To systematically enhance sustainability in automobile seat design and to holistically address the multi-dimensional requirements of the economic, environmental, and social dimensions under the Triple Bottom Line (TBL) framework, a data-driven design methodology integrating surrogate modeling with multi-objective evolutionary algorithms is proposed. First, user core needs are quantified and translated into 15 design features through SET (Social-Economic-Technical) factor analysis and the Fuzzy Analytic Hierarchy Process (FAHP). Subsequently, 10 key sustainability indicators are identified based on the TBL framework and categorized into economic, environmental, and social dimensions. Utilizing a dataset of 800 expert-student scoring records, global optimization of Random Forest (RF) hyperparameters is performed via Bayesian Optimization (BO), thereby constructing a high-accuracy surrogate model capable of predicting scores for any combination of design features across the identified sustainability indicators. The weighted composite scores for the three dimensions are adopted as optimization objectives, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to explore Pareto-optimal solution sets for design feature configurations. Through high-frequency feature statistics and SHapley Additive exPlanations (SHAP) interpretability analysis, critical design features and their contribution mechanisms to multi-dimensional sustainability are elucidated. A widely distributed Pareto front is obtained, and statistical analysis reveals that recyclable materials (D13) and modular design (D1) constitute core features simultaneously enhancing sustainability across all three dimensions. Furthermore, reinforced frames (D5) and anti-fouling/antibacterial materials (D15) are found to contribute significantly to the safety and health aspect within the social dimension. The values for high-performance materials (D14) and multi-functionality (D6) exhibit considerable variability, necessitating trade-offs contingent upon specific design objectives. Representative optimal solutions from the Pareto front are selected as quantitative benchmarks, and the optimized feature parameters are hierarchically translated into material selection, structural schemes, and functional configurations, thereby completing the conceptual design of an automobile seat. The proposed BO-RF-NSGA-II hybrid framework is demonstrated to systematically address multi-objective conflicts inherent in sustainable design, thereby providing quantifiable and interpretable decision support for designers and effectively advancing the synergistic innovation of green, human-centric, and economically viable automobile seat development.

ABSTRACT This study explores how high-ranking military officers conceptualize the fundamental dimensions of courage. Based on data collected from twenty officers serving at NATO’s Supreme Headquarters Allied Powers Europe (SHAPE) in Mons, Belgium, the research constructed a comprehensive hierarchy of courage dimensions that reflects the collective views of all participants and reveals statistically significant differences between Turkish officers and other NATO officers, grounded in cultural context. Fifteen pairwise comparisons relating to the three primary components of courage, (1) initial intent (2) risks, threats, and fears encountered during the process and (3) the outcomes of action, were evaluated using the Fuzzy Analytic Hierarchy Process (FAHP). Triangular Fuzzy Numbers (TFN) were employed to model comparisons, and both arithmetic mean and Fuzzy Geometric Mean (FGM) methods were used to minimize intra-group inconsistencies. Overall results indicate that officers primarily associate courage with individual intent, free will, and moral purpose. Risks encountered during the process and the outcomes of action were considered less decisive than the internal motivation underlying courageous behavior. Comparative analyses reveal that Turkish officers tend to prioritize idealistic dimensions such as fearlessness and free will, whereas the other NATO officers exhibit a more balanced and controlled approach. Mann-Whitney U-tests confirmed that many of these differences were statistically significant. These findings suggest that courage should be interpreted through a cultural lens to inform leadership development, mission planning, and psychological preparation in multinational military operations. Moreover, the study highlights the analytical flexibility and explanatory depth that fuzzy logic-based models offer when assessing subjective psychological constructs.

ABSTRACT Through systematic field investigation in the Helan Petroglyph Area, this study documented the developmental characteristics of 146 spalling disease cases alongside fundamental carrier rock properties. We established a comprehensive hierarchical model integrating three critical dimensions: rock physical-mechanical parameters, fracture development characteristics, and petroglyph spatial distribution patterns. The fuzzy analytic hierarchy process (FAHP) was implemented to compute precise indicator weights and generate quantitative spalling development scores (K), enabling systematic classification into three distinct risk tiers: moderate (100 cases), low (11 cases), and high (35 cases). Observed discrepancies between evaluated classifications and field conditions highlighted both methodological subjectivity in FAHP implementation and the complex etiology of spalling pathologies.Advanced machine learning approaches were subsequently employed, with C1-C11 indicators serving as input variables and K scores as targets. Two optimized neural network architectures GA-BP and PSO-BP demonstrated exceptional predictive capability, achieving determination coefficients (R2) exceeding 0.9 with minimal error metrics (MAE, MSE, RMSE approaching 0). Comparative analysis revealed the PSO-BP model’s superior accuracy in spalling prediction. Comprehensive correlation analysis through Pearson coefficients identified four predominant influencing factors: rebound strength (C1), particle density (C5), crack proximity (C7), and crack width (C9), providing valuable references for the conservation of petroglyph cultural heritage.

Commercial complex development is featured by large scale, complex operational procedures, and prominent challenges in cost management. This study proposes a data-driven intelligent approach for optimizing cost management of commercial complex projects. First,it emphasizes the necessity of applying value engineering (VE) principles—specifically integrating the Function Analysis System Technique (FAST)—in cost control. Subsequently,a model framework is constructed and workflow procedures for construction cost management are formulated,with the core methodology being the integration of VE with a Fuzzy Analytic Hierarchy Process (FAHP) model. The pre-analysis phase involves defining VE study objectives via FAST,using FAHP to determine functional coefficients,and leveraging systematic cost analysis tools.By calculating cost and value coefficients, the model realizes real-time monitoring, thus avoiding irrational construction behaviors and supporting post-implementation reviews for continuous optimization. To validate the model’s effectiveness,an existing commercial complex project was selected for optimization. Comparative analysis shows that traditional methods resulted in a total cost of 74,886,333.3 yuan across civil engineering,building construction,HVAC,and electrical engineering,while the optimized approach reduced costs to 72,740,121.5 yuan,achieving a 2.87% cost reduction.

The current product evaluation systems, constrained by fixed indicators and subjective weighting, struggle to adapt to dynamic market demands. This study proposes a data-driven framework integrating latent Dirichlet allocation, fuzzy analytic hierarchy process, and particle swarm optimization. User reviews and product parameters are collected from e-commerce platforms, with latent Dirichlet allocation extracting latent themes to build a multi-dimensional indicator system. The fuzzy analytic hierarchy process quantifies qualitative indicators to form an initial judgment matrix, while particle swarm optimization performs global optimization and consistency correction to minimize expert bias. An empirical case on smartwatches verifies the feasibility and effectiveness of the proposed framework, demonstrating its ability to capture user-centered demand characteristics and produce comprehensive evaluation results. The proposed evaluation system effectively combines demand insights with market adaptability, offering a robust theoretical and methodological foundation for product design optimization and differentiated strategy formulation.

This study develops Wire Arc Additively Manufactured (WAAM) Nickel-Stainless Steel bimetallic samples with silicon enhancement and investigates their face-milling machinability using a hybrid multi-criteria optimization framework. Seventeen machining trials were designed and conducted using a Response Surface Methodology (RSM)-based Central Composite Design (CCD) to evaluate the effects of cutting speed (8000-9500rpm), feed rate (0.1-0.25mm/tooth), depth of cut (0.5-1.5mm), and tool flute count (two and four flutes) on surface roughness (Ra), material removal rate (MRR), power consumption (Pc), and cutting force (CF). A three-stage hybrid methodology was implemented, in which RSM modelled machinability behaviour and identified significant process parameters, Spherical Fuzzy AHP (SF-AHP) assigned criteria weights under uncertainty, and Fuzzy MARCOS ranked the machining alternatives. Run 1 (9500rpm, 0.1mm/tooth, 1.5mm, four flutes) was identified as the optimal setting, exhibiting lower surface roughness (~ 0.6μm) and higher MRR (~ 35.6mm³/min), whereas Run 6 (8750rpm, 0.175mm/tooth, 1mm, two flutes) showed comparatively poorer performance with higher Ra (~ 0.8μm), reduced MRR (~ 34mm³/min), and less favorable power consumption and cutting force, indicating weaker overall machinability. Cutting speed and depth of cut strongly influenced Ra and MRR, while feed rate and flute configuration primarily affected Pc and CF. Validation through confirmation experiments, criteria-weight verification, and ranking consistency demonstrated strong agreement between predicted and measured performance. Although limited to a specific silicon-enhanced Ni-SS bimetallic system and finite experimental range, the proposed framework provides a practical and robust strategy for multi-response machinability optimization of WAAM components, offering industrial benefits such as improved surface finish, reduced power consumption, enhanced productivity, and better machining stability.

Uncontrolled municipal solid-waste dumpsites are major sources of environmental pollution in many developing regions, where the absence of engineered containment systems facilitates the migration of contaminants into surrounding ecosystems. Conventional pollution assessment approaches typically evaluate individual environmental media such as leachate, soil, or groundwater separately and therefore fail to capture the cumulative impacts of dumpsites across multiple environmental pathways. This study proposes a Pollution Potential Index (PPI) framework for integrated environmental assessment of municipal solid-waste dumpsites. The index combines quantitative indicators derived from soil, surface-water, and groundwater contamination with qualitative indicators representing ecological disturbance, noise pollution, and aesthetic degradation. The relative importance of each environmental pathway was determined using the Fuzzy Analytical Hierarchy Process (FAHP), which enables uncertainty and expert judgement to be incorporated through linguistic weighting. Pollution severity for each pathway was assigned using discrete impact magnitudes based on regulatory guideline exceedances for chemical parameters and expert-based scoring for qualitative impacts. The PPI was computed using a weighted aggregation model to generate a dimensionless score representing overall pollution potential. A field-based application using environmental data collected from an existing municipal solid-waste dumpsite in Kerala, India, demonstrated the operational procedure of the index and yielded a normalized PPI value of 63.5, indicating moderately high pollution potential. Surface-water contamination contributed the largest proportion to the overall index value, followed by soil contamination and ecological disturbance, while noise contributed minimally. The results demonstrate that the proposed PPI framework effectively integrates heterogeneous environmental indicators within a single metric and provides a transparent tool for comparative evaluation, prioritization of remediation efforts, and long-term environmental monitoring of dumpsites, particularly in regions where open dumping remains prevalent.

ABSTRACT Selecting liquefied natural gas (LNG) bunkering facilities is a complex multi-criteria decision-making (MCDM) challenge involving significant uncertainty. Decomposed Fuzzy Sets (DFS) manage this uncertainty by capturing decision-makers’ cognitive processes through functional and dysfunctional questions. This article introduces proportionality-based models for DFS for the first time. By integrating the reciprocal questioning mechanism of DFS with proportion-based elicitation, the proposed framework captures expert assessments more precisely. A novel proportional decomposed fuzzy AHP method is developed and applied to the LNG bunkering facility selection problem in maritime ports. Findings demonstrate that safety considerations heavily dominate this process; specifically, fire and explosion risk and emergency response accessibility emerge as the most critical sub-criteria. Ultimately, this study confirms the necessity of a risk-oriented, regulation-compliant approach when planning LNG infrastructure in complex port environments.

Climate-adaptive strategies and nature-based solutions for emission control supporting environmental policy and sustainable mining decision-making.

BackgroundThis study explored the optimal design of workplace emotional skills training, focusing on emotional blackmail, emotional intelligence, and emotional contagion. Emotional management is crucial to individual performance and organizational operations, particularly in high-stress or uncertain environments.ObjectiveTo identify key workplace emotional indicators, evaluate their importance, and provide practical recommendations for course design to improve employee well-being and organizational effectiveness in the high-tech industry.MethodsA modified Delphi method was employed to establish workplace emotional indicators, and the fuzzy analytic hierarchy process was then used to determine the indicators' relative weights. This study's expert panel included human resource managers, management personnel, and employees with substantial experience in the high-tech sector.Results"Cultivating emotional intelligence" (0.468) was the most crucial dimension, with "self-motivation" (0.320) and "interpersonal relationships" (0.209) being the key indicators. In the "recognizing emotional blackmail" dimension, the key indicators were "work stress management," "blackmailer characteristics," and "blackmail victim characteristics." The main focus of the "managing emotional contagion" dimension was "positive emotion cultivation," which supports team performance.ConclusionsEmotional intelligence is central to managing emotional blackmail and remaining resilient in high-stress environments. The present findings highlight the value of long-term emotional training programs, focusing on self-motivation and emotional regulation at the individual level and the cultivation of positive emotions at the organizational level. Employee turnover may serve as a contextual, long-term outcome, but short-term measures such as engagement and conflict reduction are more appropriate indicators of a program's effectiveness.

Rural women in developing countries often face significant barriers to employment, income generation, and access to productive resources. In Iran, medicinal plants (MPs) cultivation offers a sustainable pathway toward economic empowerment and livelihood resilience. This study develops and prioritizes strategies to enhance the livelihood sustainability of rural women through MPs cultivation using an integrated SWOT (Strengths – Weaknesses – Opportunities – Threats)–Fuzzy AHP (Analytic Hierarchy Process)–TOWS (Threats– Opportunities – Weaknesses– Strengths) model. Data collected from experts in agricultural and rural development sectors were analyzed to evaluate internal and external strategic factors. The strategic space analysis revealed that internal strengths (0.473) outweighed weaknesses (0.128), while external opportunities (0.325) surpassed threats (0.092), indicating that the favorable strategic space (O + S = 0.798) dominated the risky space (T + W = 0.220). Twelve strategies were formulated and prioritized, among which two emerged as most critical: (1) economic empowerment of rural women through home-based MPs processing enterprises and (2) promotion of greenhouse-based cultivation as a sustainable alternative to wild harvesting. The findings highlight the importance of leveraging indigenous knowledge, improving branding and packaging, and strengthening institutional support to achieve sustainable rural livelihoods. The proposed hybrid framework provides a replicable analytical tool for policymakers to design context-specific interventions linking women's empowerment, biodiversity conservation, and rural economic sustainability.

A robust multi-criteria decision framework for sustainable recycling of NCM lithium-ion battery cathodes under uncertainty and policy scenarios

Dryland watersheds are governed by tightly coupled source–sink dynamics, in which expanding bare soil and declining vegetated patches amplify runoff, sediment transport, and land degradation. Identifying suitable locations for water harvesting structures remains challenging due to the limited scalability of field assessments and the inability of coarse DEM-based GIS methods to capture critical microtopographic features. This study evaluates whether high-resolution (0.44 m) topographic data, integrated with multi-criteria decision analysis (MCDA), can identify suitable locations for water harvesting structures in dryland watersheds and compares the model discrimination of the Analytical Hierarchy Process (AHP) and the Fuzzy AHP (FAHP). Eight geomorphic and ecological indicators were evaluated and validated using 565 practitioner-identified restoration practice locations across two watersheds in southern New Mexico. The results show that 78% (East Control) and 94% (West Restoration) of validation sites occur within the top two predicted suitability classes, with moderate to good model discrimination (AUC: 0.671–0.723) and strong ranking performance (Boyce Index: 0.945–0.983). AHP and FAHP produced nearly identical outputs (ΔAUC < 1%; ΔBoyce ≤ 0.005). These findings demonstrate that high-resolution topography, coupled with MCDA, provides a robust and transferable framework for the landscape-scale prioritization of nature-based water harvesting structures to support ecohydrological restoration in dryland watersheds.

Comprehensive identification and prioritization of developed artificial intelligence methods for the detection and diagnosis of breast cancer can help to select proper techniques. This study aimed to introduce the best artificial intelligence techniques developed for the detection and diagnosis of breast cancer using microscopic images by fuzzy AHP-TOPSIS techniques. To identify the artificial intelligence techniques developed, a systematic search was performed in five reliable databases. After that, the Delphi method was applied to determine the proper criteria for selecting the best artificial intelligence techniques. To estimate the relative weights of the criteria, the fuzzy analytical hierarchy process (FAHP) method was used. In the next step, to prioritize the identified artificial intelligence techniques, the technique for order of preference by similarity to the ideal solution (TOPSIS) method was applied. Forty-four artificial intelligence techniques were identified. Seven selection criteria, validity, accuracy, comprehensiveness, processing time, cost, simplicity, and executive capability, were introduced. Ensemble deep learning architectures integrated with web of things (weight = 0.8041), the computer-aided diagnosis method (weight = 0.7774), the ensemble strategy (VGG16 - ResNet34 - ResNet50) (weight = 0.7475), automated tumor-stroma interface zone detection (weight = 0.7475), and MultiNet/Computer-Aided Diagnosis (CAD)-based deep learning model (weight = 0.7262) were selected as the best methods, respectively. These findings represent a total approach to the developed techniques which can be used for designing methods with better performance in the future.

Study on quantitative assessment of urban gas pipeline leakage risk using a TFN-BN hybrid model

Efficient and safe disassembly of waste electric vehicle batteries (WEVB) is critical for sustainable recycling and resource recovery. However, current studies rarely consider the impact of resource constraints in disassembly scenarios on determining the optimal disassembly sequence. To rationalize the disassembly of used power batteries, this paper proposes a human-robot collaboration (HRC) model aiming to minimize time, cost, hazard index, and energy consumption. Firstly, the dismantling resource space limitation is considered to limit the assigned tasks to one human and one robot, and shop floor operators and robots coordinates to disassembly in the HRC model. In this article, a multi-objective evolutionary algorithm with an adaptive genetic differential evolution algorithm based on successful history (AGDE-ASH), combined with external archiving, is proposed to solve the model which is compared with three other commonly used algorithms for the disassembly of battery of Tesla Model 1 to prove its effectiveness and feasibility. Finally, an entropy-weight fuzzy hierarchical analysis (EWFHA) method, which is a combination of fuzzy analytic hierarchy process (FAHP) and entropy-weight method (EWM), is proposed to evaluate the effectiveness of the proposed approach. Eight Pareto solutions obtained through AGDE-ASH is employed to determine the optimal dismantling order.

Integrating resilience and social sustainability in textile supply networks: A hybrid fuzzy AHP–DEMATEL approach

A hybrid methodological framework for flood risk assessment in data-scarce semi-arid basins: Integrating fuzzy AHP and climate scenarios in the Hodna Basin, Algeria

Construction inspection is found to be very useful in maintaining the quality of transportation and highway construction projects. Traditional inspection practices are generally based on fixed inspection intervals and regular inspection techniques, which may not be sufficient to handle construction activities with high risk of quality defects. Limited inspection resources and poor quality inspection systems are also contributing to the poor performance of traditional inspection practices. This study proposes a risk-based construction inspection and quality management model for transportation and highway infrastructure projects. The framework integrates risk identification, multi-criteria risk assessment using the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), inspection prioritization through a Risk Priority Index (RPI), and construction quality evaluation using a Quality Performance Index (QPI). Construction quality risks are categorized into material, construction process, environmental, and management factors, and their relative importance is evaluated through expert assessment. The results indicate that construction process risks and material variability represent the most significant contributors to highway construction quality deficiencies. The suggested risk-based inspection model utilizes inspection resources depending on the calculated risk level for efficient inspection of high-risk construction activities. The framework supports earlier detection of construction defects and improved quality performance compared with conventional inspection systems. This study provides a structured approach for integrating risk assessment, inspection planning, and quality evaluation in highway infrastructure construction management.

Topological Fuzzy Analytic Hierarchy Process (ToF-AHP) for geospatial assessment of malaria vulnerability in Nigeria.