Analytical Framework Research Articles

Communicating life-saving knowledge: The multimodal arrangement in Lifesaver VR

VR applications for medical simulations such as emergency situations offer new ways of providing knowledge and practical skills for saving a life and potentially represent a complex communicative environment. The communicative situations constructed by this environment bring with them an increasing level of interactivity and ergodicity and it is particularly challenging to address these analytically. We address this challenge by looking at Lifesaver VR and providing a foundational framework for the multimodal analysis of the communicative situations created in this application. We describe how information and content units of the game can be built and how these units can then be combined into complex discourse structures outlining the complexity and simultaneity of narrative, instructional, and interactive aspects of the game.

Seismic fragility analysis of nuclear containment structure with Bayesian cloud analysis framework

Nuclear containment structure plays a critical role in safe operation of nuclear power plants. Seismic safety of nuclear power plant structures has become a hot issue in nuclear engineering community following Fukushima nuclear accident. Seismic fragility analysis, as a key element in probabilistic safety assessment of nuclear power plants, is widely used in nuclear power plant structures. This study presents an innovative method for seismic fragility analysis of engineering structures. The proposed methodology combines conventional Cloud analysis method and Bayesian framework. The accuracy and efficiency of the proposed methodology were demonstrated by a single-degree-of-freedom system, heavy computational efforts but high accuracy seismic fragility analysis method was adopted for comparison. Seismic fragility analysis of a typical prestressed concrete nuclear containment structure was analyzed using the proposed method. Results indicated that method proposed in this study can improve the accuracy of seismic fragility analysis. Furthermore, with the enhance of performance level, the increasing ratios for median seismic capacity of the nuclear containment structure was decreasing.

Spectroscopic, quantum chemical and molecular docking studies of N-((anthracen-9-yl) methylene-2-methyl benzenamine): a potent probe to lipid bilayer systems

ABSTRACT A new synthesized Schiff base, N-((anthracen-9-yl) methylene-2-methyl benzenamine) (AMMB), was prepared from o-toluidine and 9-anthracenecarboxaldehyde. The structure of the compound was confirmed by single crystal X-ray diffraction and characterized through various analytical tools such as high resolution mass, 1H and 13C NMR, infrared, UV-vis and elemental composition analysis. The molecular geometries of AMMB were optimized by means of DFT/B3LYP/6311-G++ (d,p) basis set. The optimized geometric parameters of the compound were concordant with the single crystal structure. The optimized structure was utilized in comparison of experimental FT-IR with calculated frequency calculation. TD-DFT analysis was performed to compute the electronic absorption of the compound. SCF-GIAO was employed to compute 1H and 13C NMR, which was consistent with the experimental results. Additionally, NLO, NBO, MEP and global reactivity parameters were also computed using the same level of theory. Hirshfield surface analysis, fingerprint plot and energy frameworks were presented to explore the intermolecular interactions within the crystal. Molecular docking study was carried out to predict the probable binding site of AMMB into lipid bilayers such as DMPC, DPPC, POPC, DLPE, DOPC and POPE. The molecular docking analysis of AMMB showed complex formation with binding affinity energy ranging from −9.3 to −3.4 kcal/mol.

The Relationship Between Institutional Pedagogy and the Brand Identity Formation of Higher Education Institutions

This study sought to facilitate insight into the potential role of pedagogy in the brand identity formation of higher education institutions (HEIs) through a study of selected HEIs in The Gambia. Specifically, the study sought to address the following research question: What role do pedagogical practices play in building the brand identity of selected universities in The Gambia? The intra-paradigm qualitative mixed method of data collection underpinned the research design. This research design facilitated a preliminary analysis of the contents of institutional documents and social media postings. This process was followed by telephonic and virtually mediated in-depth interviews through which this researcher explored the interactionist interpretations, recollections, experiences, and opinions of 54 participants (students and staff) on the themes of institutional brand management practices, institutional pedagogical practices, institutional brand identity, and the links between pedagogical practices and institutional brand identity. The study used the Corporate Brand Identity Matrix as a supporting analysis framework. The findings show a relationship between pedagogical practices and institutional brand identity formation. The evidence suggests that the selected HEIs use hardly differentiated production-style portfolios of academic courses to pursue largely unengaged students, prospective students, and other stakeholders. Further findings indicate that teaching and learning practice is dominated by academic staff's discretionary use of transmissive pedagogy. This insight emerged against the background of additional evidence, which shows a link between pedagogy policy and practices of HEIs and stakeholder impressions. A synthesis of these findings culminated in the emergence of the pedagogy-based higher education brand identity matrix (P-HEBIM), which this study proposes as a novel framework for the branding of HEIs. The study sets out a practitioner guide on how higher education managers can pursue the institutional brand management priorities of branding strategy development, competitor intelligence, and brand communication using the P-HEBIM as a framework.

Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Chinese Swimming Club in Singapore. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots for a total of 750. Key results include: (1) The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. (2) QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. (3) Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). (4) A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players’ shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics.

Sanitary segregation imposed by Big Brother – a Multimodal Critical Discourse Analysis of Grzegorz Braun’s radical anti-vaccine rhetoric

ABSTRACT The coronavirus pandemic greatly impacted political communication. Operating within the framework of Multimodal Critical Discourse Analysis, this paper discusses the discursive strategies of Grzegorz Braun, one of the leaders of the Confederation Liberty and Independence – a Polish coalition of right-wing parties – by investigating 97 YouTube thumbnails and video titles. The present paper offers an in-depth investigation of Braun’s radical and anti-vaccine discourse, focusing on both the verbal and visual components, and how they form a coherent meaning. The findings indicate that Braun’s main strategy was to instill a sense of fear and danger. Similarly to other right-wing populists, Braun created narratives that the pandemic was a conspiracy crafted by global elites and that the vaccines were a tool for controlling society. However, Braun did not just copy the discourse of other politicians, but adjusted his message to the viewers, as he would make frequent references to the People’s Republic of Poland and compared the Polish government to communists. Overall, while Braun’s message was rather straightforward, the politician used a large variety of semiotic choices, focusing on potent cultural symbols, size, color, as well as intensification and predication strategies.

The Impact of OER’s Continuous Improvement Cycles on Students’ Performance: A Longitudinal Analysis of the RISE Framework

Open educational resources (OER) have been praised for revolutionizing education. However, practitioners and instructors battle keeping OER updated and measuring their impact on students’ performance. Few studies have analyzed the improvement of OER over time in relation to achievement. This longitudinal study uses learning analytics through the open-source Resource Inspection, Selection, and Enhancement (RISE) analysis framework to assess the impact of continuous improvement cycles on students’ outcomes. Panel data (i.e., performance and use) from 190 learning objectives of OER of an introductory sociology course were analyzed using a hierarchical linear model. Results show that more visits to an OER do not improve student achievement, but continuous improvement cycles of targeted OER do. Iterative implementation of the RISE analysis for resource improvement in combination with practitioners’ expertise is key for students’ learning. Given that the RISE classification accounted for 65% of the growth of students’ performance, suggesting a moderate to large effect, we speculate that the RISE analysis could be generalized to other contexts and result in greater student gain. Institutions and practitioners can improve the OER’s impact by introducing learning analytics as a decision-making tool for instructional designers. Yet, user-friendly implementation of learning analytics in a “click-and-go” application is necessary for generalizability and escalation of continuous improvement cycles of OER and tangible improvement of learning outcomes. Finally, in this article, we identify the need for efficient applications of learning analytics that focus more on “learning” and less on analytics.

Bravery and Friendship in Children's Literature through the Lens of Van Dijk's Critical Discourse Analysis

This research utilizes Van Dijk's (1977) Critical Discourse Analysis (CDA) framework to investigate the themes of bravery and friendship in the children's stories "The Brave Little Squirrel" and "Milo's Magical Map." The primary objective is to examine how discourse influences social identities, values, and interpersonal relationships through narrative structures and character dynamics. Using qualitative methods, the study conducts a detailed textual analysis focusing on thematic elements, character dialogues, and the moral lessons delineated in both stories. The findings indicate that these narratives portray bravery not solely as a heroic quality but as an expression of aiding others, thereby nurturing a sense of community and companionship. Additionally, the analysis uncovers the underlying social values presented in these narratives, underscoring the significance of empathy and collaboration for young readers. This research enhances the understanding of children’s literature as a crucial factor in shaping social and moral growth, offering valuable insights for educators and parents regarding themes that can effectively instill positive values in children.

Implications of age and sex on the diagnostic yield of sudden arrhythmic death syndrome

Abstract Introduction Sudden arrhythmic death syndrome (SADS) refers to an unexplained sudden death with a negative autopsy. An inherited cardiac condition (ICC) may be diagnosed through familial clinical evaluation (FE) or post-mortem genetic testing (molecular autopsy [MA]) or both. We aimed to investigate the impact of age and sex of SADS decedents on the diagnostic yield and aetiology to facilitate a targeted approach to management. Methods Consecutive referrals to a single centre for FE only, MA only or both, following a SADS death were included. First-degree family members underwent comprehensive FE. A panel of 36 cardiac genes was sequenced for MA. A post-mortem diagnosis could be obtained by either FE, MA or both. A Bayesian framework for analysis was performed to identify associations. Results Seven-hundred-and-sixty SADS decedents (66% male; mean age 31±12 years) were investigated. The overall diagnostic yield for an ICC was 37% (32-42%) and 9% (6-12%) for FE and MA cohorts, respectively. In a subset where both MA and FE were performed the diagnostic yield was 45% (38-61%). For each year increase in age, the relative risk of an FE diagnosis of long QT syndrome (LQTS) or Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT) versus remaining unexplained declined by 5.6% (RR 0.94 [0.91-0.98]) and by 11% (RR 0.89 [0.81-0.97]), respectively. Females were more likely to have a diagnosis compared with males by both FE (40% [34-45%] vs 36% [31-41%]) and MA (15% [10-21%] vs 6% [3-8%]). Females (8.1% [4.1-13.4%)], were more likely to be diagnosed with LQTS than males (1.2% [0.2-2.7%]) in the MA cohort. Conclusions After a SADS death, the diagnostic yield of comprehensive FE, MA or both in an expert setting can be up to 45%. Younger age of death is associated with greater likelihood of LQTS and CPVT diagnosis with the highest yield in children and adolescents. Female sex in SADS decedents is associated with a higher yield of LQTS in MA. These data highlight the relative utility of FE and MA depending on age and sex for determining underlying diagnoses following SADS deaths.

Built Environment’s Non-Linear Impact on Subway Passenger Flow Through Improved Interpretable Machine Learning

Understanding the complex correlation between the built environment and subway passenger flow can provide unique insights for the development of transportation operations and urban coordination policies. Few studies have systematically analyzed the rationality of selecting built environment variables and further explored the non-linear relationships. In this study, we integrated various sources of built environmental factors and developed an interpretable machine learning analysis framework using backward elimination extreme gradient boosting and SHapley Additive exPlanations (SHAP) values analysis (BE-XGBoost-SHAP). The framework was validated by analyzing passenger flows during the morning peak, non-peak, and evening peak periods at the station level. The research results indicate that there are significant differences between built environment factors and the time-varying passenger flow. Land use characteristics significantly dominate across all three temporal periods. The importance of other variable types in relation to passenger flows varies significantly across the three time periods. It is worth noting that the relationships between all variables and passenger flow at different time periods are non-linear, with the majority displaying threshold effects. Compared with the gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models, the proposed interpretive framework performs better as regards R-square, root mean square error (RMSE), and mean absolute error (MAE) metrics. This study offers valuable insights, elucidating the pivotal land use attributes that notably affect passenger flow, the significance of varied built environment factors across distinct time spans, and the acknowledgment of non-linearities and threshold effects within these relationships. These findings are imperative for urban planning and the enhancement of station area design.

Impact of addressing modifiable risk factors on the 10-year risk of cardiovascular disease in individuals with familial hypercholesterolemia: a causal analysis utilising UK biobank

Abstract Background Familial hypercholesterolemia (FH) is a prevalent autosomal dominant disorder characterised by elevated low-density lipoprotein cholesterol (LDL-C) levels from birth. These elevated LDL-C levels significantly amplify the lifetime risk of cardiovascular disease (CVD). While the impact of early and effective initiation of lipid-lowering medication (LLM) on CVD risk is well documented in genetically confirmed FH, the additional impact of addressing modifiable lifestyle risk factors (MLRF) in that patient population remains largely unquantified. Methods This study quantified the potential effects of successfully managing 4 MLRF (smoking, obesity, alcohol consumption, and low physical activity) on the 10-year incidence of a first CVD diagnosis in adults with genetically confirmed FH treated with LLM within the UK Biobank. The synthesised causal estimates of these 4 MLF were obtained using two recent causal analysis frameworks, namely the doWhy causal calculus framework and the Causal Forest Double Machine Learning Method. Results In 660 FH individuals (mean age 56±8 years; 60.15% women), smoking cessation and reducing body mass index below 30 kg/m2 delivered the most substantial risk reductions (pooled estimates, adjusted for the 3 other MLRF: -6.5% and -3.2%, respectively; both p<0.001), followed by initiating moderate or high physical activity (-1.7%; p<0.001; Figure). Notably, cessation of alcohol consumption significantly increased CVD risk (+2.5%; p<0.001), aligning with existing literature. Conclusion In over 10 years, reductions in CVD risk ranging from 1.7% to 6.5% were achievable through smoking cessation, BMI reduction, and enhanced physical activity in FH individuals. Leveraging causal inference methodologies facilitates the discernment of which risk factors offer the most pronounced additional benefits beyond lipid-lowering interventions in this high-risk population.Forest Plot of Causal Effect Estimates

Energy Transition in Northern Netherlands: seeking a balance between top-down and bottom-up initiatives

This brief report focuses on top-down and bottom-up processes within the field of energy transition. It aims at gaining a better understanding of the needs of the local energy initiatives. On this basis, policy recommendations are formulated to help the municipality of Groningen to facilitate local energy initiatives, ultimately leading to a more balanced approach of the local energy transition. Thus, this study explored the (mutual) interests, barriers and expectations of the municipality and local citizen initiatives. The theoretical framework is the implementation analysis framework, distinguishing top-down and bottom-up approaches. Specifically, this qualitative (thematic analysis) research study investigates the mismatch in expectations between a number of local energy initiatives and the municipality of Groningen regarding their roles within the local energy transition context. To this end, semi-structured interviews have been conducted with members of the municipality of Groningen, Grunneger Power (a local energy intermediary), and four local energy initiatives. Need and expectation gaps have been identified and potential solutions have been explored. The main findings of the study illustrate the need of professional support for citizen initiatives, at both technical and organizational level, especially in the first phases of their development. Additionally, clear mutual communication on short and long-term planning and ambitions of the involved parties is of key importance for the alignment of the interests and the course of actions. Consequently, a clear context is needed, within which an exchange of feedback on the envisioned strategies, and the subsequent energy saving or generation interventions, can take place in an efficient and effective way. Additionally, such a context increases confidence and provides a clear understanding to the citizen initiatives regarding their role and the level and nature of support they can expect in their intended projects and activities. Based on these findings, policy implications have been drawn.

E-waste challenges of generative artificial intelligence.

Generative artificial intelligence (GAI) requires substantial computational resources for model training and inference, but the electronic-waste (e-waste) implications of GAI and its management strategies remain underexplored. Here we introduce a computational power-driven material flow analysis framework to quantify and explore ways of managing the e-waste generated by GAI, with a particular focus on large language models. Our findings indicate that this e-waste stream could increase, potentially reaching a total accumulation of 1.2-5.0 million tons during 2020-2030, under different future GAI development settings. This may be intensified in the context of geopolitical restrictions on semiconductor imports and the rapid server turnover for operational cost savings. Meanwhile, we show that the implementation of circular economy strategies along the GAI value chain could reduce e-waste generation by 16-86%. This underscores the importance of proactive e-waste management in the face of advancing GAI technologies.

Discourse Historical Analysis of Identity Construction of New Energy Enterprises in China and the United States

In recent years, with the increasing awareness of climate change and environmental protection, new energy vehicles have gradually become a hot topic in the automotive industry. This article uses the Social Responsibility Report of BYD and Impact Report of Tesla as corpus, adopts a discourse-history research approach to analyze the three-dimensional framework of discourse analysis, and uses the corpus tool Wmatrix 6.0 to analyze the macro-themes, meso-discourse strategies, and micro-language features of corporate identity construction in China and the United States, explaining their similarities and differences in the construction of new energy enterprise identities. This study provides reference for the identity construction of new energy enterprises in China, and has practical significance for enhancing the international discourse construction ability of Chinese new energy enterprises.

A Review of Comprehensive Guidelines for Computational Fluid Dynamics (CFD) Validation in Solar Chimney Power Plants: Methodology and Manzanares Prototype Case Study

This review provides a comprehensive examination of CFD modeling procedures for SCPP, with an emphasis on the detailed methodologies and a case study of the Manzanares prototype in Spain. The introduction delineates the historical context and physical modeling principles of solar chimneys, while highlighting their potential in industrial applications. The governing equations are meticulously discussed, covering assumptions in both 2D and 3D CFD modeling, the continuity and momentum equations, and the selection and accuracy of turbulence models, particularly the k-ε equations. The review also delves into heat transfer modeling, encompassing the energy equation and radiation modeling. Analytical evaluations of turbine pressure drop ratios and performance metrics for power generation efficiency are critically analyzed. The establishment of boundary conditions in solar chimney applications, including sky temperature assessments and distinctions between 2D and 3D boundary conditions, is extensively explored. Mesh generation techniques for both 2D and 3D CFD models are presented, supported by case studies. Parametric studies and experimental investigations are scrutinized to elucidate their impact on the performance of solar chimneys. The temperature–entropy diagram for an idealized Brayton cycle is introduced as a conceptual framework for efficiency analysis. Validation of the CFD codes, both 2D and 3D, against experimental data is performed to ensure model accuracy. The review further examines energy balance approaches in modeling solar chimneys, presenting state-of-the-art CFD results and discussing their implications in both 2D and 3D contexts. The synthesis of these findings culminates in a comprehensive conclusion, offering insights into the future directions and potential advancements in the CFD modeling of solar chimneys. This work aims to serve as a definitive reference for researchers and practitioners in the field, providing a robust foundation for the development and optimization of SCPP technology.

Development of an Analysis of Alternatives Tool for Human-Agent Teaming Research

As human-agent teaming (HAT) research continues to proliferate, it becomes more difficult for both HAT researchers and agent developers to stay abreast of the literature. To this end, we developed an analysis of alternatives tool that enables The Contextual Labeling of Analytical Information into Relevant Valuable Options to Yield Alternatives for Novel Techniques (CLAIRVOYANT). Powered by a multiple criteria decision analysis (MCDA) framework, CLAIRVOYANT aids in hypothesis generation and experimental design by displaying summarized relationships observed in the HAT literature based on user needs. After iteratively designing literature-informed frameworks, generating an initial literature database, and instantiating the MCDA, a validation study was conducted demonstrating CLAIRVOYANT’s potential. Current limitations and future directions are discussed.

Insights Into Critical Discussion: Designing a Computer‐Supported Collaborative Space for Middle Schoolers

Collaborative learning environments that support students' problem solving have been shown to promote better decision‐making, greater academic achievement, and more reasonable argumentation about controversial issues. In this research, we developed a technology‐based critical discussion platform to support middle school students' argumentation, with a focus on evidence‐based reasoning and perspective taking. A feasibility study was conducted to examine the patterns of group interaction and individual students' contributions to the critical discussion and their perceptions of the critical discussion activity. We found that more students used text‐based communications than audio, but students who used audio collaborated with each other more frequently. In addition, student engagement in argumentative discourse varied greatly across groups as well as individuals. At the end of the discussion, most groups provided a solution that integrated both sides of the controversial issue. Survey and interview results suggest an overall positive experience with this technology‐supported critical discussion activity. Using the insights from our research, we develop a conceptual dialogue analysis framework that identifies relevant skills under the argumentation and collaboration dimensions. In this report, we discuss our design considerations, feasibility study results, and implications of engaging students in computer‐supported collaborative argumentation.

Assessment of the Choked Flow Model of RELAP5 for Application of Inverse Quantification Methods

In the framework of deterministic safety analysis, best estimate plus uncertainty methodologies can provide a more informative detailed analysis of transients than conservative approaches. However, one important step in the application of such methodologies is derivation of uncertainty of the physical models. Probability density functions of the physical model parameters are obtained by applying inverse uncertainty quantification (IUQ) methods to experimental data. The present work deals with evaluation of the experimental database and assessment of the simulation model, which are required steps prior to the application of IUQ methods. The U.S. Nuclear Regulatory Commission system code RELAP5 has been applied for simulation of three experimental databases on choked/critical flow: Sozzi-Sutherland, Super MobyDick, and Marviken Critical Flow Tests. First, the adequacy of the experimental data to the specific target domain is carried out, to then proceed to calibration of the input model parameters by combining the use of Gaussian Process metamodels and optimization schemes. The assessment of the simulation models is performed by evaluating independently accuracy, precision, and consistency through four statistical indicators, including a novel indicator to evaluate the consistency of the results. The final results indicate that the model is capable of reproducing the selected experimental database and overall average accuracy, precision, and consistency below the 10% threshold and can be used for application of IUQ methods.

High variability exists in 3D leg alignment analysis, but underlying principles that might lead to agreement on a universal framework could be identified: A systematic review.

To (1) investigate the hypothesis that there is high variability in the reported methods to derive axes and joint orientations from three-dimensional (3D) bone models to (a) perform 3D knee-related leg alignment analysis and (b) define coordinate systems for the femur, tibia and leg and (2) identify underlying principles that might lead to agreement on a universal 3D leg alignment analysis framework. A systematic review of the literature between January 2006 and June 2024 was performed. Articles explicitly reporting methods to derive axes and joint orientations from CT-based 3D bone models for alignment parameters and/or coordinate systems of the femur, tibia and leg were included. Study characteristics and reported methods were extracted and presented as a qualitative synthesis. A total of 93 studies were included. There was high variability in the reported methods to derive axes and joint orientations from 3D bone models. Nevertheless, the reported methods could be categorized into four groups, and several underlying principles of the four groups could be identified. Furthermore, the definitions of femoral and tibial coordinate systems were most frequently based on the mechanical axis (femoral, 13/19 [68%]; tibial, 13/26 [50%]) and a central medial-lateral axis (femoral, 16/19 [84%]; tibial, 12/26 [46%]); no leg coordinate system was reported. Interestingly, of the included studies that reported on leg alignment parameters (76/93, 82%), only a minority reported expressing these in a complete coordinate system (25/76, 33%). There is high variability in 3D knee-related leg alignment analysis. Therefore, universal 3D reference values for alignment parameters cannot yet be defined, and comparison of alignment parameter values between different studies is impossible. However, several underlying principles to the reported methods were identified, which could serve to reach more agreement on a future universal 3D framework for leg alignment analysis. Level I (1).

20 years review of the multi actor multi criteria analysis (MAMCA) framework: a proposition of a systematic guideline

AbstractMulti-criteria Decision Making (MCDM) involves evaluating alternatives based on a comprehensive set of conflicting criteria, often requiring the involvement of varied decision makers. This has led to the emergence of stakeholder-based multi-criteria group decision-making (MCGDM) frameworks. However, traditional MCGDM frequently overlooks the interactions and trade-offs among different actors and criteria. The Multi-actor Multi-criteria Analysis (MAMCA), developed in 2000, provides a transparent decision-making process explicitly considering these interrelationships. It allows diverse stakeholder groups to represent their priorities, thereby enhancing their understanding of their own and others’ positions. Over the past two decades, MAMCA has seen a significant rise in popularity and has been widely applied in diverse contexts, proving valuable as both a decision-making and stakeholder engagement tool. However, our analysis of publications on cases in which MAMCA was applied over the years shows that considerable variation exists in the overall process approach, contingent upon the specific goals and context. To address these variations, this paper proposes a modularized MAMCA structure, complemented by systematic application guidelines, to aid future users in navigating the process steps and identifying the most suitable methods for each step. Additionally, future research directions are suggested for potential enhancements to the MAMCA framework by integrating varied methodologies.