Search Results (4,085)

Protecting and preserving the agricultural heritage, folk culture and ecological environment of traditional villages is a key element in advancing the strategy for comprehensive rural revitalisation. This paper constructs a theoretical framework for tourism symbiosis, examines the level of tourism symbiosis in the 13 national-level traditional villages of Jia County, and proposes strategies for tourism development. This study employs the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, alongside spatial analysis techniques such as the Hotspot Analysis, to reveal the levels of tourism symbiosis in traditional villages and their spatial distribution. The results indicate that traditional villages are distributed along the Yellow River, with a linear clustering pattern particularly evident in the central region of Jia County; the overall level of symbiosis exhibits a spatial pattern of higher levels in the north and lower levels in the south, with uneven levels across various dimensions; The traditional villages are categorised into four symbiotic models: comprehensive advantage-led, cultural corridor-dependent, ecological and cultural tourism potential, and low-development conservation. Based on these categories, strategies are proposed to deepen the exploration of local culture, promote industrial integration and regional collaboration, prioritise ecological conservation and environmental restoration, and establish distinctive brands through the rational utilisation of surrounding resources. The research framework and conclusions of this paper provide methodological references and practical insights for the concentrated and contiguous protection of traditional villages, as well as for research on rural revitalisation and sustainable development. Full article

This research introduces a novel methodology that combines Building Information Modelling (BIM) and Economic Multi-Criteria Decision-Making (EMCDM) with Neural Networks to optimize hybrid renewable energy systems in small communities. Its core aim is to improve sustainability, technical performance, and financial vokiability through integrated modelling and decision-making. The approach is applied to a hydropower site, evaluating five Scenarios (IDs 1–5) under a Community and Industry model. Financial benchmarks include a 10% Minimum Required Return and a 7-year payback period. ID3—hydropower, solar, and wind—proves most effective, with ANPV of €10,905 (wet) and €4501 (dry), and ROI of 155%/64%. Its ROIA/MRA Index peaks at 539%, and Payback/N ratios remain within acceptable limits (55%/96%). LCOE stays stable in average conditions (0.042–0.046 €/kWh), rising in dry years (0.07–0.10 €/kWh). Profitability differences primarily stem from demand and curtailment, rather than production costs. The NARX neural network reliably models SS% values from renewable inputs with low error across scenarios. The integrated BIM–EMCDM framework ensures transparent, sustainable, and risk-balanced energy system decisions for long-term autonomy. Full article

Existing studies conduct general cost analyses for prefabricated components, yet structural heterogeneity results in distinct cost drivers. Most studies concentrate on the technical performance of prefabricated staircases, with insufficient investigation into dedicated cost-estimation methods. This study establishes a hybrid prediction framework integrating GAN-based data augmentation and SHAP-empowered Multilayer Perceptron (SHAP-MLP) modeling, using prefabricated straight staircases as empirical objects for multidimensional analysis. Total cost is classified into production, transportation, and on-site installation phases, followed by systematic screening of 33 influencing factors for predictive modeling. The Analytic Hierarchy Process (AHP), with a 1–9 scale, is adopted to quantify indicator weights and prioritize features. Triple verification (multi-expert consistency test, group opinion coordination test, and sensitivity analysis) removes five weakly correlated parameters to form a preliminary indicator system. Based on 240 original engineering data samples, the GAN generates 60 high-fidelity synthetic samples. Distribution consistency between synthetic and original data is validated via the Kolmogorov–Smirnov (KS) test, p-value verification, and kernel density estimation (KDE). SHAP interpretability analysis identifies four core determinants: prefabrication rate, total staircase area, standardization level, and number of floors. Eight low-impact parameters are excluded to optimize model input, leaving 20 validated indicators. The GAN-SHAP-MLP model maintains superior performance in testing, with a test-set RMSE of 49.538, representing improvements of 41.3%, 22.5%, and 25.7% over LSTM (89.33), CNN (67.59), and standard MLP (70.56), respectively. The difference between its test-set and overall R² is only 0.69%, significantly lower than 2.06% for LSTM and 5.47% for MLP. Empirical validation with real engineering cases from four different regions further confirms the model’s high prediction accuracy, with a minimum error of only 1.49%. The integration of data augmentation and interpretable deep learning provides a high-precision, interpretable cost prediction tool for prefabricated straight staircases, promoting methodological progress in construction economics. Full article

Soil erosion is a significant environmental concern in arid regions, particularly in dam-regulated watersheds, where intermittent flows from sprinkler irrigation can exacerbate land degradation. This study assesses soil erosion susceptibility in the Sidi Aich watershed using a combined approach of the Revised Universal Soil Loss Equation (RUSLE) and the Analytic Hierarchy Process (AHP), enabling the integration of both regional characteristics and expert-driven weighting. The RUSLE model accounts for natural and human-induced factors, whereas AHP provides a hierarchical weighting system that highlights rainfall erosivity and the local impacts of dam-regulated discharges. Results show that 26.12% of the area falls into the very high susceptibility category, 25.45% into high, 23.91% into moderate, and 24.51% into low susceptibility. Model validation demonstrates satisfactory predictive performance, with Area Under the Curve (AUC) values of 0.85 for AHP and 0.78 for RUSLE. Overall, the findings emphasize the critical role of dam-controlled releases in increasing soil vulnerability, a factor that may not be fully captured when using RUSLE alone. By combining RUSLE and AHP, this research provides a more realistic and regionally tailored assessment of erosion risk, offering valuable guidance for watershed management and erosion mitigation strategies in arid environments. Full article

Quality evaluation in production systems represents a significant challenge in the manufacturing industry, particularly in environments where expert judgment plays a key role in managing the inherent uncertainty of the production system. This study proposes a fuzzy multicriteria decision-making control chart, termed Fuzzy Decision-Making Control Chart based on AHP-Extent and Triangular Fuzzy Numbers (FDMCC-AHPE). The method integrates expert knowledge through triangular fuzzy numbers and a Fuzzy Analytic Hierarchy Process supported by Extent Analysis, to define fuzzy decision intervals for quality assessment and subsequently perform a structured analysis to classify the product within a control chart framework. In this framework, expert judgments expressed through linguistic evaluations are systematically translated into triangular fuzzy numbers and processed using FAHP–Extent Analysis, allowing the aggregation of subjective assessments within a structured mathematical decision model. The proposed method was validated in a tannery company, specifically in the retanning process. The industrial case study considers both qualitative criteria, such as surface defects and color uniformity, and quantitative process variables that include bath pH, treatment duration, and processing temperature. The results were compared with an empirical expert-based evaluation and a structured expert assessment supported by a multicriteria decision-making method. The findings demonstrate that the FDMCC-AHPE exhibits greater sensitivity in discriminating between quality states under uncertain evaluation conditions, particularly when samples involve complex evaluation conditions. Full article

Translating diverse stakeholders’ needs for tourism into precise technical requirements for mobile resilience applications is a significant challenge, especially for at-risk coastal communities. Therefore, we developed a structured decision-support framework that uses the Analytic Hierarchy Process (AHP) combined with Multi-Criteria Decision Analysis (MCDA) to systematically identify and prioritize functional features for a disaster-resilient tourism application called AuTour. The framework was validated through a case study in Aurora Province, Philippines, involving 152 diverse stakeholders, including government officials, tourism operators, and technology students. The AHP analysis results revealed that safety infrastructure (a mean weight of 0.5256) was the dominant design criterion, far outweighing environmental sustainability (0.2480) and community preparedness (0.1241). The MCDA ranked key functional modules using these criteria to determine an optimal system architecture. The highest-priority features identified were a real-time Disaster Preparedness Alert module, a geospatial Smart Tourism Guide, and a participatory Health Surveillance module. The analysis results confirmed high utility for features incorporating AI-powered chatbots (a mean score of 4.1921) and multi-dialect communication capabilities (4.1513). The developed scalable, data-driven framework can be used for user-centered design in the critical domain of disaster-resilient technology. By translating stakeholder priorities into a ranked set of technical specifications, the framework contributes to the development of resilient mobile systems, supporting the achievement of Sustainable Development Goals for innovation (SDG 9) and resilient infrastructure (SDG 11). Full article

This article examines how algorithmic classification systems participate in the production of meta-identities, understood as operational classificatory constructs that mediate the visibility, circulation, and interpretation of digital content and its authors. The study employs a mixed-methods design combining controlled analytical simulation with qualitative interpretive analysis, systematic thematic coding, and comparative statistical procedures. Empirical data are derived from the analysis of 150 audiovisual works produced in formative workshops and interpreted by four types of agents: authors, peers, specialized human analysts, and two Large Language Model-based AI systems (ChatGPT and Gemini). Interpretations were analyzed across micro, meso, and macro levels, using a consolidated system of thematic categories with hierarchical weighting and normalization procedures to ensure inter-agent comparability. The results demonstrate a systematic and structural divergence between human and algorithmic classifications. While human agents preserve semantic plurality and contextual anchoring, AI systems tend to reorganize thematic hierarchies through semantic aggregation and stabilization, thereby privileging broad, reusable categories. This process produces recurring, opaque classificatory patterns that serve as infrastructural references for subsequent algorithmic decisions. The article contributes methodologically by offering a replicable framework for comparing human and algorithmic regimes of meaning production in digital environments. Full article

In ethics-sensitive product development, Generative AI can improve the efficiency of concept generation, but it also raises challenges related to accountability, value alignment, and decision transparency. To address limitations in current human-AI co-design processes, including unclear allocation of decision-making authority, insufficiently structured translation from design requirements to design constraints, and limited explainability in scheme evaluation, this study proposes an explainable Human–Computer Interaction (HCI)-based decision support framework for human-AI co-design, termed GAGT. The framework integrates Generative AI with multi-criteria decision-making methods. Specifically, the Analytic Hierarchy Process (AHP) is used to structure design requirements and determine their priorities, Grey Relational Analysis (GRA) is used to compare candidate schemes, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to support transparent final ranking. Within the framework, human designers are mainly responsible for requirement confirmation, priority judgment, review at key checkpoints, and final scheme selection, while AI mainly supports information organization, candidate scheme generation, and quantitative comparison. The framework was applied to the design of a community medical vehicle through a small-sample, case-based, quasi-experimental study. Compared with the human-only condition, the GAGT-supported condition reduced design time by 56.1%. Compared with the AI-autonomous condition, it showed no observed HIPAA violations and a Value Drift Index of 16.1%, indicating better consistency with human-defined priorities. The results suggest that the proposed framework may improve design efficiency while supporting clearer human oversight and decision explainability in Generative AI-assisted design, and may provide a structured approach to organizing human and AI roles in ethics-sensitive design tasks. Full article

Despite advances in engineering, fire safety improvements have plateaued in developed nations, necessitating a reassessment of resource allocation. This study develops a comprehensive fire safety assessment model for the Polish context using the Analytic Hierarchy Process (AHP). A panel of ten experts—comprising fire safety inspectors, State Fire Service officers, and architects—evaluated safety through a two-dimensional framework: the Fire Hazard Index (FHI) and Fire Safety Index (FSI). The results reveal a critical asymmetry: human factors (0.228) and combustible materials dominate the hazard landscape, whereas intelligent AI/IoT systems (0.4133) and passive protection (0.2113) offer the highest potential for safety enhancement. A novel “convergence–divergence” phenomenon was identified: hazard-focused assessments produce convergent priorities across building types (span 0.116), implying universal mitigation needs (e.g., education), while protection-focused assessments yield divergent priorities (span 0.250), justifying targeted investment. Specifically, healthcare facilities (ZL II) require disproportionate protection investment (priority 0.310). The study concludes that sustainable fire safety strategies must combine universal hazard mitigation with targeted technological interventions, offering a data-driven framework for policy optimization in Poland. Full article

The people-oriented city serves as the value orientation of urban work in the new era, and age-friendliness is precisely its core practical standard for intergenerational equity and inclusive sharing. Currently, the renovation of old residential areas should transcend single-dimensional physical patching and shift towards an all-age-friendly model that meets the complex needs of multi-age groups. Taking Tuanjiehu Communities in Beijing as a case study, this research constructs an evaluation system covering three dimensions—place, atmosphere, and culture—and 22 third-level indicators, and adopts the Semantic Differential Method (SD) and Analytic Hierarchy Process (AHP) to quantitatively analyze residents’ perceptions. The study finds that old residential areas generally suffer from problems such as “insufficient place safety and functionality, lack of atmospheric vitality, and weak cultural cultivation”. Based on these findings, a progressive renewal strategy of “Consolidating Safety Foundation → Boosting Community Vitality → Cultivating Community Culture” is proposed, offering an empirical illustration for the all-age-friendly renovation of high-density urban old residential areas to transform from “survival-oriented” spaces to “life-oriented” homes, offering preliminary insights for the all-age-friendly renovation of similar high-density urban old residential areas. Full article

In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R² = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts. Full article

The conservation of traditional villages has shifted from isolated site-by-site protection to regional collaboration, and exploring pathways for their sustainable development has become a key focus of research. Existing research still falls short in areas such as the integration of heritage value into decision-making mechanisms and the establishment of systematic conservation frameworks, leading to prominent issues of isolated conservation and homogeneous development. Taking traditional villages in Yanchuan County, China, as a case study, this research aims to establish a clustered conservation system and achieve a transition towards networked collaborative governance. The study utilised field surveys and literature review to establish a database and systematically catalogue heritage resources; it combined the Analytic Hierarchy Process (AHP) and the Delphi method to construct a value evaluation system and identify distinctive features; and it integrated cluster theory with GIS spatial analysis to construct a clustered conservation framework across three dimensions: classification and grading, symbiotic models, and the overall spatial pattern. The results indicate that: (1) the spatial distribution of villages in Yanchuan County is uneven, and the villages themselves exhibit significant homogeneity in their characteristics; (2) core characteristics include Loess culture, cave dwellings and revolutionary heritage sites, with comprehensive scores ranging from 0.4437 to 0.9116; these are classified into three protection levels, identifying five categories of villages of value. (3) Five major cluster zones were delineated based on resource and spatial characteristics. By integrating river basins and transport corridors, a comprehensive protection framework of ‘one belt, two wings, two centers and five zones’ was established, alongside three types of cluster symbiosis models, thereby achieving regional resource integration and enhancing collaborative efficiency. The cluster-based protection system proposed in this study can effectively address the challenges facing the conservation and development of traditional villages, providing a feasible solution for regional collaborative protection, and holds practical significance for cultural heritage management and sustainable development. Full article

Mill relining manipulator is essential maintenance equipment used to replace liners in a grinding mill. However, its excessive structural weight significantly constrains maneuverability and operational efficiency. To address this problem, this paper proposed a lightweight design framework for the manipulator’s upper arm, integrating improved multiple operating conditions topology optimization with size optimization. Firstly, a finite element model of the manipulator was established in ANSYS Workbench 2022R2. The loads under the corresponding operating conditions were extracted and applied to the finite element model of the upper arm to perform multi-condition finite element simulations. Secondly, a mathematical model for multi-condition topology optimization was developed using the variable density method combined with the Analytic Hierarchy Process (AHP), and the weight coefficients for each operating condition were determined. Finally, a combined response surface methodology (RSM) and genetic algorithm (GA) approach was employed to optimize the structural parameters of the upper arm. A response surface model with maximum equivalent stress and maximum deformation as the response variables was constructed, and the Pareto optimal set was obtained using the non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal structural design. Quasi-static load tests were conducted on a scaled prototype to verify the reliability of the numerical optimization results. The results demonstrate that the optimized upper arm satisfies the strength and stiffness requirements while achieving a 12% mass reduction (2463 kg), confirming the effectiveness and engineering applicability of the proposed lightweight design methodology. Full article

Wastewater heat recovery has emerged as a vital strategy for building energy conservation, due to its significant potential and the inherent thermal stability of sewage as a heat source. Enhancing synergy between such waste heat and other clean energy sources is a key research focus. This study developed a solar-assisted sewage-source coupled heating system for a Chinese university dormitory and established a multiobjective optimization framework integrating economic, environmental, and energy efficiency indicators via a combined weighting approach of the Analytic Hierarchy Process and Entropy Weight Method. Optimization was conducted using the Hooke–Jeeves algorithm, Particle Swarm Optimization algorithm, and the Hooke–Jeeves–Particle Swarm Optimization hybrid algorithm (shorten as HJ–PSO), with subsequent comparative performance analysis. The HJ–PSO hybrid performed best: 24% lower operating costs, a 4.8-year shorter dynamic payback period, 26.35% fewer carbon dioxide emissions, 38.65% lower overall energy consumption, and an 11.18% higher system coefficient of performance. Supported by relevant policies, the system is low-carbon and economically viable, enabling grid peak shaving. This research provides theoretical and engineering references for renewable energy heating systems. Full article

Financial literacy is critical for individual well-being and sustainable economic development, yet significant gaps remain among Portuguese young adults. Using a two-phase design, this study combines a diagnostic assessment and multi-criteria decision analysis to identify and prioritise effective financial education strategies. In Phase 1, a diagnostic questionnaire administered to 172 first-year university students revealed pronounced deficiencies in core financial concepts. Only 29.1% correctly answered a question on compound interest, and almost half were unable to understand the concept of inflation. Additionally, 62.8% reported low exposure to financial education during compulsory schooling, and 59.9% strongly agreed that it should be included in the mandatory curriculum, indicating both unmet need and strong receptiveness. Phase 2 employed the Analytic Hierarchy Process (AHP) to evaluate five educational alternatives across four criteria. Engagement and motivation (0.32) and knowledge acquisition (0.31) were prioritised over behavioural impact (0.22) and accessibility (0.15). Based on expert assessments weighted by student preferences, in-person courses emerged as the most effective strategy (0.42), substantially outperforming online courses (0.22), videos and digital content (0.14), books (0.13), and games (0.10). The findings point to the need for policy-driven integration of structured, educator-led financial education within formal curricula, supported by approaches that prioritise active engagement and knowledge acquisition over convenience, with digital tools serving as complements rather than replacements. Full article