Evaluation of influencing factors of China university teaching quality based on fuzzy logic and deep learning technology

Jie Yu

doi:10.1371/journal.pone.0303613

Peer Review History

Original SubmissionNovember 22, 2023
12 Jan 2024 Decision Letter - Le Hoang Son, Editor PONE-D-23-38898Evaluation of Influencing Factors of University Teaching Quality Based on Fuzzy Logic and Deep Learning TechnologyPLOS ONE Dear Dr. Yu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Feb 26 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: No Reviewer #3: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The abstract should be revised as it does not introduce the area of research and the research question. The introduction should be rewritten professionally. Please explain the proposed method in more detail, what is the novelty of the proposed method compared to the state of the art? Current experiments are weak and a fair comparison with other recent methods is very necessary. I think it would be good to represent the experiment results in the abstract. The experiment description section is too rough. A description of data collection should be added. For the experimental results, it will be good to present a statistical test the comparison the results with other published methods. This can help support the claim of improved results obtained with the studied selection methods. Another aspect where the paper can be improved is motivation and the reason for the given architecture. The current approach seems to be more like we have these different types of architectures, let's mix them and present results by training them. It would be of great interest to why a particular model was selected and what part of the framework is helping us learn. English can be improved. Proofreading should ensure the appropriate use of grammar, tense, and punctuation. Longer sentences should be converted into smaller ones. Reviewer #2: This study assesses the influencing factors of college teaching quality in China. The manuscript is well written and analyses seem robust. My major concern is the data collection, more details are needed regarding how the survey responses are collected, such as from where, whom, information about students and teachers’ background (major, what courses etc). Specific comments: I highly recommend the author including line number and page number for future manuscripts, this will allow reviewers to refer to comments more easily. Title: I recommend including ‘China’ in the title if that is where the data are collected. Abstract: should include more information on data, such as sample number, major results and implications. Abstract: ‘approaches must be used …’ – change ‘must’ to ‘could’ Keywords: should use words that are not in the title Introduction: - ‘by numerous variables’: add references and explain why these variables are important - ‘requirements can be encouraged’: replace ‘can be’ by ‘are’ - ‘Recognizes the components xxx’: this doesn’t seem to be a complete sentence - ‘NN approaches’: specify what it is - ‘FRs can effectively assess xxx’: add references - ‘An influence factors analysis model xxx’: explain the performance of such model - ‘teaching wushu’: what is wushu? - Add references for ‘Resources for teaching wushu xxx’ Section 2 Related work has summarized too much literature, but not explaining why they are relevant to this work. If some don’t use the same method, I suggest removing them. Figure 6: figure legend overlaps with some bars Reviewer #3: This paper develops a powerful approach for assessing the influencing factors of college and university teaching effects by implementing the Sequential Intuitionistic Fuzzy (SIF) assisted Long Short-Term Memory (LSTM) model. The enhancement of the SIF-LSTM model for assessing the influencing factors of teaching quality is proved by the accuracy of 98.4%, Mean Square Error (MSE) of 0.028%, Tucker Lewis Index (TLI) measure for all influencing factors and entropy measure of non-membership and membership degree correlation of factors related to quality in teaching by various dimensional measures. The effectiveness of the proposed model is validated by implementing data sources with a set of 60+ teachers' and students' open-ended questionnaire surveys from a university. However, there are some comments. 1- You must add qualitative comparison between your proposed scheme and some of existing methods based some metrices such as complexity time, methodology, preprocessing, and others. 2- In figure 3, the three boxes of membership degree, non-membership degree and hesitation degree do not connect any other box in the flowchart, I think there is something wrong. 3- the resolution of some figures needs some enhancements ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Dr B Santhosh Kumar Reviewer #2: No Reviewer #3: Yes: Ahmed A. A. Gad-Elrab ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0303613.r001
Revision 1
18 Feb 2024 Author Response Reviewer #1: 1. The abstract should be revised as it does not introduce the area of research and the research question. Ans: Abstract is rewritten accordingly. 2. The introduction should be rewritten professionally. Ans: Introduction section rewritten accordingly. 3. Please explain the proposed method in more detail, what is the novelty of the proposed method compared to the state of the art? Ans: The main intention of this study is to improve the teaching quality using SIF-LSTM method. A DL model for gathering and analyzing factors influencing the enhancement of college teachers' academic and technical capabilities was proposed to raise the innovation of academic achievement in colleges and universities and assist teachers in improving their knowledge related to academic growth. DL can also be used to identify the most effective teaching methods for specific subjects or topics. By analyzing large datasets of student performance data in different subject areas, deep Algorithms for learning can spot relationships and trends that human experts may not immediately recognize. The excellence of the system is compared with existing methods such as AVGA-FBPNN, DL-ELSVM, CFNN, and DA-BP. 4. Current experiments are weak and a fair comparison with other recent methods is very necessary. Ans: The system is compared with existing methods AVGA-FBPNN, DL-ELSVM, CFNN, and DA-BP. During the comparison, qualitative and quantitative metrics are utilized to make the comparison. 5. I think it would be good to represent the experiment results in the abstract. Ans: Thanks for the positive response 6. The experiment description section is too rough. Ans: Experimental discussions are fine-tuned accordingly. 7. A description of data collection should be added. Ans: An open dataset of 65 university students encompasses an institute's social sciences, the humanities, and science and engineering, and 62 faculty members with various university levels and types, professional positions, teaching demographics, and designations participated in a questionnaire with an open-ended poll [26]. The initial influential factors of workload involvement, competence, energy investment, and teaching emotional involvement make up most of faculty members' teaching contributions. The suggested four-dimensional teaching involvement model was done for a preliminary test on 342 faculty members. Confirmatory component analysis revealed that the data assessment model fit the test's 293 faculty participants well. 8. For the experimental results, it will be good to present a statistical test the comparison the results with other published methods. Ans: statistical comparison is given according to your comment. Table 4 and 5 is utilized for this comparison. 9. This can help support the claim of improved results obtained with the studied selection methods. Ans: The results are improved accordingly and existing methods are utilized for comparison AVGA-FBPNN, DL-ELSVM, CFNN, and DA-BP. 10. Another aspect where the paper can be improved is motivation and the reason for the given architecture. Ans: The teacher's communication ability, technical aspect, size of the learning environment, the number of students, the teaching assets accessible to teachers and students, and the degree of student engagement are possible basic factors. These factors can be described as a fuzzy set that accurately conveys how much of an impact they have on the calibre of teaching. The main intension of this study is to improve the teaching quality according to various factors. 11. The current approach seems to be more like we have these different types of architectures, let's mix them and present results by training them. Ans: Here, LSTM utilized to train the data. According to the influencing factors information is trained that helps to improve the teaching quality. Therefore, present study is compared with the existing approaches to justify the proposed system efficiency. Here, existing system concept is taken how the method working on present study is analyzed to examining the system performance. it's important to carefully design the ensemble, select appropriate architectures, and consider the integration of fuzzy logic and deep learning in a meaningful way. Additionally, the evaluation and interpretation of results should be thorough, considering the strengths and weaknesses of each architecture in contributing to the overall assessment of teaching quality. 12. It would be of great interest to why a particular model was selected and what part of the framework is helping us learn. Ans: the selection of the model and the components of the framework work synergistically to ensure that the analysis is tailored to the unique challenges and goals of evaluating university teaching quality based on fuzzy logic and deep learning technology. Each step in the framework contributes to a deeper understanding of the factors influencing teaching quality, ultimately leading to informed decision-making and improvements in educational practices. 13. English can be improved. Proofreading should ensure the appropriate use of grammar, tense, and punctuation. Ans: Document proofreaded. 14. Longer sentences should be converted into smaller ones. Ans: Checked longer sentences are changed into smaller ones. Reviewer #2: This study assesses the influencing factors of college teaching quality in China. The manuscript is well written and analyses seem robust. My major concern is the data collection, more details are needed regarding how the survey responses are collected, such as from where, whom, information about students and teachers’ background (major, what courses etc.). Specific comments: I highly recommend the author including line number and page number for future manuscripts, this will allow reviewers to refer to comments more easily. 1. Title: I recommend including ‘China’ in the title if that is where the data are collected. Ans: Evaluation of Influencing Factors of China University Teaching Quality Based on Fuzzy Logic and Deep Learning Technology 2. Abstract: should include more information on data, such as sample number, major results and implications. Ans: The enhancement of the SIF-LSTM model for assessing the influencing factors of teaching quality is proved by the accuracy of 98.4%, Mean Square Error (MSE) of 0.028%, Tucker Lewis Index (TLI) measure for all influencing factors and entropy measure of non-membership and membership degree correlation of factors related to quality in teaching by various dimensional measures. 3. Abstract: ‘approaches must be used …’ – change ‘must’ to ‘could’ Ans: Fuzzy and Deep Learning (DL) approaches must be could to build an assessment model that can precisely measure the teaching qualities to enhance accuracy 4. Keywords: should use words that are not in the title Ans: Keywords: Teaching quality, sequential intuitionistic fuzzy, predictive model, Intuitionistic fuzzy long short-term memory; membership degree; Influencing factors; 5. Introduction: - ‘by numerous variables’: add references and explain why these variables are important Ans: Assessment of teaching quality is a challenging, unpredictable system issue influenced by numerous variables such as class size, learning environment, teacher-student relationship, learning outcomes etc. - ‘requirements can be encouraged’: replace ‘can be’ by ‘are’ Ans: Universities with various fundamental curriculum requirements are encouraged to use the binary relative assessment approach to analyze the influencing factors that affect the efficacy of teaching quality in experiment design across every university. - ‘Recognizes the components xxx’: this doesn’t seem to be a complete sentence Ans: Recognizes the factors that influence the success of instruction in education through efficient and intelligent approaches, such as utilizing technologies like the WEKA tool for problem-solving. This is especially pertinent in the context of the changing educational environment, where Artificial Intelligence technologies are vital for increasing student learning experiences and overall educational advancement. - ‘NN approaches’: specify what it is Ans: Neural Networks (NN) approaches. - ‘FRs can effectively assess xxx’: add references Ans: Reference included. - ‘An influence factors analysis model xxx’: explain the performance of such model Ans: The creation of an influence factors analysis model is frequently an iterative procedure, where the model is continuously improved as further data is obtained or as the comprehension of the phenomena grows more profound. The objective is to develop an effective tool for thoroughly analyzing the numerous aspects that contribute to the observed results. - ‘teaching wushu’: what is wushu? Ans: Changed. - Add references for ‘Resources for teaching wushu xxx’ Ans: Reference 10 utilized for this sentence. 6. Section 2 Related work has summarized too much literature, but not explaining why they are relevant to this work. If some don’t use the same method, I suggest removing them. Ans: few literature survey is removed according to your comment. 7. Figure 6: figure legend overlaps with some bars Ans: Figure 6 changed accordingly. Reviewer #3: This paper develops a powerful approach for assessing the influencing factors of college and university teaching effects by implementing the Sequential Intuitionistic Fuzzy (SIF) assisted Long Short-Term Memory (LSTM) model. The enhancement of the SIF-LSTM model for assessing the influencing factors of teaching quality is proved by the accuracy of 98.4%, Mean Square Error (MSE) of 0.028%, Tucker Lewis Index (TLI) measure for all influencing factors and entropy measure of non-membership and membership degree correlation of factors related to quality in teaching by various dimensional measures. The effectiveness of the proposed model is validated by implementing data sources with a set of 60+ teachers' and students' open-ended questionnaire surveys from a university. However, there are some comments. 1- You must add qualitative comparison between your proposed scheme and some of existing methods based some metrices such as complexity time, methodology, preprocessing, and others. Ans: In addition, SIF-LSTM system efficiency is further evaluated using qualitative metric such as time investment for preparation and time efficiency for assessment. The obtained result is shown in Table 5 and the results are compared with existing methods such as AVGA-FBPNN, DL-ELSVM, CFNN, and DA-BP. Table 5: Qualitative Analysis of the proposed SIF-LSTM Parameter AVGA-FBPNN DL-ELSVM CFNN DA-BP Proposed SIF-LSTM Time investment for preparation 78 hrs 72hrs 67hr 54hr 36hr Time efficiency for assessment 64hr 60hr 58hr 50hr 35hr From the table 5, it clearly states the proposed SIF-LSTM approach attains minimum preparation and assessment time for improving the teaching quality. The set of training samples and patterns are more useful to understand the concept that leads to minimize the time consumption. However, the system consumes high teaching quality and efficiency compared to other methods. 2- In figure 3, the three boxes of membership degree, non-membership degree and hesitation degree do not connect any other box in the flowchart, I think there is something wrong. Ans: Three boxes are related to assessment qualities. According to these three factors, first and second assessment quality is checked and the obtained results are fed into the fuzzy scoring factor computations. 3- the resolution of some figures needs some enhancements Ans: Figure resolutions are checked and changed accordingly. Attachments Attachment Submitted filename: Response to reviewers.docx https://doi.org/10.1371/journal.pone.0303613.r002
22 Mar 2024 Decision Letter - Le Hoang Son, Editor PONE-D-23-38898R1Evaluation of Influencing Factors of China University Teaching Quality Based on Fuzzy Logic and Deep Learning TechnologyPLOS ONE Dear Dr. Yu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by May 06 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Le Hoang Son, Ph.D Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: No Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The whole article is properly written understandably. Moreover, this article sounds well with various aspects in this research area and the involvement of this work is appreciable. Reviewer #2: (No Response) Reviewer #3: Thanks all done. the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication.. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Dr B Santhosh Kumar Reviewer #2: No Reviewer #3: Yes: Ahmed A. A. Gad-Elrab ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0303613.r003
Revision 2
27 Mar 2024 Author Response Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Thank you for your comment. I have replaced the retracted articles in the references section. Please check for further review. Review Comments to the Author Reviewer #1: The whole article is properly written understandably. Moreover, this article sounds well with various aspects in this research area and the involvement of this work is appreciable. Thank you for your comment. Reviewer #2: (No Response) Reviewer #3: Thanks all done. the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication. Thank you for your comment. Attachments Attachment Submitted filename: Response to reviewers.docx https://doi.org/10.1371/journal.pone.0303613.r004
29 Apr 2024 Decision Letter - Le Hoang Son, Editor Evaluation of Influencing Factors of China University Teaching Quality Based on Fuzzy Logic and Deep Learning Technology PONE-D-23-38898R2 Dear Dr. Yu, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Le Hoang Son, Ph.D Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #2: All comments have been addressed Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes Reviewer #3: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #2: No Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #2: Yes Reviewer #3: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #2: (No Response) Reviewer #3: Thanks all done. the authors have adequately addressed your comments raised in a previous round of review and you feel that this لاmanuscript is now acceptable for publication. ******** https://doi.org/10.1371/journal.pone.0303613.r005
Formally Accepted
9 Jul 2024 Acceptance Letter - Le Hoang Son, Editor PONE-D-23-38898R2 PLOS ONE Dear Dr. Yu, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Prof. Le Hoang Son Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0303613.r006

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