Three-phase Methodology Research Articles

Working Toward Advanced Architectural Education: Developing an AI-Based Model to Improve Emotional Intelligence in Education

This study explored the integration of emotional intelligence (EI) with artificial intelligence (AI) to address emerging challenges in architectural education. An AI-supported teaching model was developed, utilizing AI tools to assess students’ emotional responses and enabling educators to adapt teaching strategies based on emotional data. This study employed a three-phase methodology: theoretical, analytical, and experimental phases. The theoretical phase involved a comprehensive literature review focusing on the role of EI in architectural education. In the analytical phase, a survey was conducted to evaluate students’ ability to overcome learning challenges using a case study from an Egyptian university. The experimental phase implemented an EI-driven teaching approach with a pilot group of students, incorporating instructor feedback and ChatGPT-4O for assessments in order to minimize potential bias. The results demonstrate that integrating EI into education significantly enhances students’ performance compared to traditional teaching methods. Furthermore, the findings contribute to the development of an AI-based model that provides personalized feedback and fosters a dynamic learning environment, aiming to achieve higher academic and behavioral standards among architecture students. This research offers theoretical and practical insights into advancing the integration of AI and EI in architectural education.

NEXUS OF WIREFRAMES, 3D MODEL, AND SIMULATION FOR THE DEVELOPMENT OF AN INTELLIGENT WASTE MANAGEMENT SYSTEM

The exponential growth in urban population has intensified the challenges of municipal waste management, necessitating innovative technological solutions. This research presents an integrated approach to developing a smart waste management system by integrating wireframes, 3D modeling, and simulation technologies. The study employs a three-phase methodology: utilizing wireframe prototyping for user interface design, enabling iterative development based on stakeholder feedback, implementing detailed 3D modeling of smart waste bins to visualize and optimize waste flow dynamics, and conducting comprehensive simulations to evaluate system performance under various scenarios. The simulation analysis compared multiple routing algorithms while monitoring real-time bin capacity through virtual sensors. Results demonstrate a 25% improvement in collection efficiency using dynamic routing compared to traditional fixed routes and a 30% reduction in overflow incidents through predictive capacity monitoring. The user-centered design approach, validated through wireframe testing with 50 participants, showed a 40% increase in system engagement compared to conventional waste management interfaces. Additionally, the 3D modeling phase identified critical design modifications that improved bin accessibility by 35% and reduced maintenance requirements by 20%. This research contributes to the growing knowledge of smart city solutions while providing practical insights for municipalities seeking to modernize their waste management. infrastructure. The proposed system offers a scalable, efficient approach to addressing urban waste challenges while promoting environmental sustainability. A limitation of the study is its reliance on simulation-based testing, which may not fully represent real-world operational complexities. A major challenge is implementation dependency on continuous stakeholder engagement and high reliance on technological infrastructures.

Enhancing shared street accessibility in heritage sites for individuals with visual disabilities: a Canadian perspective.

Heritage sites often pose significant accessibility challenges for individuals with visual disabilities due to their preserved architectural features and strict regulations against modifications. In shared streets, designed to encourage pedestrian use and reduce vehicle dominance, these challenges are exacerbated by the lack of tactile and directional cues for visually impaired users. This study, set in the context of Canadian heritage sites, explores how shared streets can be adapted to be more inclusive while respecting the integrity of historical environments. The objective of this study is to explore and propose practical solutions to enhance the accessibility of shared streets for individuals with visual disabilities within heritage sites, with a particular focus on preservation requirements. The study adopts a three-phase methodology. First, co-design sessions were conducted with three groups of stakeholders: people with disabilities, caregivers, and experts in accessibility and heritage preservation. Second, a narrative literature review was undertaken to identify practices from existing research and urban planning cases. Finally, solutions were developed in collaboration with a design firm to create practical, adaptable prototypes that address the specific needs identified in earlier phases. The co-design sessions revealed several key challenges, including the lack of tactile references, difficulties with snow removal, and the need for clearer delineation between pedestrian and vehicular zones. Solutions such as tactile paving, modular bollards, and the use of contrasting materials were developed to address these issues. The narrative review reinforced the importance of universal design in heritage contexts and provided insights into successful implementations in other urban settings. The study concludes that shared streets can be made more accessible for individuals with visual disabilities by adopting a modular design approach that integrates tactile cues and adaptable urban furniture. These solutions ensure that accessibility and safety can coexist with heritage preservation, promoting inclusivity in public spaces. The research highlights the importance of stakeholder engagement in the design process and offers a replicable framework for improving accessibility in heritage sites globally. However, further field testing is needed to assess the feasibility and acceptance of these solutions within the regulatory constraints of heritage environments.

Development of a conceptual model on I4.0 technologies’ influence on SCR

Purpose In a dynamic and uncertain business environment, it is necessary for companies to adapt to be capable of addressing the changing circumstances and ensure business continuity. Hence, companies are employing advanced Industry 4.0 (I4.0) technologies. This study aims to explore the role of advanced I4.0 technologies in facilitating companies’ development towards supply chain resilience (SCR).Design/methodology/approach The study adopted a structured approach of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to identify 234 articles. Under PRISMA, we adopted a three-phase methodology of planning, conducting and reporting the review results. The data of these articles were synthesized using a Scopus database to investigate the relationship between I4.0 technologies and SCR.Findings The study’s findings map the technologies appropriate in different supply chain nodes and bridge the literature gap in the intersection of I4.0 technologies and SCR. The study results indicate the role of I4.0 technologies role in achieving resilience in key functions of an organization through an I4.0 technologies-enabled sourcing, manufacturing, distribution and return resilience (I-SMDRR) framework. The I-SMDRR framework also depicts the relationship being moderated by internal and external risk factors.Originality/value The study offers a unique framework by mapping sourcing, manufacturing, distribution and return resilience through I4.0 technologies. Additionally, the review delineates the theory-mapped research gaps helpful for future researchers. In summary, this systematic review of the literature identifies the components that lead to developing SCR.

Strategic Implementation of Social Support for Expatriate Management in Thailand’s Hospitality Sector

This study explores the strategic implementation of social support enablers to address expatriate turnover and the shortage of skilled labour in Thailand’s hospitality industry, particularly in the post-COVID-19 context. By integrating Social Support Theory, Cultural Intelligence Theory, and Social Capital Theory, this research identifies and prioritises key enablers essential for expatriate success. We employed a novel three-phase methodology, which included a literature review, a hybrid Best–Worst Method (BWM), Grey Relational Analysis (GRA), and semi-structured interviews with experts. The findings reveal a hierarchical framework of social support enablers, with health and psychological adjustment support, work–life balance initiatives, career development opportunities, and national support for the host country emerging as top priorities. Implementing these enablers effectively addresses expatriates’ adaptation challenges and significantly enhances their performance, well-being, and retention. This study contributes to the literature by providing the first empirically derived, prioritised approach to social support enablers implementation and practical guidance for developing holistic support systems in multicultural hospitality environments. Focusing on Thailand, the methodology and framework offer a robust foundation for future cross-cultural comparisons in expatriate management research.

The urban footprint of rural forced displacement

The rapid growth of marginal settlements in the Global South, largely fueled by the resettlement of millions of internally displaced people (IDPs), underscores the urgent need for tailored housing solutions for these vulnerable populations. However, prevailing approaches have often relied on a one-size-fits-all model, overlooking the diverse socio-spatial realities of IDP communities. Drawing on a case study in Medellin, Colombia, where a significant portion of the population consists of forced migrants, this interdisciplinary study merges concepts from human geography and urban theory with computational methods in remote sensing and exploratory spatial data analysis. By integrating socio-spatial theory with quantitative analysis, we challenge the conventional housing paradigm and propose a novel framework for addressing the housing needs of IDPs. Employing a three-phase methodology rooted in Lefebvre’s theoretical framework on the production of space, including participatory mapping, urban morphology characterization, and similarity analysis, we identify distinct patterns within urban IDP settlements and advocate for culturally sensitive housing policies. Our analysis, focusing on Colombia, the country with the largest IDP population globally, reveals the limitations of standardized approaches and highlights the importance of recognizing and accommodating socio-cultural diversity in urban planning. By contesting standardized socio-spatial practices, our research aims not only to promote equality but also to foster recognition and inclusivity within marginalized communities.

Enhancing Skin Cancer Diagnosis through the Integration of Deep Learning and Machine Learning Approaches

Skin cancer is a disease characterized by the uncontrolled proliferation of skin cells, typically manifesting as lesions or abnormal growths. Early diagnosis is critical for improving treatment outcomes. This study proposes an innovative approach to skin cancer diagnosis by integrating modern deep learning models with traditional machine learning algorithms. A three-phase methodology was developed. In the first phase, meaningful features were extracted from skin lesion images using various transfer learning models, including Xception, VGG16, ResNet152V2, InceptionV3, InceptionResNetV2, MobileNetV2, EfficientNetB2, and DenseNet201. In the second phase, dimensionality reduction was performed using Principal Component Analysis (PCA). In the final phase, the reduced feature sets were classified using K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms. Experimental results demonstrated that the highest accuracy of 91.28% was achieved through the combination of DenseNet201 for feature extraction, PCA for dimensionality reduction, and Random Forest for classification. These findings highlight the effectiveness of integrating transfer learning models, dimensionality reduction techniques, and machine learning algorithms in enhancing the accuracy of skin cancer diagnosis.

Norms of Translating Medical Terms in English Patient Information Leaflets into Kurdish

Patient Information Leaflets (PILs) are documents included in medicine packages and provide important information about the medication for patients. This study investigates the translation problems of English PILs into Sorani Kurdish. The aim of this study is to identify the translation procedures used for translating medical terminologies in PILs for lay people. It also aims to reveal which translational norms are operating in Kurdish translations of PILs. To do so, the study employs a corpus of 150 English PILs along with their Kurdish translations. To analyse the corpus, the researcher adopts Toury’s (1995-2012) three-phase methodology within the descriptive approach of translation. It also uses Vinay and Darbelnet’s (1995) translation procedures to determine how translators handled terminological rendering in the Kurdish PILs. According to the findings, nine types of translation procedures were used in translating the terminologies, with borrowing had the highest frequency, and modulation having the lowest frequency. They also revealed that Toury’s initial norms were predominantly operating in the translated PILs which labelled them as adequate translations. The translators’ choice to closely follow the original PILs was indicative of their attempt to meet the expectations of PILs’s target users, thus, revealing Chesterman’s expectancy norms therein. The study concludes that the translation of PILs into Kurdish is not regulated within the medical field and that is mainly related to the absence of an officially regulated body in the Kurdistan Region of Iraq. It also concludes that while the seemingly established norm of PIL translation into Kurdish highly relies on English borrowings, it is a significant step towards developing Kurdish medical language.

A Comparative Study of English and Translated Kurdish Business Contracts from lexical and Syntactic Perspectives

In Kurdish, legal translation as a non-literary translation has received little attention in the field of translation studies. One genre of legal translation that is specifically under-studied is business contracts. This paper analyses and compares features of English and Kurdish business contracts from lexical and syntactic perspectives. It also addresses the issue of LSP lexical and syntactic precision used in the translated Kurdish contracts by translators. To this end, the paper attempts to identify the translation procedures and norms that manifest in translating business contracts from Kurdish into English. As a result, it scrutinizes a set of 20 Kurdish-English business contracts translated by professional licensed translators using Toury’s (2012) three-phase methodology. The translation direction is from Kurdish into English because it is the most practised direction by the translation service providers in Iraqi Kurdistan. The study demonstrates that both Kurdish and English legal languages use similar syntactic and lexical tools in drafting business contracts. It also reveals that initial norms, preliminary norms, and the law of interference are shown in the Kurdish- English translations. It further discloses that transposition, amplification, and couplets are prominently employed to deal with translation challenges. This paper is significant because it is the first attempt to scientifically investigate semantic features of originally written as well as translated Kurdish business contracts. For further research, it recommends a further focus on differences in legal systems and languages between Kurdish and English

Optimizing Vendor-Managed Inventory in Multi-Tier Distribution Systems

This paper addresses the concept of the vendor-managed inventory (VMI) problem, focusing on the replenishment policy and the vehicle routing problem (VRP) model. These components are integrated to tackle a three-echelon distribution issue comprising a single plant, multiple depots, and multiple retailers, with the primary objective of minimizing transportation and inventory costs within this complex distribution network. A three-phase methodology is proposed to optimize the entire supply chain, from the plant to the final retailer, and its performance is evaluated through computational experiments. This research is motivated by a real-life supply network, highlighting its practical relevance and applicability. To extend the capabilities of existing methods for solving the combined inventory and routing problem, an insertion heuristic is incorporated to enhance vehicle utilization, thereby reducing total costs. Computational results demonstrate the effectiveness of the improved algorithm, indicating that it is sufficiently robust for practical application. Significant cost savings can be achieved with the proposed approach, making it a valuable contribution to the field of supply chain optimization.

Translating Ulyssei@s: Work in progress between general and specialised translation in light of NMT

It is common knowledge that the publication of translation projects carried out by students is a very motivating factor for training. Since the second semester of 2015-2016, undergraduate translation students of the Faculty of Arts and Humanities of the University of Porto have participated in the development of the French and English versions of the digital encyclopaedia Ulyssei@s, created by the Institute for Comparative Literature Margarida Losa (ILCML) in the early 2010s. Besides motivating students, this translation project published on the Internet allows us to develop translation skills, foster teamwork, consolidate the three-phase methodology proposed by Gouadec (2002, 2007), and experiment with several essential tasks in language services: collaborative glossary-making, translation, revision of human translation and post-editing neural machine translation (NMT). Some of the challenges posed by the translation of Ulyssei@s are common to all types of translation, while others are specific to the translation of entries of this sui generis encyclopaedia, such as a variety of textual genres (biography, literary criticism, prose, poetry, theatre, essay) in hybrid texts and the need to find the original or the official translation of the works cited. This paper focuses on the data collected in the spring semester of 2022 and is based on the results of a survey submitted to students at the end of the project. It aims to share the experience of developing an inevitably unfinished project and adapting training and learning to the latest advances in Human Language Technology, since NMT has become a valuable tool for human translators, and Ulyssei@s is a digital encyclopaedia regularly enriched by ILCML collaborators with new entries.

Forging a sustainable path: improving India’s (Bharat’s) coal mining operations for a better tomorrow

PurposeSustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.Design/methodology/approachAn integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.FindingsThe study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.Practical implicationsThe proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.Originality/valueThe current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.

A hybrid matheuristic approach for the integrated location routing problem of the pineapple supply chain

This paper proposes a matheuristic approach for the location-routing of industrial platforms of the pineapple supply chain problem. We have proposed a three-phase methodology to solve the considered problem. The first phase consists of obtaining the potential supply in terms of suitability and productivity, the potential location of platforms, and the times of the value chain echelons. In the second phase, a mathematical optimization model for the location problem of platforms considering the coverage in terms of timing is proposed. Finally, the final phase proposes a cluster-routing and a granular reactive tabu search approach for the routing phase. The proposed methodology uses official information on production times, speed, and capacity and georeferenced aptitude, spatial, economic, and land yield information for the first time. The proposed approach has been validated through scenarios, particularly pineapple exports for the Colombian country. The obtained results show the efficiency of the proposed approach.

Integration technologies in laboratory information systems: A systematic review

Clinical laboratories have evolved with technological advancements through integrating various subsystems into Health Information Systems (HIS), particularly the Laboratory Information System (LIS). The LIS automates processes, manages results, and interfaces with healthcare information sources. Challenges include workflow inefficiencies and data interpretation issues. Despite increased data accessibility, managing clinical data across systems remains complex. Integrating laboratory machines into LIS is essential for optimizing healthcare delivery, requiring effective integration technologies. This study aims to synthesize the existing empirical studies on the utilization of integration technologies for Software-to-Software (S2S) communication in automating clinical laboratory processes. This study systematically examined integration technologies in LIS using PubMed and following PRISMA 2020 guidelines. The three-phase methodology included a scoping analysis, methodological analysis, and a gap analysis, focusing on S2S communication, interoperability frameworks, data standards, communication protocols, and challenges in LIS integration technologies. Analysis of 28 sample studies revealed a complex landscape in LIS integration shaped by end-users, care providers, and researchers. Clinical laboratories prioritize integration, focusing on patient data and sustainability. Standards like HL7 and FHIR ensure interoperability. Eleven methodologies highlight system development in Health Information Systems (HIS). Interoperability is a common objective, with 22 out of 28 studies achieving success. Challenges include limited generalizability, poor validation, and post-implementation modifications. Issues like security, data incompatibility, and evolving standards persist.

A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis.

Recognizing emotions from electroencephalography (EEG) signals is a challenging task due to the complex, nonlinear, and nonstationary characteristics of brain activity. Traditional methods often fail to capture these subtle dynamics, while deep learning approaches lack explainability. In this research, we introduce a novel three-phase methodology integrating manifold embedding, multilevel heterogeneous recurrence analysis (MHRA), and ensemble learning to address these limitations in EEG-based emotion recognition. The proposed methodology was evaluated using the SJTU-SEED IV database. We first applied uniform manifold approximation and projection (UMAP) for manifold embedding of the 62-lead EEG signals into a lower-dimensional space. We then developed MHRA to characterize the complex recurrence dynamics of brain activity across multiple transition levels. Finally, we employed tree-based ensemble learning methods to classify four emotions (neutral, sad, fear, happy) based on the extracted MHRA features. Our approach achieved high performance, with an accuracy of 0.7885 and an AUC of 0.7552, outperforming existing methods on the same dataset. Additionally, our methodology provided the most consistent recognition performance across different emotions. Sensitivity analysis revealed specific MHRA metrics that were strongly associated with each emotion, offering valuable insights into the underlying neural dynamics. This study presents a novel framework for EEG-based emotion recognition that effectively captures the complex nonlinear and nonstationary dynamics of brain activity while maintaining explainability. The proposed methodology offers significant potential for advancing our understanding of emotional processing and developing more reliable emotion recognition systems with broad applications in healthcare and beyond.

Real-Time Embedded Control of Electrical Drives: A New Approach for Bringing Hands-On Experience Into the Classroom

This article presents a novel educational platform for teaching advanced electric drive systems to students using embedded controllers. The developed platform is based on a portable motor test stand with a quadrotor motor interfaced with the Texas Instruments (TI) LaunchPad F28069 and TI BOOST-DRV8301 inverter. The platform is combined with Altair Embed educational software, allowing for direct access from personal laptop computers. We also present a three-phase methodology of teaching electric drives, which relies on simulation (SIM), co-SIM (COSIM), and hardware-in-the-loop (HIL) testing. The proposed approach would allow students to learn the control theory and techniques of motor drives with hands-on experience and without the need for advanced programming skills.

Analytical hierarchy process for ranking green neighbourhood efforts in the Middle East and North Africa region

PurposeResearch on green certificate rankings in the MENA region primarily focuses on building scale, relying on the certified project count. This assessment approach overlooks the spatial factor, failing to capture their influence on the urban built environment, thus potentially undermining other efforts not reflected by the project count. This research aims to rank countries in the Middle East and Northern Africa (MENA) region based on their ongoing efforts regarding green neighbourhood certification.Design/methodology/approachThis study employs a three-phase methodology to rank MENA countries' adoption of green neighbourhood certification systems: content analysis, multicriteria analysis (MCA) using the analytical hierarchy process (AHP) and spatial analysis.FindingsBased on the content analysis, four major performance indicators were identified and the conventional ranking using projects count was presented. Using AHP, the MCA could rank the countries in the region according to their unique performance indicators score, clarifying the differences between conventional and AHP-based rankings. Finally, the spatial analysis phase uncovers shortcomings in the traditional ranking method, revealing inaccuracies and misrepresentations for several countries.Originality/valueThe study presents an innovative ranking methodology to monitor the green neighbourhood actions of countries in future development and establish a pioneering framework to evaluate the impact of green certifications within the region.

A framework for occupational health risk assessment of nursing personnel in Indian healthcare system

Nursing personnel are exposed to different kinds of health hazards because of various job characteristics and complex work systems. The objective of this study is to identify and analyze the risk factors related to occupational health hazards of nurses in the Indian healthcare system. A three-phase methodology is used to assess occupational risk factors. Through literature review and opinions of experienced nursing personnel, the risk factors such as biological, chemical, ergonomic, physical, and psychosocial are identified and evaluated based on three parameters i.e., the effect of exposure, exposure period and exposure probability. The weights of the parameters are determined by Best-Worst Method (BWM) and the Fuzzy VIKOR method is considered to rank the risk factors in accordance with their level of severity. Biological and ergonomic risk factors are found to have higher levels of adverse effects in all the wards of Indian hospitals whereas, impact of chemical, physical and psychosocial risk factors varied from ward to ward. The risk assessment framework as developed helps to categorize risk factors from high degree to moderate degree, which might help to prepare an action plan to mitigate the risks associated with nursing activities in order to improve their health and safety status at the hospital.

The Status of the Saudi Construction Industry during the COVID-19 Pandemic

The outbreak of COVID-19 has had a profound impact on the Saudi construction industry as well as the country’s economy. The pandemic jeopardized the positive perspectives and growth in megaproject numbers, as it has amplified the constraints that prevent the construction sector from realizing growth. This research work is intended to evaluate the status of the Saudi construction industry during the COVID-19 pandemic. In order to achieve this objective, a three-phased methodology was developed. In the first part of the research, semi-structured interviews with forty industry experts were conducted. The gathered data from both, the literature review and interviews were synthesized. This process resulted in four domains to be explored: project performance measurement, workforce, supply chain, and financial management. The outcomes from phase one were then utilized to develop a questionnaire survey that was communicated to construction firms all over Saudi Arabia, for which 124 responses were received. Data analysis was carried out, and the obtained results were clarified and triangulated through a focus group discussion in the third phase of the research. The outcomes from the mixed-methods research methodology were aggregated to enrich and interpret findings and draw conclusions and recommendations. The findings indicate that the pandemic has had a total of ten core impacts. The highly impacted areas in the industry were the technical performance of projects, reduction in productivity, risk management practices, downsizing the scope of ongoing projects, reduction in new projects or contracts, material shortage, recruitment of manpower in the construction firms and affiliated projects, and the financial performance of the organization. In the meantime, it was found that the government implemented mitigation measures from which the sector benefited, where 27% of contractors obtained Saned system assistance, 51% received cash compensation and 22% received tax postponement. The scope of this study is limited to exploring the status of the Saudi construction industry (SCI) during the COVID-19 pandemic. The study findings are of added value and represent a significant contribution to the body of knowledge in the field. However, further research on the exit strategies bringing the industry to the new normal, including the use of cutting-edge technologies in the age of multi-faceted disruption would be of great importance.

Dynamic drift-off warning limits for dynamically positioned MODU with Deepwater Artificial Seabed (DAS) system coupling

A new Deepwater Artificial Seabed (DAS) drilling system, which enables the shallow-water rated drilling equipment to operate in deep and ultra-deep water, has been developed for dynamically positioned Mobile Offshore Drilling Unit (MODU), with a focus on key issues of well access and riser design. Nevertheless, the MODU may drift-off due to the critical failures of the dynamic positioning (DP) system and thus the well integrity will be gravely threatened. This study is committed to establishing the quantitative criteria of drift-off warning limits, which consist of the yellow limit, red limit and physical limit, for dynamically positioned MODU with the coupled DAS drilling system. To achieve this aim, a three-phase methodology is proposed on the basis of a fully coupled model of the MODU-DAS drilling system. Furthermore, a hybrid method integrating the orthogonal experimental design, back propagation neural network, and Garson's algorithm is developed to systematically investigate the importance of the correlative influencing factors on the drift-off warning limits in consideration of both the efficiency and accuracy of the numerical calculations. The validity of the proposed methodology is demonstrated by a case study. The results indicate that the dynamic drift-off warning limits are able to assist in decision-making of safe DP operations on the MODU and the operating depth of the artificial seabed is the most critical factor affecting the envelope of drift-off warning limits. These findings and recommendations could improve the safety of DP drilling operations.