practicality of the research. The overall structure of the paper is complete and logically coherent. However, there are still areas in the research that need to be revised and improved in the following aspects:

(1) There are many errors in language expression and grammar: some sentences are lengthy, structurally disordered, and have obvious grammar and vocabulary problems, seriously affecting the reading experience. It is recommended to polish the language throughout the article, especially by adjusting technical terminology and long sentence structures. For example, 'At Crowded Cities, the delivery good task and services become a challenge...' should be modified to 'At Crowded Cities, the delivery good task and services become a challenge...' In crowded cities, the task of delivering goods and services becomes challenging due to complex road infrastructure...

(2) The introduction should highlight the research gap and innovative points, clearly explaining why existing methods are insufficient and how MSPP can make up for them.

(3) The abstract section should summarize the research objectives, methods, results, and conclusions more concisely and clearly, and clearly explain the advantages of MSPP compared to traditional methods.

(4) The literature review section suggests categorizing the methods in Table 1 more systematically, such as by "graph search class", "optimization algorithm class", "hybrid method class", etc. For each type of method involved, a brief summary of its advantages and disadvantages should be provided to lay a more comprehensive foundation for the proposal of MSPP.

(5) The mathematical expression and algorithm description of the formulas in the paper are not rigorous enough, and some formulas and algorithm pseudocode are expressed vaguely, lacking strict mathematical definitions. The meanings of nodes, edges, and weights (such as distance and time) in the graph should be clearly defined. Algorithm 1 pseudocode should use a standard format, with consistent variable names and clear annotations. Suggest supplementing the mathematical formal description of MSPP, such as the mathematical definition of inflation operator.

(6) Lack of in-depth discussion and analysis of algorithm limitations, including inadequate explanation of adaptability to complex situations such as terrain height and multi-layer road networks in the paper.

(7) The experimental part needs further deepening, lacking detailed comparisons with other algorithms (such as time efficiency, path quality, etc.), and using only integer operands (IntOps) as an evaluation metric is somewhat weak. The process of obtaining and processing data sources (such as map data and traffic speed data) should be clearly explained. Suggest adding multi-dimensional comparisons such as path length and computation time with other algorithms (such as Dijkstra, A) under the same experimental settings.

(8) The results and discussion section should be combined with charts to analyze in depth the reasons for the differences in delivery time in different regions and time periods. Suggest the author to discuss the feasibility of MSPP in practical deployment (such as real-time and scalability).

(9) There are shortcomings in the conclusion and future work. The conclusion should be more concise, highlighting contributions and practical significance. The future work can be supplemented with multi-objective optimization (such as cost, time, carbon emissions), dynamic real-time path adjustment, and traffic prediction combined with machine learning.

(10) Some details issues. For example, the format of charts and references is not consistent; Unclear labeling of charts and inconsistent reference formats; The coordinate axes, units, and legends should be clearly labeled on the charts (Figures 4 and 5) to avoid blurring or duplication. Unified reference format (currently some have DOI, while others do not); Ensure that all chart numbers, titles, and references are consistent; Check for duplicate citations or omissions of important literature; Check for spelling errors (such as "road" or "transportation" instead of "via"). Ensure that all abbreviations are given their full names (such as MSPP, MM, etc.) when they first appear.

Comments on the Quality of English Language

Suggest the author to conduct in-depth polishing of the manuscript.

Author Response

Response to all comments and Issues

We express our gratitude to all reviewers for their comments and concerns, which have been provided with the aim of improving this document. In response to all comments, we have provided a detailed reply for each one, explicitly indicating the corrections made.

Thank you for your valuable feedback.

In response to the concerns raised by the reviewers, the majority of which pertained to the style and formatting of the document, we elected not to highlight the modified sections, as most of the text was altered to enhance clarity.

Reviewer 1

This manuscript has proposed a resource delivery optimization strategy based on morphological shortest path planning (MSPP) for path planning and delivery time analysis in high-density urban environments. The optimization problem of logistics distribution paths in high-density cities, combined with practical constraints such as traffic flow and time windows, has a clear engineering application background, and the selected research problem has great practical significance. The research method used in the paper is relatively novel, introducing the dilation operator in mathematical morphology for graph search and proposing the MSPP algorithm. Compared with traditional Dijkstra, A and other methods, it significantly improves the computational efficiency by about 60%. Meanwhile, taking Mexico City as a case study, multiple distribution centers were selected for analysis, combined with real geographic data and traffic speed, enhancing the credibility and practicality of the research. The overall structure of the paper is complete and logically coherent. However, there are still areas in the research that need to be revised and improved in the following aspects:

• There are many errors in language expression and grammar: some sentences are lengthy, structurally disordered, and have obvious grammar and vocabulary problems, seriously affecting the reading experience. It is recommended to polish the language throughout the article, especially by adjusting technical terminology and long sentence structures. For example, 'At Crowded Cities, the delivery good task and services become a challenge...' should be modified to 'At Crowded Cities, the delivery good task and services become a challenge...' In crowded cities, the task of delivering goods and services becomes challenging due to complex road infrastructure...

Response

Thank you for your comment. We conducted a thorough review of the coherence in English, rewrote several paragraphs, and improved the English style. Other sections were also rewritten to enhance clarity.

• The introduction should highlight the research gap and innovative points, clearly explaining why existing methods are insufficient and how MSPP can make up for them.

Response

You are correct; the introduction has been revised and improved to enhance clarity and to demonstrate the paper's contribution clearly.

• The abstract section should summarize the research objectives, methods, results, and conclusions more concisely and clearly, and clearly explain the advantages of MSPP compared to traditional methods.

Response

The abstract has now been improved, with enhancements to the contribution. Additionally, an explanation is provided regarding the rationale for utilizing MSPP in comparison to other well-established approaches.

• The literature review section suggests categorizing the methods in Table 1 more systematically, such as by "graph search class", "optimization algorithm class", "hybrid method class", etc. For each type of method involved, a brief summary of its advantages and disadvantages should be provided to lay a more comprehensive foundation for the proposal of MSPP.

Response

The comment was accepted, and the table was revised to address your concerns regarding the category separation, including the addition of a column for advantages and disadvantages.

• The mathematical expression and algorithm description of the formulas in the paper are not rigorous enough, and some formulas and algorithm pseudocode are expressed vaguely, lacking strict mathematical definitions. The meanings of nodes, edges, and weights (such as distance and time) in the graph should be clearly defined. Algorithm 1 pseudocode should use a standard format, with consistent variable names and clear annotations. Suggest supplementing the mathematical formal description of MSPP, such as the mathematical definition of inflation operator.

Response

Thank you for your comment. As this paper employs the MSPP algorithm, our aim is to illustrate the algorithm practically. A comprehensive explanation is available in the referenced material. Nevertheless, the text has been revised to enhance clarity regarding the algorithm and to indicate areas where readers may require a more thorough understanding.

• Lack of in-depth discussion and analysis of algorithm limitations, including inadequate explanation of adaptability to complex situations such as terrain height and multi-layer road networks in the paper.

Response

The discussion and analysis of the algorithms have been revised in the results and conclusions sections to provide greater depth regarding the theoretical rationale and justification for the effectiveness of Morphological Approaches in modeling complex scenarios.

• The experimental part needs further deepening, lacking detailed comparisons with other algorithms (such as time efficiency, path quality, etc.), and using only integer operands (IntOps) as an evaluation metric is somewhat weak. The process of obtaining and processing data sources (such as map data and traffic speed data) should be clearly explained. Suggest adding multi-dimensional comparisons such as path length and computation time with other algorithms (such as Dijkstra, A) under the same experimental settings.

Response

Thank you for your comment, and I appreciate the additional information regarding the quality of images used and the dimensions, which complement the details of the IntOps performed by each algorithm. This supplementary information aids in contextualizing the amount of data required to compute each trajectory as a reference.

• The results and discussion section should be combined with charts to analyze in depth the reasons for the differences in delivery time in different regions and time periods. Suggest the author to discuss the feasibility of MSPP in practical deployment (such as real-time and scalability).

Response

Thank you for your comment; you are correct. We expand upon the opportunity areas of the algorithm and additional cases and research directions derived from this proposal. The detailed discussion was addressed in the sections on results, conclusions, and future work.ns.

• There are shortcomings in the conclusion and future work. The conclusion should be more concise, highlighting contributions and practical significance. The future work can be supplemented with multi-objective optimization (such as cost, time, carbon emissions), dynamic real-time path adjustment, and traffic prediction combined with machine learning.

Response

Thank you for your comments. We regard your feedback as prudent. The conclusions have been revised to encompass contributions from both theoretical and practical perspectives, and detailed research opportunities based on the proposal have been incorporated.

• Some details issues. For example, the format of charts and references is not consistent; Unclear labeling of charts and inconsistent reference formats; The coordinate axes, units, and legends should be clearly labeled on the charts (Figures 4 and 5) to avoid blurring or duplication. Unified reference format (currently some have DOI, while others do not); Ensure that all chart numbers, titles, and references are consistent; Check for duplicate citations or omissions of important literature; Check for spelling errors (such as "road" or "transportation" instead of "via"). Ensure that all abbreviations are given their full names (such as MSPP, MM, etc.) when they first appear.

Response

Thank you for your insightful comment. You are correct that there were several errors in the figures and some references. To resolve the issue related to resolution deficiencies, we normalized all included figures to a quality of 300 dpi. We acknowledge that the use of Edge Explorer, which features its own viewer, may induce confusion in the paper due to software limitations. Conversely, Adobe Reader renders documents at full resolution without any blurring. Regarding the citations, we have removed duplicate entries and completed all bibliographic information. Additionally, the abbreviations have been reviewed and corrected where necessary.

 

Reviewer 2 Report

Comments and Suggestions for Authors

Thanks for authors' revision, I have no further comments.

Author Response

Response to all comments and Issues

We express our gratitude to all reviewers for their comments and concerns, which have been provided with the aim of improving this document. In response to all comments, we have provided a detailed reply for each one, explicitly indicating the corrections made.

Thank you for your valuable feedback.

In response to the concerns raised by the reviewers, the majority of which pertained to the style and formatting of the document, we elected not to highlight the modified sections, as most of the text was altered to enhance clarity.

Reviewer 3 Report

Comments and Suggestions for Authors

Thank the authors for addressing all the comments for improvement. I am satisfied with the revised manuscript. The authors should do a thorough proofreading of the entire manuscript before publication to avoid unnecessary wording and grammatical issues. 

Author Response

Response to all comments and Issues

We express our gratitude to all reviewers for their comments and concerns, which have been provided with the aim of improving this document. In response to all comments, we have provided a detailed reply for each one, explicitly indicating the corrections made.

Thank you for your valuable feedback.

In response to the concerns raised by the reviewers, the majority of which pertained to the style and formatting of the document, we elected not to highlight the modified sections, as most of the text was altered to enhance clarity.

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

The author has made revisions item by item according to the comments and suggestions of the reviewers, so I personally believe that the manuscript has met the requirements of the submission target journal.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.