RETRACTED ARTICLE: Male Separatism: Discourse, Ideology, and Argumentation

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Min Xu & Li Tang (2022) Safety Collection Algorithm of Big Data for Blockchain-Based Power Grid Systems, IETE Journal of Research, DOI: 10.1080/03772063.2022.2028585 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Kusum Yadav, Mnaouer Kachout, Yasser Alharbi, Abdulrahman Alreshidi, Anurag Jain, Eissa Jaber Alreshidi, Md Haidar Sharif, Bassam Wasfi Aboshosha & Shoayee Dlaim Alotaibi (2022) Securing Diagnosed Parkinson's Disease Data by Blockchain Technology using 2E2C Algorithm, IETE Journal of Research, DOI: 10.1080/03772063.2022.2063196 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Yuntao Zou & Jiang Li (2022) Sequential Pattern Data Mining Algorithm and Blockchain Technology for Preparing a Housing Price Prediction Model, IETE Journal of Research, DOI: 10.1080/03772063.2022.2038286 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Yan Liu, Yali Gu, Di Yang & Jingmin Wang (2022) Fault Identification and Relay Protection of Hybrid Microgrid Using Blockchain and Machine Learning, IETE Journal of Research, DOI: 10.1080/03772063.2022.2050307 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Kusum Yadav, Anurag Jain, Nada Mohamed Osman Sid Ahmed, Sawsan Ali Saad Hamad, Gaurav Dhiman & Shoayee Dlaim Alotaibi (2022) Internet of Thing based Koch Fractal Curve Fractal Antennas for Wireless Applications, IETE Journal of Research, doi:10.1080/03772063.2022.2058631 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

Chapter one sets the scene for this pivot publication. In addition to giving an outline of the monograph’s research objectives and presenting an overview of past research on Wikipedia, this chapter details the theoretical framework motivating the holistic approach to Wikipedia taken in this study. I discuss why it is important to know about Wikipedia’s modus operandi, technological affordances, policies and general site characteristics as well as its position in society and its potential societal functions. In this context, I draw on Herring’s research in Computer-Mediated Discourse Analysis (CMDA) and complement this with a discussion of developments in (Critical) Discourse Studies to underscore the importance of contextualisation and theorisation when examining digital discourse. Finally, this chapter gives a brief outline of the remaining chapters.KeywordsWikipedia(Critical) discourse studies (CDS)Computer-mediated discourse analysis (CMDA)Social media critical discourse analysis (SM-CDS)Contextualisation

FigureThe concept of peer review can be interpreted very differently among nurses and healthcare professionals. Some think of a subject expert reviewing a manuscript for a journal's editorial staff. Others think of regulatory bodies requiring hospitals to have an internal process to ensure that healthcare team members are competent and able to perform within their scope of practice.1 Still others think of peer review as a quality assurance process in which healthcare team members audit each other's documentation to validate care standardization. We provide insight into a nursing peer review process designed to evaluate performance, and the journey to its implementation. A well-defined peer review process and tool, utilized in conjunction with a nurse's annual performance evaluation, is one way to infuse meaningful peer input into a performance appraisal. This system allows nurses to provide insight into one another's strengths and opportunities for growth. A detailed approach was used to create, develop, and sustain a nursing peer review program that's flexible enough to be used by all staff members within a pediatric hospital system. In addition to promoting professional growth among nursing staff, this process also meets the current peer review standards set by the Magnet Recognition Program® and The Joint Commission. The ultimate goal of sharing this information is to help other organizations that are just beginning the peer review process and those that have struggled in the past with development and implementation to bring about a sustainable change. This, in turn, will promote nursing cohesiveness and professionalism as we work together to bring healthcare into a new era. Organizational standardization Our organization embarked on an initiative to create a standardized peer review process that would be utilized by all nursing departments. The process needed to be integrated organizationally and applicable to clinical nurses within both inpatient and outpatient environments. This journey began with a review of the current literature and an examination of the current peer review practice at other Magnet® facilities. Through this process, it was discovered that the majority of these facilities utilized and defined peer review in a variety of ways. We identified variation in the management of the peer review process within our own organization. Acknowledging the vast array of discrepancies within and external to the organization, a task force of clinical nurses was developed to redefine the way our organization administered the clinical nurse peer review process. This task force also included two leadership liaisons who served as resources to the clinical nurses during this development and provided insight into the management side of the peer review process. There were four steps in our process: (1) defining a peer, (2) developing a peer review form, (3) transforming the process, and (4) implementing the process. Who's a peer? The first step in our work involved evaluating, discussing, and reaching a consensus on the definition of peer. In order to standardize the peer review process, the task force recognized a true definition was needed to measure success with the new process. Looking at previous peer review practice in the organization, many nurses and department directors had different definitions and ideas of what it meant to be a peer. Some departments included other disciplines in a nurse's specialty for evaluations, whereas others utilized only fellow nursing staff members. The task force agreed that the purpose of peer review is to foster professional growth and development among staff members by utilizing a process through which measurable outcomes are assessed. After much dialog, the task force adopted the definition of peer utilized by the American Nurses Association, which defines a peer as an individual of the same rank or standing according to the established standards of practice.2 Form development The next step in redefining the peer review process was to develop a new peer review tool that could be transferrable and applicable among the various nursing specialties and departments. Key areas identified by the task force for development of this tool included creating a short, concise form that's easy to understand with limited directions and applicable to all clinical nursing departments. To achieve this goal, the task force worked collaboratively to establish eight domains that would provide nurses with a peer evaluation framework. These domains were established through open dialog, including question-and-answer sessions, review of current job descriptions, and evaluation of other tools utilized by various Magnet facilities. Through this work, a common set of core expectations were identified and utilized in the development of each domain. These domains encompass the essential clinical nurse job functions, behavioral competencies, and basic roles and responsibilities throughout the organization. The domains can be found in Table 1.Table 1: The eight domains of peer evaluationTo further guide and support high-quality peer feedback, three to five specific, measurable objectives were created and listed under each domain for nurses to measure performance. These objectives were developed to guide peers in evaluating each nurse by providing focused, pertinent feedback. The task force wanted to eliminate vague, nonspecific feedback that didn't facilitate the identification of future growth opportunities. The objectives created by the task force assist peers in identifying evidence from the nurse's daily work, communication, time management, and interdisciplinary interactions. Each domain also included a comment field, allowing nurses the autonomy to provide open-ended feedback and elaborate on outstanding work or opportunities for the employee's growth and improvement. The comment section was extremely important in the solicitation of meaningful feedback because it allowed nurses to provide examples to reinforce the ratings selected for the objectives. The directions on the new tool clearly state that comments are mandatory for certain ratings to allow for elaboration and examples. The task force believed a Likert rating scale was essential to standardize the peer review process. The previous process utilized a vague, numerical score that hadn't historically provided nurses with adequate descriptions of their work ethic and performance. Lower scores were considered negative responses, whereas higher scores equated to positive responses. After review of the current literature, the task force employed a 4-point Likert scale comprising "not met," "approaching," "meets expectations," and "exceeds expectations." In addition to the scaled questions, an open-ended question "Do you feel comfortable working with this nurse?" was added to the form. Utilizing a mixed-method methodology, Likert scale, and open-ended question format allowed the evaluating nurse a better opportunity to provide real-life contextual examples related to the evaluated nurse's care.3 Process transformation After the peer evaluation tool was created, the task force focused its attention on creating a framework to aid departments in implementing the new peer review process. Throughout our hospital, there are units of vastly different sizes. Some nursing departments have four to six clinical nurses, whereas other departments have as many as 150 to 200 clinical nurses on staff. The variability in staff sizes meant that the task force had to be creative in determining how many peer reviewers should evaluate each nurse annually and how these reviewers should be selected. With the new process, each nurse receives feedback from two to four RNs. Limiting the number of peer evaluations eliminated the previous dissatisfaction and/or barrier of evaluators being asked to fill out 20 to 30 peer evaluations a month due to exceedingly large nursing departments. The task force also allowed clinical nurses to select one to two peers of their choice to provide feedback; the management team selected the remaining peers. Implementation After the task force completed the new peer review tool and process recommendations, the nursing department directors and the CNO gave the approval for implementation. The standardized peer review form, along with the revised process, was implemented using various educational modalities. The first presentation was provided to the inpatient and outpatient nursing directors to inform them about the new form and the revised process. The directors were provided with handouts outlining the changes and educational fact sheets for the staff members to use as a reference. The task force increased its availability to ensure educational consistency to all clinical nurses by attending and presenting at unit-based councils, charge nurse meetings, and department-wide staff development programs. The feedback received from the different educational presentations was extremely positive. Feedback discussed the tool's ease of use, applicability to all nursing departments, appropriate form length, and measurable objectives that allowed nurses to comment on focused job roles and responsibilities. Other feedback included the improved functionality of the new rating scale and the process change that limited the number of requests for peer evaluations. Some directors expressed resistance to changing their current peer review practice. Certain directors thought feedback from four nurses wasn't enough if the department had a high number of nursing staff members. After an open discussion with the task force chairperson and the directors of large departments, it was agreed that meaningful feedback from four people would be adequate. Other directors mentioned that they liked the idea of adding specific clinical skills for peer evaluation. However, we couldn't add specific skills because they wouldn't universally apply to the various nursing department specialties. The task force collected all comments and feedback, and created a frequently asked questions document to address these concerns and explain the thought process behind the decisions made about the tool and recommendations. Directors were then provided with answers to and rationales for their specific questions and concerns, creating a consistent message and clarity to all departments. All education and implementation occurred over the course of 4 months. During this time, the task force worked with web development to formulate an electronic version of the peer evaluation document for the purpose of online submission. The electronic document was widely popular because it eliminated the use of paper and facilitated tool access for all clinical nurses. Staff members were able to complete the tool online and submit the evaluation through the organization's intranet directly to the person who requested the feedback. The task force also concluded that in order for this new tool and process to remain functional and meaningful, continued evaluation of its use would be essential. Reaching the top As we diligently work to move the nursing profession forward, it's important to remember that peer review can positively impact not only an individual's nursing practice, but also an entire hospital system. Nurses are at the forefront of healthcare transformation and are integral to sustainable practice improvement. By empowering clinical nurses to lead this initiative, we've been able to successfully introduce, develop, and support a valuable peer review process across our organization. Utilizing a comprehensive peer review tool can help organizations improve patient outcomes and patient satisfaction.

Information for authors and editorial policies

Applying cognitive pragmatics to Critical Discourse Studies: A proximization analysis of three public space discourses

In this article I present a framework for critical discourse studies on mental health. By relying on principles belonging to Critical Discourse Studies (Flowerdew & Richardson, 2018; van Leeuwen, 2008; 2007), and the Sociology of Health (Caponi, 2014; Martinez-Hernaez, 2014; Mitjavila, 2015; Rose & Abi-Rached, 2014), at first I introduce a brief contextualization of Critical Discourse Studies as a field of research and establish connections between this field and mental health studies. In the sequence, I present a framework divided in 8 steps focused on the investigation of social practices involving mental health. These steps are then applied to the analysis of one appellate decision produced by the Superior Court of Justice in Brazil involving the diagnosis of Attention Deficit and Hyperactivity Disorder. The analysis reveals ADHD has a neuropolitical function and that authority related to the diagnosis is vested in medical expertise only, despite the lack of biological markers for it.

One of the more recent developments on the Critical Discourse Studies (CDS) landscape lies in critical applications of Cognitive Linguistics (e.g. Chilton 2004; Koller 2004; Hart 2010, 2011a, 2014a; Hart and Lukes 2007). Critical Linguistic CDS (CL-CDS) is characterised by a shift in focus to the interpretation-stage of analysis (O’Halloran 2003; Hart 2010). That is, CL-CDS addresses the cognitive-semiotic processes involved in understanding discourse and the fundamental role that these processes play in the construction of knowledge and the legitimation of action. Cognitive Linguistic approaches to CDS thus typically present detailed semantic analyses of language usages. In particular, CL-CDS emphasises the conceptual nature of meaning construction and is concerned with modelling the conceptual structures and processes which, invoked by text in the course of discourse, constitute an ideologised understanding of the situations and events being described. Cognitive Linguistics itself is not a specific theory but a paradigm within linguistics comprised of several related theories. Accordingly, Cognitive Linguistics makes available to CDS a set of alternative ‘tools’ as different theories may be operationalised as methodologies in critical analyses of discourse. Theories in Cognitive Linguistics, however, share a common set of assumptions about the nature of language. These assumptions are naturally shared by Cognitive Linguistic studies in CDS and thus provide the common thread and theoretical backdrop that defines a more general Cognitive Linguistic school of CDS (cf. Hart 2011b, 2015b). In this chapter, then, I begin, in Section 2, by introducing the Cognitive Linguistic perspective, reviewing the common aims and commitments of Cognitive Linguistic approaches. In section 3, I introduce some of the methods employed in Cog Linguistic approaches. And finally in Section 4, I provide an example analysis using data sourced from three online newspaper articles reporting on the 2014 Million Mask March in London.

Statement of Retraction We, the Editor-in-Chief and Publisher of International Journal of Healthcare Management, have retracted 8 articles that were published in the Special Issue titled “Safety & Security of Medical Health Data for IoT-based healthcare systems”. Following publication, the Publisher identified concerns regarding the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editor-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. The full list of retracted articles is detailed below: • Saturi, S., & Banda, S. (2022). Modelling of deep learning enabled lung disease detection and classification on chest X-ray images. International Journal of Healthcare Management, 1–12. https://doi.org/10.1080/20479700.2022.2102223 • Karanam, S. R., Srinivas, Y., & Chakravarty, S. (2022). A systematic approach to diagnosis and categorization of bone fractures in X-Ray imagery. International Journal of Healthcare Management, 1–12. https://doi.org/10.1080/20479700.2022.2097765 • A, T., Saju, R., John, A., & C, U. A. (2022). DMSENet: Deep multi-modal squeeze and excitation network for the diagnosis of Alzheimer's disease. International Journal of Healthcare Management, 1–13. https://doi.org/10.1080/20479700.2022.2130631 • Praveen, S. P., Murali Krishna, T. B., Anuradha, C. H., Mandalapu, S. R., Sarala, P., & Sindhura, S. (2022). A robust framework for handling health care information based on machine learning and big data engineering techniques. International Journal of Healthcare Management, 1–18. https://doi.org/10.1080/20479700.2022.2157071 • Bhan, A., Mangipudi, P. S., & Goyal, A. (2023). Left atrium MRI image segmentation using efficient Xception stochastic depth based generative adversarial network. International Journal of Healthcare Management, 1–12. https://doi.org/10.1080/20479700.2023.2166206 • Sonia, S. V. E., Nedunchezhian, R., Ramakrishnan, S., & Kannammal, K. E. (2023). An empirical evaluation of benchmark machine learning classifiers for risk prediction of cardiovascular disease in diabetic males. International Journal of Healthcare Management, 1–16. https://doi.org/10.1080/20479700.2023.2170006 • Haulath, K., & Mohamed Basheer, K. P. (2023). TT self-weighted Deep-AD3-Net: An AD stage and risk prediction. International Journal of Healthcare Management, 1–13. https://doi.org/10.1080/20479700.2023.2175414 • Karanam, S. R., Srinivas, Y., & Chakravarty, S. (2023). A statistical model approach based on the Gaussian Mixture Model for the diagnosis and classification of bone fractures. International Journal of Healthcare Management, 1–12. https://doi.org/10.1080/20479700.2022.2161146

Statement of Retraction We, the Editor-in-Chief and Publisher of International Journal of Healthcare Management, have retracted 8 articles that were published in the Special Issue titled “Safety & Security of Medical Health Data for IoT-based healthcare systems”. Following publication, the Publisher identified concerns regarding the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editor-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. The full list of retracted articles is detailed below: • Saturi, S., & Banda, S. (2022). Modelling of deep learning enabled lung disease detection and classification on chest X-ray images. International Journal of Healthcare Management, 1–12. https://doi.org/10.1080/20479700.2022.2102223 • Karanam, S. R., Srinivas, Y., & Chakravarty, S. (2022). A systematic approach to diagnosis and categorization of bone fractures in X-Ray imagery. International Journal of Healthcare Management, 1–12. https://doi.org/10.1080/20479700.2022.2097765 • A, T., Saju, R., John, A., & C, U. A. (2022). DMSENet: Deep multi-modal squeeze and excitation network for the diagnosis of Alzheimer's disease. International Journal of Healthcare Management, 1–13. https://doi.org/10.1080/20479700.2022.2130631 • Praveen, S. P., Murali Krishna, T. B., Anuradha, C. H., Mandalapu, S. R., Sarala, P., & Sindhura, S. (2022). A robust framework for handling health care information based on machine learning and big data engineering techniques. International Journal of Healthcare Management, 1–18. https://doi.org/10.1080/20479700.2022.2157071 • Bhan, A., Mangipudi, P. S., & Goyal, A. (2023). Left atrium MRI image segmentation using efficient Xception stochastic depth based generative adversarial network. International Journal of Healthcare Management, 1–12. https://doi.org/10.1080/20479700.2023.2166206 • Sonia, S. V. E., Nedunchezhian, R., Ramakrishnan, S., & Kannammal, K. E. (2023). An empirical evaluation of benchmark machine learning classifiers for risk prediction of cardiovascular disease in diabetic males. International Journal of Healthcare Management, 1–16. https://doi.org/10.1080/20479700.2023.2170006 • Haulath, K., & Mohamed Basheer, K. P. (2023). TT self-weighted Deep-AD3-Net: An AD stage and risk prediction. International Journal of Healthcare Management, 1–13. https://doi.org/10.1080/20479700.2023.2175414 • Karanam, S. R., Srinivas, Y., & Chakravarty, S. (2023). A statistical model approach based on the Gaussian Mixture Model for the diagnosis and classification of bone fractures. International Journal of Healthcare Management, 1–12. https://doi.org/10.1080/20479700.2022.2161146

Statement of Retraction We, the Editor-in-Chief and Publisher of Bioengineered (the “Journal”), have retracted the following articles which were published in the Special Issue titled “Algal Bioprocess Engineering”, guest edited by Pau Loke Show, Kit Wayne Chew & Jo-Shu Chang, Bioengineered, 11(S12), originally published in 10(01) and 11(01). Show, P. L., Chew, K. W., & Chang, J. S. (2020). Special issue on algae bioprocess engineering. Bioengineered, 11(SI2), 188. https://doi.org/10.1080/21655979.2020.1729546 Koyande, A. K., Show, P. L., Guo, R., Tang, B., Ogino, C., & Chang, J. S. (2019). Bio-processing of algal bio-refinery: a review on current advances and future perspectives. Bioengineered, 11(SI2), 574–592. https://doi.org/10.1080/21655979.2019.1679697 Ghanbariasad, A., Taghizadeh, S. M., Show, P. L., Nomanbhay, S., Berenjian, A., Ghasemi, Y., & Ebrahiminezhad, A. (2019). Controlled synthesis of iron oxyhydroxide (FeOOH) nanoparticles using secretory compounds from Chlorella vulgaris microalgae. Bioengineered, 11(SI2), 390–396. https://doi.org/10.1080/21655979.2019.1661692 Cheah, W. Y., Show, P. L., Yap, Y. J., Mohd Zaid, H. F., Lam, M. K., Lim, J. W., … Tao, Y. (2019). Enhancing microalga Chlorella sorokiniana CY-1 biomass and lipid production in palm oil mill effluent (POME) using novel-designed photobioreactor. Bioengineered, 11(SI2), 61–69. https://doi.org/10.1080/21655979.2019.1704536 Tan, J. S., Lee, S. Y., Chew, K. W., Lam, M. K., Lim, J. W., Ho, S. H., & Show, P. L. (2020). A review on microalgae cultivation and harvesting, and their biomass extraction processing using ionic liquids. Bioengineered, 11(SI2), 116–129. https://doi.org/10.1080/21655979.2020.1711626 Taghizadeh, S. M., Berenjian, A., Chew, K. W., Show, P. L., Mohd Zaid, H. F., Ramezani, H., … Ebrahiminezhad, A. (2020). Impact of magnetic immobilization on the cell physiology of green unicellular algae Chlorella vulgaris. Bioengineered, 11(SI2), 141–153. https://doi.org/10.1080/21655979.2020.1718477 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor-in-Chief and Publisher have decided to retract all the articles within the above-named Special Issue. The Editor-in-Chief and the Publisher have not confirmed if all the authors were aware of the concerns with the peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner