Search Results (990)

In the global trend of museums transitioning from static displays to digital, narrative, and experiential forms, heritage museums face challenges such as weakened cultural identity, insufficient emotional resonance, and the separation of reality and fiction. To address these issues, this study, based on the theory of spatial narrative, introduces the tripartite theory of spatial production to jointly construct a narrative experience model with overlapping time and space. By expanding the dimensions of time and space, it achieves a deep correspondence of virtual experiences, providing guidance for the virtual-real integration experience design of heritage museums. Methodologically, a combined approach of FAHP1-spatial syntax-FAHP2-FCE is adopted. Taking the Panlongcheng Heritage Museum as an example, with user experience needs as the starting point and the analysis results of the physical exhibition space as the basis, the heritage culture theme serves as the narrative thread, integrating into an experiential model with contextual virtual-real fusion. Finally, the design practice is verified through FCE. The results show that this model can optimize the virtual-real integration experience, enhance users’ cultural identity and emotional resonance, and provide beneficial insights for the digital and experiential transformation of heritage museums. Full article

Rockburst is a dynamic disaster that frequently occurs in hard and brittle rock tunnels under high in situ stress conditions. It is influenced by multiple factors, including lithological condition, in situ stress condition, and surrounding rock mass structural condition. Rockbursts are highly destructive and difficult to predict accurately. At present, many methods have been proposed for predicting rockburst proneness. However, the above methods suffer from a lack of diversity, limited applicability, and low predictive accuracy. Therefore, based on the Analytical Hierarchy Process (AHP) method and fuzzy mathematics theory, the eight evaluation indexes (strength brittleness index, stress coefficient, elastic energy index, surrounding grade, etc.) were selected to establish the new AHP fuzzy comprehensive evaluation model. Based on the field case studies, the feasibility and accuracy of the model were validated. The results indicate that the proposed multi-index prediction model demonstrates strong feasibility and high predictive accuracy, and the model has promising application prospects. Meanwhile, the 13 recognized evaluation indexes were summarized, and an approach for accurate rockburst prediction was proposed. The predicting model and predicting approach proposed in this paper are of great significance for improving the accuracy of rockburst prediction. Full article

Using used serviceable material (USM), recycled and upcycled, for aircraft is environmentally and financially beneficial in helping the aviation industry achieve sustainability. This study aims to identify determinants of aircraft USM value and assess their significance using the Fuzzy Analytic Hierarchy Process (FAHP) to gain insights for making the USM market more active. Sixteen factors in four categories are selected based on literature and focus group interviews. A survey to analyze factor priority is conducted with 118 industry experts. The results show that maintenance requirements, airworthiness directive status, and maintenance status from the technical category are the most critical determinants of aircraft USM value, followed by traceability, former operator, and former aviation authority from the operational category and new part value. The technical category corresponds to “must-be” traits in the Kano model, requiring compliance by sellers, whereas new part value information can help buyers’ decisions. The implementation of an internationally agreed mutual accreditation system for approved maintenance organizations and a standard for aircraft dismantling is proposed to improve technical and operational determinants to achieve fewer uncertainties in USM valuation. This study aims to offer a new guideline for evaluating USM value to market participants. Price modeling of USM is left for future studies. Full article

Sustainable Development (SD) has increasingly become a core strategic direction. This study centers on the automotive industry, serving as the primary focus for both research and empirical analysis. If Taiwan’s automotive industry can successfully achieve transformation, it will generate a more advanced multiplier effect on the overall development of Taiwan’s industries. The study confirms that Lean Management (LM) and SD can effectively produce synergistic effects. Digital Transformation (DT) is increasingly recognized as a key driver of future business development. Exploring the interrelationships among SD, LM, and DT presents a strategic and practical research direction. Findings from this study suggest that integrating LM and DT can generate synergies that enhance resource efficiency, minimize waste, and improve both environmental and social performance. The primary objective of this study is to develop a structured framework connecting SD, LM, and DT by utilizing the House of Quality (HoQ) from the Quality Function Deployment methodology. The research employs multiple attribute decision-making techniques, including the Fuzzy Delphi Method, the Fuzzy Analytic Hierarchy Process, and the Compromise Ranking Method. By constructing and analyzing two HoQs, the study identifies key LM practices and DT technologies that serve as critical strategies for advancing SD performance. Finally, with regard to LM practices, no previous research has attempted to conduct a hierarchical classification. This study is the first to construct a hierarchical structure for Just-in-Time and Jidoka. Full article

The Strait of Malacca faces persistent maritime safety challenges due to high vessel density and complex navigational conditions. Current risk assessment methods often lean towards treating static accident analysis and dynamic traffic modeling separately, although some nascent hybrid approaches exist. However, these hybrids frequently lack the capacity for comprehensive, real-time factor integration. This study proposes an integrated framework coupling accident hotspot identification with vessel traffic network analysis. The framework combines trajectory clustering using improved DBSCAN with directional filters, Kernel Density Estimation (KDE) for accident hotspots, and Fuzzy Analytic Hierarchy Process (FAHP) for multi-factor risk evaluation, acknowledging its subjective and region-specific nature. The model was trained and tuned exclusively on the 2023 dataset (47 incidents), reserving the 2024 incidents (24 incidents) exclusively for independent, zero-information-leakage validation. Results demonstrate superior performance: Area Under the ROC Curve (AUC) improved by 0.14 (0.78 vs. 0.64; +22% relative to KDE-only), and Precision–Recall AUC (PR-AUC) improved by 0.16 (0.65 vs. 0.49); both p < 0.001. Crucially, all model tuning and parameter finalization (including DBSCAN/Fréchet, FAHP weights, and adaptive thresholds) relied solely on 2023 data, with the 2024 incidents reserved exclusively for independent temporal validation. The model captures 75.2% of reported incidents within 20% of the study area. Cross-validation confirms stability across all folds. The framework reveals accidents concentrate at network bottlenecks where traffic centrality exceeds 0.15 and accident density surpasses 0.6. Model-based associations suggest amplification through three pathways: environmental-mediated (34%), traffic convergence (34%), and historical persistence (23%). The integrated approach enables identification of both where and why maritime accidents cluster, providing practical applications for vessel traffic services, risk-aware navigation, and evidence-based safety regulation in congested waterways. Full article

Climate change is expected to negatively impact agricultural production, leading to phenological and metabolic changes, increased water demands, diminished yields, and changed organoleptic characteristics, restricting the positive geographic productivity potential. As an adaptive strategy, agriculture in mountainous regions has gained prominence despite the fact that it entails new challenges. Indeed, mountain-specific conditions and limitations need to be considered, compared to the traditional productive regions. Consequently, there is a lack of information about the most suitable locations because the new conditions and limitations need to be accounted for. This study provides a crop suitability assessment approach to be used in mountainous regions where data about crop yield or development is scarce or nonexistent. Specifically, we evaluated the suitability of vineyards and apple orchards in the southern Pyrenees and Pre-Pyrenees. Using Geographical Information System (GIS) techniques, integrated with fuzzy logic and the Analytic Hierarchy Process (AHP), we combined traditional climatic, soil, and topographic indicators with factors relevant to mountainous regions. Our results indicated that the most suitable areas were primarily in lower basins and sunny hillsides, with smaller water needs. Vineyards would benefit from a very low risk of late spring frosts and elevated solar radiation, whereas apple orchards from a reduced risk of hailstorms, a very low risk of late spring frosts, and mild slopes. The fuzzy membership functions combined with the AHP facilitated the integration of indicators, effectively identifying areas with high potential for crop development. This approach contributes to landscape management and planning by offering a modifiable tool for assessing crop suitability in mountainous regions. Full article

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 10⁴ t) and 78% of the annual oil production (738.2 × 10⁴ t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 10⁴ t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 10⁴ t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields. Full article

Iran is a uniquely compelling case due to its ancient and diverse culinary heritage, coupled with a strategic national mandate to significantly boost tourism, making the development of this high-impact sector a crucial policy imperative. The present study adopts a scenario planning approach to first identify the key factors influencing food tourism in rural areas of Iran, then explores plausible future scenarios for rural tourism development, and finally ranks strategic alternatives for enhancing food tourism in these regions. Methodologically, the research combines a goal-oriented, descriptive-analytical approach with future study techniques. Data for the initial phase were collected through a literature review, field studies (surveys, interviews), and expert surveys, and subsequently analyzed using MICMAC and ScenarioWizard software tools. Strategic alternatives were then evaluated using Picture Fuzzy Sets (PFSs) and the COPRAS method based on six critical factors. The findings reveal that six primary factors—promotional activities, pricing, food quality, infrastructure, government support, and investment—play pivotal roles in advancing food tourism in rural Iran. Based on these six primary factors, the study constructs three future scenarios: optimistic, stagnant, and crisis-driven scenarios. In the third phase of the analysis, employing Picture Fuzzy COPRAS and Picture Fuzzy Analytic Hierarchy Process (PF-AHP), the results indicate that “food festivals and promotional campaigns” carry the greatest weight and are deemed the most influential in attracting tourists, whereas “investment” ranks the lowest. Following normalization and application of weights, COPRAS analysis identifies “improving the quality of tourism infrastructure” as the most effective strategy, receiving the highest score (464.0620). A sensitivity analysis further confirms that the overall ranking of the strategies remains stable despite changes in the criteria weights, with only minor shifts observed among mid-ranked alternatives. These results offer policymakers a practical decision-making tool to allocate limited resources efficiently and focus on high-impact strategies that support the sustainable development of food tourism in Iran’s rural areas. Full article

Deteriorating check dams pose significant threats to human safety and property, while impeding eco-environmental restoration in soil–water conservation systems in vulnerable watersheds like the Jiuyuangou Basin on China’s Loess Plateau. This study aimed to develop a comprehensive risk assessment framework for the check dam system in the Jiuyuangou Basin, China, to mitigate its threats to safety and eco-environmental restoration. A multi-index and multilevel risk evaluation system was established for check dam systems in the Jiuyuangou Basin, utilizing data gathering, hydrological statistics, numerical computation, and various methodologies. The index weights were determined via the fuzzy analytic hierarchy process with an integrated modeling framework for key parameters. Finally, the risk level of the check dam system in the Jiuyuangou Basin was assessed based on the comprehensive score. The results show that (1) nearly half of the check dams are at mild risk, approximately 25% are at moderate risk, and a few are basically safe. (2) Among various types of risk, the distribution of engineering risk is relatively uniform, environmental risk is generally high, loss risk is relatively concentrated, and management risk is particularly prominent. This research provides a scientific foundation for optimizing check dam governance, enhancing sediment control, and strengthening ecological service functions in vulnerable watersheds. Full article

In the era of digital intelligence, information literacy (IL) competency has become a critical indicator for measuring the comprehensive quality and sustainable development potential of university’s education. Using Yancheng Institute of Technology as a case study, this study systematically elucidates the connotation and current development status of college students’ IL within the framework of sustainable development. An evaluation index system is constructed, comprising four dimensions: information awareness and attitude, information ethics, law and security, information knowledge and skills, and information integration and innovation. The study employs the Analytic Hierarchy Process (AHP) to determine the weights of indicators at various levels and integrates the Fuzzy Comprehensive Evaluation Method (FCEM) to establish a quantitative assessment model for IL competency. Empirical research demonstrates that the proposed model effectively enables a multidimensional and quantitative evaluation of students’ IL, with results that exhibit sound scientific validity and applicability. Based on the analysis, specific strategies are proposed to enhance students’ IL from the perspectives of curriculum design, teaching models, and library services, thereby providing theoretical references and practical pathways for advancing informatization and sustainable development in higher education. Full article

Frequent water and mud inrush accidents during karst tunnel construction severely impact tunnel construction safety, environmental sustainability, and the long-term use of infrastructure. Therefore, conducting practical risk assessment for karst tunnel water and mud inrush is crucial for promoting sustainable practices in tunnel engineering, as it can mitigate catastrophic events that lead to resource waste, ecological damage, and economic loss. This paper establishes an improved weighted cloud model for karst tunnel water and mud inrush risk to evaluate the associated risk factors. The calculation of subjective weight for risk metrics adopts the ordinal relationship method (G1 method), which is a subjective weighting method improved from the analytic hierarchy process. The calculation of objective weight employs the improved entropy weight method, which is superior to the traditional entropy weight method by effectively preventing calculation distortion. Game theory is applied to calculate the optimal weight combination coefficient for two computational methods, and cloud model theory is finally introduced to reduce the fuzziness of the membership interval during the assessment process. This study applied the established risk assessment model to five sections of the Furong Tunnel and Cushishan Tunnel in Southwest China. The final risk ratings for these sections were determined as “High Risk,” “High Risk,” “Medium Risk,” “High Risk,” and “Moderate Risk”, respectively. These results align with the findings from field investigations, validating the effectiveness and reliability of the cloud model-based mud and water outburst risk assessment using combined weighting. Compared to traditional methods such as fuzzy comprehensive evaluation and entropy weighting, the evaluation results from this study’s model demonstrate higher similarity and reliability. This provides a foundation for assessing mud and water outburst hazards and other tunnel disasters. Full article

Slope instability may cause severe casualties, property losses, and ecological damage. To accurately evaluate slope stability grades and mitigate geological hazards, a dynamic stability assessment method based on variable weight theory and trapezoidal cloud model is proposed. First, an evaluation index system for slope stability is established following the principles of uniqueness, purposefulness, and scientific validity. Then, to improve the accuracy of subjective constant weights, the intuitionistic fuzzy analytic hierarchy process (IFAHP) is employed to calculate subjective constant weights. Considering the contrast intensity and conflict among indicators, an improved CRITIC method is applied to determine objective constant weights. To balance subjective and objective factors and avoid constant weight imbalance, the optimal comprehensive constant weights are computed based on game theory, effectively reducing bias caused by single weighting methods. Furthermore, to fully account for the influence of indicator state values on their weights, variable weight theory is introduced to dynamically adjust the comprehensive constant weights. Finally, based on the variable weights of evaluation indicators, a trapezoidal cloud model is utilized to construct the slope stability evaluation model, which is validated through an engineering case study. The results indicate that the stability grade of Stage 1 is assessed as basically stable, while Stages 2 and 3 are evaluated as stable. Numerical simulations show the safety factors of the three stages are 1.36, 1.83, and 2.36, respectively, verifying the correctness of the proposed model. The proposed model demonstrates practical engineering value in slope stability assessment and can be referenced for slope reinforcement and hazard prevention in later stages. Full article

With the designation of the first cohort of national parks and the continued operation of remaining pilots, China’s national park reform has entered a critical stage requiring consolidation and adaptive improvement. A key challenge lies in the ambiguous status of five pilot zones, which lack a standardized evaluation mechanism to guide decisions on future inclusion or exit. This study develops a comprehensive indicator system specifically tailored to assess the construction and development of national park pilots, thereby supporting evidence-based governance beyond initial entry criteria. Drawing on relevant theories and China’s institutional context, the framework employs Analytic Hierarchy Process, expert consultation, and fuzzy scoring to determine indicator weights and evaluation standards. The resulting system integrates three dimensions—ecological protection system, management system, and public service system. Nanshan National Park was selected as a case study, scoring 87.77 in 2024 (Class II, “Proficient”), with strong overall performance but notable weaknesses in landscape connectivity, recreational product diversity, and regional integration. These findings suggest the need for targeted improvements in ecological corridors, service enrichment, and community benefit-sharing. Overall, the proposed framework provides a replicable tool for evaluating pilot zones, offering practical insights for refining China’s national park development and enhancing governance effectiveness. Full article

To address persistent challenges in tennis—such as inefficient ball retrieval, the high cost of serving equipment, and difficulties in scheduling matches—this study proposes the design of a shared tennis service robot aimed at improving user experience and validating design feasibility. Grounded in user experience theory, user requirements were collected through questionnaires and structured interviews. The Analytic Hierarchy Process (AHP) was adopted to construct a hierarchical model of requirements. Weighted calculations were then applied to quantify and rank user needs. Design solutions were then derived based on these rankings. To evaluate the solutions, the Fuzzy Comprehensive Evaluation (FCE) method was utilized for multidimensional assessment. The results show that AHP identified three core requirements: intelligent ball retrieval, intelligent serving, and personalized serving parameter customization. Guided by these priorities, the proposed design integrates a shared rental model with multisensory interactive feedback. The final evaluation yielded an FCE score of 87.83, confirming the effectiveness of the solution. The combined AHP-FCE method provides a systematic framework for quantifying user needs and objectively evaluating design alternatives. It also offers a methodological foundation for the development of sports service robots. The shared tennis robot effectively reduces labor and operational costs while enhancing the overall user experience. Full article

Arid regions in Central Asia face persistent and increasing water scarcity, with groundwater serving as the primary source for drinking water, irrigation, and industry. The effective exploration and management of groundwater resources are critical, but are constrained by limited monitoring infrastructure and complex hydrogeological settings. This study investigates the Akbakay aquifer, a representative area within Central Asia with challenging hydrogeological conditions, to delineate potential zones for fresh groundwater exploration. A multi-criteria decision analysis was conducted by integrating the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), supported by remote sensing datasets. To address the subjectivity of weight assignment, the AHP results were further validated using Monte Carlo simulations and fuzzy logic aggregation (Fuzzy Gamma). The integrated approach revealed stable high-suitability groundwater zones that consistently stand out across deterministic, probabilistic, and fuzzy assessments, thereby improving the reliability of the groundwater potential mapping. The findings demonstrate the applicability of combined AHP–GIS methods enhanced with uncertainty analysis for sustainable groundwater resource management in data-scarce arid regions of Central Asia. Full article