The study aimed to develop a comprehensive flight performance evaluation model based on flight parameters, covering the entire flight and applicable to normal and abnormal conditions. Thirty-seven pilots performed one normal traffic pattern flight and one single-engine failure emergency flight using a Cessna-172 simulator. The complete flight was divided into distinct phases - takeoff, climb, cruise, descent, approach/landing, and emergency, with evaluation metrics defined for each phase. The analytic hierarchy process was employed to determine the weights of flight phases and evaluation metrics. Two flight instructors provided ratings of performance after reviewing video recordings of the flights. ChatGPT generated five sets of performance scores based on the flight data. Intraclass correlation coefficient and correlation analyses indicated good consistency across multiple evaluation sources. Significant correlations were found among model-derived scores, instructor ratings, and ChatGPT-generated scores. These findings demonstrate that the model is reliable, and potentially applicable to real-world flight training and operations.

Rural buildings in northwestern Hunan face multiple challenges in achieving a green and low-carbon transition, including fragile ecological environments, limited access to resources, and strong cultural preservation demands-rendering existing urban-based green building strategies largely inapplicable. To address these issues, this study develops an integrated evaluation framework combining Life Cycle Assessment (LCA) and the Analytic Hierarchy Process (AHP), aiming to facilitate the green transformation of rural architecture in the region. Based on field surveys and simulation modeling of 24 sample buildings, findings indicate that carbon emissions across the building lifecycle are predominantly concentrated in the material production and operational phases, jointly accounting for over 85% of total emissions. Among the three building types, traditional timber dwellings exhibit the lowest total carbon footprint (34,875.5-47,184.0 kg CO₂-eq), followed by modern energy-efficient houses (91,284.0-117,908.5 kg CO₂-eq), while brick-timber hybrid structures show the highest emissions (99,300.0-139,020.0 kg CO₂-eq). AHP-based weight analysis identifies "Resource Efficiency" and "Environmental Livability" as the two most influential dimensions, with a combined weight of 0.699, underscoring their pivotal role in shaping green performance. Accordingly, the study proposes differentiated low-carbon optimization pathways: traditional buildings should focus on utilizing locally sourced low-carbon materials and passive ventilation strategies; modern structures should prioritize operational energy efficiency; and brick-timber hybrids require targeted energy retrofit interventions. The results validate the scientific robustness of the LCA-AHP hybrid model and enhance its regional applicability through localized parameter adjustments, offering a quantitative foundation and optimized pathway for advancing sustainable rural building design in ecologically sensitive areas.

Background/Objectives: Viral diseases remain a major threat to global public health, particularly during outbreaks when limited therapeutic resources must be rapidly and fairly distributed to large populations. Although Convalescent Plasma (CP) transfusion has shown clinical promise, existing allocation frameworks treat patient prioritization, donor selection, and validation as separate processes. This study proposes a credible, converged smart framework integrating multicriteria decision-making (MCDM) and regression-based validation within a telemedicine environment to enable transparent, data-driven CP allocation. Methods: The proposed framework consists of three stages: (i) Analytic Hierarchy Process (AHP) for weighting five clinically relevant biomarkers, (ii) dual prioritization of patients and donors using Order Preference by Similarity to Ideal Solution (TOPSIS) and Višekriterijumsko Kompromisno Rangiranje (VIKOR) with Group Decision-Making (GDM), and (iii) regression-based model selection to identify the most robust prioritization model. An external dataset of 80 patients and 80 donors was used for independent validation. Results: The external GDM AHP-VIKOR prediction model demonstrated strong predictive performance and internal consistency, with R2 = 0.971, MSE = 0.0010, RMSE = 0.032, and MAE = 0.025. Correlation analysis confirmed biomarker behavior consistency and stability in ranking, thereby reinforcing the reliability of the prioritization outcomes. Conclusions: The proposed patient–donor matching framework is accurate, interpretable, and timely. This work presents an initial step toward realizing safe AI-enabled transfusion systems within telemedicine, supporting transparent and equitable CP allocation in future outbreak settings.

The high level of competition in the coal mining sector demands that companies optimize their strategies, technologies, and resources, including selecting the right vendors and partners, to survive and achieve maximum profit with minimal cost. The objective of this research is to select the best vendor for the procurement of wastewater quality monitoring at PT XYZ. The method used is the Analytic Hierarchy Process (AHP). There are six criteria used in the vendor selection process: quality, cost, delivery, environment, safety, and morale. The weight of each criterion was obtained through a pairwise comparison process conducted by seven experts in supply management and occupational safety, using Saaty's scale. The consistency of judgments was verified using the Consistency Ratio (CR) calculation, and all comparison matrices met the CR threshold. Among five suppliers evaluated, Supplier 4 (coded as A4) emerged as the best vendor, achieving the highest overall weight across all criteria.

Based on improved thin-film dispersion method with an optimized preparation process, elevated encapsulation efficiency and excellent stability of the single-chamber liposome, lip@MET/CUR, were achieved for co-delivery of metformin (MET) and curcumin (CUR). To increase the volume of the hydration chamber and the specific surface area during lipid membrane formation, Tween-80 and glass microspheres were introduced in the preparation process. On this basis, the optimal process parameters for high encapsulation efficiency were screened and determined by combining the analytic hierarchy process (AHP), entropy weight method (EWM), and Box-Behnken response surface optimization. Eventually, the optimal encapsulation efficiencies for MET and CUR were determined to be 46.4% ± 1.3% and 94.1% ± 1.5%, respectively. The lip@MET/CUR exhibited an average particle size of 150 ± 2.5 nm with uniform particle size and good storage stability. In vitro drug release experiments revealed a significant sustained-release characteristic of lip@MET/CUR. Specifically, the cumulative release rate of MET decreased from 96.8% to 57.4% within the initial 2 h. Results from MTT assays and experiments conducted in tumor-bearing mice further demonstrated that lip@MET/CUR was more effective in inhibiting the growth of HepG2 cells and tumors compared to free CUR or lip@CUR. In summary, our findings suggest that the optimized lip@MET/CUR formulation holds great potential as a candidate for investigating the synergistic effects of CUR and MET in tumor treatment.

In 2022, an unexpected Japanese encephalitis virus (JEV) outbreak affected Australia, causing human and pig infections. Climate conditions were previously found to be risk factors of JEV outbreaks. Hence, understanding their future risk due to climate change can help inform public health authorities of the potential JEV risk, particularly in nonendemic areas such as Victoria, Australia. Following up on a previous investigation, this study aimed to identify regions in Victoria, Australia, that might present high-risk areas of JEV in future climatic scenarios. An analytical hierarchy process with an expert panel was the methodology implemented to analyze the risk of JEV under 2 emission scenarios: SSP1-2.6 (low emission) and SSP5-8.5 (very high emission) from 2021 to 2100. Victoria showed more high-risk areas of JEV than the historical risk during the summer months under both emission scenarios and for all periods. Gippsland, Hume, and the Melbourne Metropolitan areas were the most vulnerable regions to JEV risk, with more high-risk areas also in the autumn and spring months under the SSP5-8.5 emission scenario. Climate change could exacerbate the presence of high-risk areas of JEV in Victoria, Australia, in the immediate and distant future. These results underline the urgency of preparing for outbreaks and epidemic events, particularly in regions of Victoria not currently categorized as high-risk for flavivirus outbreaks.

The complexity of industrial investment casting is determined by three factors related to geometry, desired features, and manufacturability that are further driven by 19 elements, 52 attributes, and 212 meta-attributes. The complexity of the investment casting process is calculated using one of the important multi-criteria decision-making methods, such as the analytical hierarchy process. The complexity helps to make decisions about producing industrial casting using investment casting, the impact of any individual parameter, as well as the dependency of individual parameters on the overall process. However, representing complexity for easy understanding is challenging. The researchers explored various approaches, but graph theory in injective coloring remains unexplored in published literature. This paper presents a solution to the problem of representing the complexity index in graph-based systems that leverage both a graph theory adaptive consensus mechanism algorithm and injective coloring. The proposed approach guarantees effective node representations and is robust enough to identify the maximum and minimum complexity values of a particular parameter on the graph. By applying injective coloring, collection conflict is minimized and node differentiation is extended, allowing critical components of the network to be easily located. The adaptive consensus mechanism algorithm readily adapts to a varying graph structure to create scalability and efficiency.

AimsTo enhance the user experience and satisfaction of children’s medical nebulizers, and to improve adherence and the efficacy of nebulization treatments for children, this study explores an innovative design and evaluation method for children’s medical nebulizers from the perspective of user needs.MethodsFirstly, this study conducts a thorough analysis of user behaviors and their potential needs for children’s medical nebulizers through field observations, User Journey Mapping (UJM), relevant user interviews, and the KJ method, thereby constructing a hierarchical model of demand indicators. Next, the Analytic Hierarchy Process (AHP) is employed to calculate the weight and priority ranking of each indicator, effectively identifying the crucial demand indicators that influence nebulizer design. Based on the findings from the demand analysis, an innovative design practice for children’s medical nebulizers is carried out. Finally, the fuzzy comprehensive evaluation (FCE) method is used to assess the user satisfaction of the proposed design scheme and an existing nebulizer product case used in children’s hospitals and compare the results to verify the feasibility and effectiveness of the design approach and scheme in this study.ConclusionThe results indicate that the combined application of UJM, the KJ method, AHP, and FCE can help designers more accurately capture diverse user needs, enhance the scientific rigor and rationality of design and evaluation processes for children’s medical nebulizers, and ultimately produce products with higher user satisfaction. This study contributes to the field by providing a systematic framework for the design and evaluation of preschool-aged children’s medical nebulizers, offering theoretical guidance and practical reference for future designers.

Optimization of rural infrastructure conservation in the peruvian highlands using the HDM-4 model and the analytic hierarchy process

Flood hazard zonation using remote sensing, geographic information system, and analytic hierarchy process in the Bhagirathi River Basin, Uttarakhand, India

Landslide susceptibility mapping in the Bokoya Massif, Northern Morocco: A geospatial and multi-factor analysis using the analytic hierarchy process (AHP)

Under the dual impetus of industrial upgrading and higher education quality enhancement, it has become a common consensus to construct a student competency evaluation tool that can reflect the entire "teaching-learning-application" chain. However, existing studies remain insufficient in terms of system integrity and quantitative operability. Guided by the CIPP evaluation model (Context-Input-Process-Product), this study follows the logical progression of "context-input-process-output." Through a combination of literature review, expert interviews, and the Delphi method, an initial set of indicators was developed. The Analytic Hierarchy Process (AHP) was then employed to determine indicator weights, and a fuzzy comprehensive evaluation approach was integrated to construct the quantitative model. The results indicate that the established system effectively balances process monitoring and outcome orientation, emphasizing university-industry collaboration, authentic learning contexts, and ability transferability, while demonstrating strong interpretability and diagnostic value. The final framework includes 4 primary indicators, 11 secondary indicators, and 68 tertiary indicators. The expert authority coefficients for the two Delphi rounds were 0.840 and 0.845, respectively, with Kendall's coordination coefficients of 0.182 and 0.244. The AHP consistency test yielded CR < 0.1, confirming reliability. Using a sample of 132 students from the 2019 cohort of the Mechanical Engineering program at a "Double First-Class" university, model application results showed that 79.6% of students achieved an overall competency level of "good" or above. Among the first-level dimensions, the expected values of process evaluation and input evaluation outperformed those of context evaluation and output evaluation, suggesting the need to further strengthen institutional reputation building and graduate quality feedback mechanisms. The findings demonstrate that the proposed indicator system and evaluation model can effectively mitigate ambiguity and subjectivity in competency assessment. It possesses high applicability and promotional value in supporting teaching quality diagnostics, talent training program optimization, and deep university-industry collaboration.

Landslide susceptibility mapping using geospatial, analytical hierarchy process (AHP), and binary logistic regression (BLR) techniques – A study of Wadi Habban Basin, Shabwah, Yemen

The remediation of contaminated sites necessitates robust and objective sustainability assessment frameworks to guide decision-making, yet prevailing methods often rely on qualitative or semi-quantitative metrics susceptible to subjectivity. This study establishes a comprehensive, fully quantitative evaluation system integrating environmental, economic, and social dimensions, comprising 13 objective indicators derived from Life Cycle Assessment (LCA), economic documentation, and publicly accessible social data—including nighttime light intensity, Point of Interest (POI) data, and social media sentiment analysis. The system employs the Analytic Hierarchy Process (AHP) for weight assignment, ensuring methodological rigor and expert consensus. Validated through three case studies of remediated contaminated sites in Shandong Province, China, the framework reveals distinct sustainability profiles: Site 1 achieved the highest composite score (0.1030), demonstrating balanced performance across all dimensions, whereas Sites 2 and 3 yielded negative scores (−0.2490 and −0.1069, respectively), reflecting trade-offs between remediation efficiency, secondary environmental impacts, and socio-economic outcomes. The key findings underscore the dominance of environmental health indicators—notably Disability-Adjusted Life Years (DALYs)—in overall weighting and highlight the critical influence of remediation technology selection on lifecycle impacts. The study validates the utility of a quantitative, multi-criteria approach in identifying optimal remediation strategies, facilitating cross-project comparability, and supporting the transition from cost-centric remediation toward value-driven, sustainable redevelopment.

CCUS technology or renewable energy for India’s net zero carbon emission mission? Fuzzy analytical hierarchy process

Background and Aim: Highly pathogenic avian influenza (HPAI) remains a global threat to poultry production, trade, and public health. While Morocco has not yet reported confirmed HPAI outbreaks, the endemic circulation of low-pathogenic avian influenza (LPAI) H9N2 since 2016, proximity to affected neighboring countries, and Morocco’s position along migratory bird flyways highlight the country’s vulnerability. This study aimed to identify high-risk areas for HPAI introduction and spread to inform risk-based surveillance and control policies. Materials and Methods: We applied a spatial multi-criteria decision analysis integrated with geographic information systems at the provincial scale. Relevant risk factors were identified through a literature review and expert consultation, and categorized into the introduction (wetlands, live poultry imports, recreational bird imports, and poultry products) and spread (poultry density and type, live bird markets, transport networks, and human population density) domains. Weights were assigned to factors using the analytic hierarchy process based on responses from 73 poultry-sector experts. Data were normalized, integrated into composite risk maps, and validated against historical LPAI H9N2 outbreak data (2016). Sensitivity and uncertainty analyses were used to assess model robustness. Results: The final maps revealed that 25 provinces (33.3% of the national territory) exhibited high-to-very high risk of HPAI introduction, particularly along northern coastal provinces, border regions, and areas linked to recreational bird imports. For spread risk, 41 provinces (41.3%) were classified as high to very high, concentrated in the Casablanca–Settat, Rabat–Salé–Kenitra, Fès–Meknès, and Marrakech–Safi regions, which are characterized by dense poultry production, major trade hubs, and extensive transport networks. Sensitivity analyses confirmed the model's stability, with variations in weight producing a minimal impact on risk classifications. Conclusion: This study provides the first comprehensive spatial risk maps of HPAI introduction and spread in Morocco, highlighting priority provinces for early detection, targeted surveillance, and preventive biosecurity measures. Despite limitations arising from reliance on LPAI data and expert judgment, the approach offers a robust decision-support tool for veterinary authorities. The methodology is adaptable to regional applications and can be refined with real-time surveillance data, enhancing Morocco’s preparedness and resilience against future avian influenza incursions. Keywords: avian influenza, biosecurity, geographic information system, Morocco, multi-criteria decision analysis, risk mapping, surveillance.

In 2023, Brazil’s steel production accounted for 1.7% of global steel output, ranking the country as the 9th largest producer worldwide. The country accounted for 54.84% of the regional production in Latin America. This economic situation presently coexists with environmental and social challenges inherent to the steel industry, stemming from the repercussions of its activities on the environment and human health and well-being. Thus, while the Brazilian steel sector is crucial for economic progress, an examination of the sustainable performance of these entities uncovers challenges and underscores the necessity of reconciling economic, environmental, and social factors to secure a sustainable future. Consequently, a classification system for the sustainable performance of Brazil’s three largest steel companies, centered on the Triple Bottom Line and grounded in criteria associated with corporate reports compliant with the Global Reporting Initiative (GRI) framework, is essential to elucidate the conduct of these companies. Consequently, utilizing 11 criteria (four economic, four social, and three environmental) derived from documentary research conducted between 2019 and 2021, this study formulates a framework for classifying the principal steel companies through the ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, integrated with the Gaussian Analytical Hierarchy Process (Gaussian AHP) for weight assignment, thereby eliminating the need for specialists and mitigating inherent subjectivity. The method’s application revealed a change in the classification of steel mills according to the criteria established by the research. The current study enables interested parties to assess organizational behavior and identifies areas for improvement to enhance sustainable performance rankings.

Flood susceptibility mapping in Kali River Basin, Southern India: A GIS-based analytical hierarchy process modelling

Integrated use of the analytical hierarchy process method for mapping areas susceptible to flooding in the urban area in a city in southwest Bahia, Brazil

Abstract Water resources planning must take into account a range of interconnected and uncertain factors, including climate, technology, economic conditions, environmental concerns, and political systems. Conventional approaches to water resources planning often fall short when dealing with deep uncertainty—particularly uncertainties driven by climate change. This study introduces a novel framework for planning new water infrastructure using Engineering Options Analysis (EOA) to identify the most promising and adaptive development pathways. The framework differentiates between deep and statistical uncertainties and evaluates alternatives across thousands of potential future scenarios using Net Present Value (NPV) analysis and the Monte Carlo Temporal Analytic Hierarchy Process (MC‐TAHP). To enhance decision‐making, the framework incorporates two key managerial options—delay and cancellation—enabling the identification of pathways that are both robust and flexible. The approach is applied to the Senegal River Basin, where it successfully identifies three flexible development pathways. The findings show that integrating flexibility into planning can substantially improve system robustness by hedging against the risks of undesirable outcomes while maintaining opportunities for high performance. For instance, adding flexibility to KBG development pathway (the sequential construction of the Koukoutamba, Boureya, and Gourbassi dams) simultaneously reduces downside risk by raising the 10th percentile aggregated performance score from 0.19 to 0.35, and enhances upside potential by increasing the 90th percentile score from 0.89 to 0.96.