Search Results (822)

Prefabricated mechanical, electrical, and plumbing (MEP) systems have been increasingly adopted in energy station projects; however, systematic evaluation frameworks capable of integrating construction performance, cost constraints, and uncertain multi-indicator assessments remain limited. To address this gap, this study constructs an Analytic Hierarchy Process (AHP)–Entropy–Fuzzy evaluation framework to assess the comprehensive benefits of BIM-enabled prefabricated MEP construction in energy stations. A hierarchical evaluation system was established based on five dimensions: schedule, quality, cost, safety, and environmental performance, and ten secondary indicators were defined. The Analytic Hierarchy Process was used to determine expert-based subjective weights, the entropy method was applied to capture objective data variability, and multiplicative normalization was employed to obtain combined weights. A fuzzy comprehensive evaluation model was then introduced to transform heterogeneous construction records into comparable benefit levels and scores. The prefabricated method scored 87.80 and was classified as “high”, whereas the conventional method scored 60.85 and was classified as “low”. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based sensitivity analysis further showed that, under 10%, 20%, and 50% criterion-weight perturbations, the prefabricated group consistently achieved higher closeness coefficients than the conventional group. The smallest margin occurred when the schedule weight was reduced by 50%, but the prefabricated group retained a positive advantage. The results demonstrate that Building Information Modeling (BIM)-enabled prefabricated MEP construction can achieve superior overall project performance through the coordinated optimization of schedule, cost, safety, quality, and environmental objectives, offering a practical evaluation framework and decision-support tool for the industrialized delivery of future energy infrastructure projects. Full article

To systematically assess the regional impacts of new-energy base (NEB) development, this study proposes a comprehensive evaluation model integrating the Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight Method (EWM), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A 26-indicator framework across environmental, technological, economic, and social (ETES) dimensions was constructed. Empirical analysis of representative cases was carried out using game-theoretic integration of FAHP and EWM to derive indicator weights. Furthermore, an obstacle degree model was employed to identify key constraints. Three representative NEBs in Xinjiang Province were selected for analysis, including a medium-scale wind-PV hybrid base (Case A), a large-scale PV project with standalone storage (Case B), and a wind power expansion project (Case C). The results validate the scientific robustness of the ETES framework, with combined weighting indicating that economic criteria hold the highest priority. The case assessments reveal that Case B attained the highest relative closeness in the TOPSIS ranking, whereas Cases A and C performed less favorably due to significant deviations from ideal indicator values. Obstacle analysis further identified distinct limiting factors. These findings offer a theoretical basis and practical insights for analogous renewable energy initiatives, particularly in regions facing complex sustainability trade-offs. Full article

Transportation hub cities serve as pivotal nodes within integrated transport systems. This study reveals the corridor-oriented characteristics of comprehensive transportation system, confirms the progress of its transportation hub city development, and identifies future improvement directions based on diagnostic evaluation, taking Gansu Province, China as the research subject. To address hierarchical differentiation and structural constraints in the development of integrated transportation hubs, this study develops an evaluation framework integrating the entropy-weighted TOPSIS method, a coupling coordination model, and indicator-based diagnostic analysis. This framework was applied to 14 prefecture-level cities and autonomous prefectures in Gansu, classifying them into four hub tiers according to the comprehensive evaluation index. The results reveal a pronounced hierarchical and corridor-oriented spatial structure: Lanzhou is identified as the only Tier 1 core hub, five cities are classified as Tier 2 backbone hubs, seven cities and prefectures as Tier 3 general hubs, and Pingliang as Tier 4 terminal hub. Lanzhou exhibits the highest development level, with a comprehensive evaluation index of 0.9640, which is substantially higher than the provincial mean of 0.3867, but its radiation-driving capacity still needs to be strengthened. In terms of subsystem coordination, Lanzhou reaches the primary coordination stage with a coupling coordination degree of 0.532, while Jiuquan, Jiayuguan, and Tianshui are classified into the near-coordination stage with D values of 0.353, 0.351, and 0.321, respectively; the remaining ten units are classified as uncoordinated relatively. Based on the combined perspectives of development level and subsystem coordination, the study identifies future development directions for hub operational organization, multimodal transport integration, feeder connectivity, and industry-logistics coupling. The findings reveal the corridor-oriented characteristics and development progress of Gansu’s transportation hub system, highlight the analytical value of distinguishing hub development level from subsystem coordination, and provide empirical evidence for understanding hierarchical and functional differentiation in corridor-oriented inland regions. Full article

Small and medium enterprises routinely face a paradox in financial monitoring: their accounting documents exist, but the cost of converting heterogeneous PDFs into timely financial signals is prohibitive without dedicated analytical staff or specialized software. This paper presents a two-layer artifact, designed under Design Science Research, that bridges this gap using only public-web large language models (LLMs) and a parsimonious multi-criteria decision routine. Layer 1 implements a structured LLM-driven workflow that extracts account–value pairs from annual tax balance sheets without code, APIs, or fine-tuning. Layer 2 reconstructs auditable accounting aggregates and ranks yearly financial condition through TOPSIS with min–max normalization—a deliberate replacement for classical vector normalization, which fails when profitability indicators are negative, as routinely occurs in distress years. To avoid size effects and algebraic redundancy, the decision matrix uses only three criteria spanning liquidity, profitability, and solvency. The artifact is demonstrated in a four-year case study of an anonymized construction SME (2021–2024), with accountant-verified document-level match rates of 0.810, 0.998, 0.950, and 0.909. Equal weighting is the only weighting configuration used; a supplementary entropy-based dispersion diagnostic yields the same ordinal ranking—2024 > 2023 > 2021 > 2022—and 10,000 Monte Carlo replications, with uncertainty injected at the reconstructed-aggregate level, confirm that the extreme ranks are invariant across all runs. The contribution is methodological and practical: a transparent, low-infrastructure pipeline that brings first-pass financial screening within reach of SMEs operating under severe data and budget constraints. Full article

Reservoir effectiveness in marine shales is controlled not only by pore volume but also by pore-fluid occurrence, pore–throat connectivity, and mineral–organic matter coupling. In this study, the Permian Gufeng Formation shales from the Enshi area, western Hubei, South China, were investigated through an integrated analysis of total organic carbon (TOC), X-ray diffraction (XRD)-based mineral composition and lithofacies, low-field nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), micro-computed tomography (Micro-CT), and entropy-weighted technique for order preference by similarity to an ideal solution (TOPSIS) evaluation. The TOC content ranges from 1.60% to 21.38% and shows clear vertical differentiation, with moderate but variable enrichment in the lower interval, reduced organic matter abundance in the middle interval, and pronounced organic enrichment in the upper interval. Mineral compositions demonstrate an upward transition from a mixed siliceous–carbonate system to a dominantly siliceous shale system. NMR results reveal strong heterogeneity in porosity, NMR-derived permeability, T2cutoff, bound-fluid saturation, and free-fluid saturation. Based on saturated and centrifuged T2 spectra, four descriptive reservoir response types were identified: short-T2-dominated micropore-bound response, intermediate-T2-dominated movable-fluid response, long-T2-enriched but low-efficiency response, and NMR-inferred enhanced mobility composite response. SEM observations show diverse pore types, including organic-matter-related pores, dissolution pores, interparticle pores, mineral-edge pores, pyrite intercrystalline pores, and local microfracture-like pores. Micro-CT results indicate that micrometer-scale pore bodies are commonly isolated, demonstrating that pore abundance or pore size alone cannot determine reservoir effectiveness. TOC mainly controls pore generation potential, whereas siliceous minerals, pore–throat connectivity, movable fluid proportion, and local fractures exert stronger controls on effective reservoir development. The most favorable reservoir responses are concentrated in the upper high-organic siliceous shale interval from A33 to A42, with local enhanced responses in A16 and A21. These results provide an integrated framework for evaluating reservoir heterogeneity and favorable intervals in complex marine shale systems. Full article

Background/Objectives: Patient-specific subperiosteal implants are increasingly used to treat severely atrophic ridges due to advances in digital planning and additive manufacturing. This study aimed to evaluate the effects of material type and implant configuration on stress distribution in subperiosteal implant systems and to compare their overall biomechanical performance using a multi-criteria decision framework. Methods: A three-dimensional model of a severely atrophic maxilla was reconstructed to simulate four clinical scenarios combining two configurations (one-piece and two-piece) and two materials (titanium and 60% carbon fiber-reinforced polyetheretherketone). Finite element analysis was conducted to assess stress distribution within the implant body, fixation screws, prosthetic framework, and surrounding bone under vertical and oblique loading conditions. Maximum and minimum principal stresses were evaluated in bone, whereas von Mises stresses were calculated for implant components. The resulting biomechanical indicators were subsequently integrated using an entropy weight–TOPSIS multi-criteria decision analysis. Results: Principal stresses in the surrounding bone showed minimal variation between titanium and 60% carbon fiber-reinforced polyetheretherketone across all configurations. Implant configuration had a more pronounced effect on implant body stress. Under oblique loading, the two-piece configuration demonstrated substantially higher implant stresses than the one-piece design, whereas under vertical loading, lower implant stresses were observed in the two-piece configuration. The multi-criteria analysis ranked the one-piece titanium model highest under oblique loading and the two-piece titanium model highest under vertical loading. Conclusions: Implant configuration and loading direction influenced biomechanical behavior more than material selection in patient-specific subperiosteal implants. Full article

Artificial shelterbelts in arid and semi-arid regions play a key role in controlling land degradation, regulating wind erosion, and maintaining ecological security. However, their long-term protective effectiveness increasingly depends on accurate degradation diagnosis and targeted management of aging and degraded stands. This study developed a comprehensive health assessment and degradation grading framework for poplar shelterbelts in the Kubuqi Desert, northern China, using an indicator system covering stand structure, community structure, soil conditions, health risks, and external disturbances. Indicator weights were determined using the entropy weight method, and degradation grades were classified by combining the technique for order preference by similarity to ideal solution (TOPSIS) model with the rank-sum ratio (RSR)–Probit method. The results showed that soil conditions and stand structure were the dominant dimensions distinguishing degradation status, with weights of 50.98% and 25.30%, respectively. Grade I, Grade II, Grade III, and Grade IV stands accounted for 21.88%, 25.00%, 34.38%, and 18.75% of the plots, respectively, indicating that lightly and moderately degraded stands were predominant. Degradation grades were also associated with changes in understory cover and surface soil nutrients, especially decreases in soil organic matter and alkali-hydrolyzable nitrogen. Based on these results, grade-specific management strategies were proposed, including conservation and maintenance, density regulation, assisted restoration, and near-natural transformation. This framework provides a practical basis for diagnosing degradation status and guiding the renewal and management of aging shelterbelts in arid sandy regions. Full article

Choosing the most suitable tractor is a complex and high-stakes decision where technical performance, financial capability, and sustainability considerations must be balanced. However, tractor selection in existing studies lacks objective, sustainability-oriented evaluation frameworks, leaving farmers vulnerable to potentially poor investments with long-term economic, operational, and environmental impacts. Therefore, this research proposes a software-based Decision Support System (DSS) that incorporates objective multi-criteria decision-making (MCDM) models within a management control perspective focused on sustainability and provides a clear, data-driven method for tractor selection for small farmers. Four popular tractor models in Egypt were selected for evaluation based on three criteria related to sustainability: power (C1), purchase price (C2), and availability of maintenance and spare parts (C3). Subsequently, a DSS was implemented using Python, and five MCDM methods—CRITIC, MEREC, Entropy, Standard Deviation (SD), and TOPSIS—were used to select the tractor that best meets sustainability objectives. The findings indicate that tractor T2, which had the lowest purchase price (USD 12,390) and enough power (60 HP), was the best-rated tractor. The impact of each criterion varied by method: C1 was the most important in the Entropy method (0.3657), while C2 was the most important in the CRITIC (0.5552), MEREC (0.3432), and SD (0.5938) weightings. The proposed DSS improves transparency and supports more informed, evidence-based decisions in agricultural mechanization. Overall, the system offers a practical and scalable tool that helps smallholder farmers and policymakers make sustainable tractor choices, contributing to progress toward SDGs 2, 7, 12, and 13. Full article

While traditional expansion tubes exhibit excellent energy absorption, their uniform wall thickness limits lightweighting and performance optimization. Graded thickness designs can reduce the initial peak crushing force (IPCF) and enhance material efficiency. This paper proposes a variable-thickness expansion tube integrating high energy absorption with tailored mechanical response. Material tensile tests were conducted to determine the constitutive relationship, and axial compression experiments on expansion tubes were performed. Numerical simulations were validated against experimental results, establishing an accurate finite element model. The influence of design parameters on crashworthiness indicators was analyzed via orthogonal experiments. A fully connected neural network with a feature importance layer was then constructed to efficiently replace computationally expensive simulations. Key performance indicators—including IPCF, total energy absorption (EA), and structural mass (m)—were synergistically optimized using a multi-objective genetic algorithm. Finally, the entropy weight–gray relation–TOPSIS method was employed to select the most satisfactory solution from the Pareto front. The relative discrepancies between the selected solution and finite element simulations are 3.65% for EA, 0.23% for mass, and 4.37% for IPCF, confirming the framework’s reliability. This study establishes a systematic design approach combining machine learning, multi-objective optimization, and multi-criteria decision-making for high-performance energy-absorbing structures. Full article

Efficient allocation of capital toward environmental, social, and governance (ESG) objectives has become a critical challenge for emerging economies pursuing sustainable development and financial resilience. While prior research has primarily focused on ESG investment volumes, considerably less attention has been devoted to the efficiency with which financial and institutional systems transform capital into measurable sustainability outcomes. This study introduces the concept of ESG-Oriented Capital Allocation Efficiency (ECAE) and develops a hybrid multicriteria decision-making (MCDM) framework to evaluate its performance across 24 emerging market economies during the period 2021–2025. The proposed framework integrates DEMATEL, ANP, entropy weighting, TOPSIS, and VIKOR methods to capture causal relationships, interdependencies, weighting structures, and comparative efficiency rankings. The results identify governance effectiveness, ESG policy stability, and regulatory quality as the most influential drivers of ECAE, while higher ESG investment volumes alone do not necessarily generate superior sustainability outcomes. Sensitivity analysis confirms the robustness of the ranking results across alternative weighting scenarios. The findings suggest that strengthening institutional quality, policy coherence, and governance effectiveness is essential for improving sustainable finance outcomes. The study contributes to the sustainable finance literature by providing a policy-oriented framework for evaluating how effectively emerging market economies translate ESG-oriented capital into tangible sustainability performance. Full article

Marine propulsion shaft alignment is affected by bearing offsets, hull deformation, thermal growth, and condition-dependent propeller and gear loads. An alignment scheme optimized for a single condition may therefore lead to unbalanced bearing reactions or excessive shaft-line deformation in service. To improve multi-condition alignment performance while reducing the reliance on repeated direct finite element evaluations during optimization, this study proposes a hybrid surrogate-assisted multi-objective optimization framework for a container-ship propulsion shafting system. A beam finite element model based on Euler–Bernoulli theory is established and numerically checked using jack-up calculations. Cold static, hot operating, and zero-pitch conditions are considered. Bearing-load uniformity, maximum coupling vertical offset, and maximum shaft slope are selected as objectives. According to response characteristics, an extremely randomized trees model is used for the nonlinear load-uniformity response, whereas response surface models are used for the smoother coupling-offset and shaft-slope responses. The Pareto front is obtained using multi-objective particle swarm optimization, and a compromise scheme is selected using entropy-weighted TOPSIS. For the investigated case, the preferred scheme reduces the three objectives by 44.36%, 38.62%, and 8.65%, respectively, relative to the pre-optimization scheme, and finite element recalculation gives prediction deviations below 5%. The proposed framework provides a practical reference for propulsion shaft alignment optimization under operating conditions. Full article

Face milling of aluminum 7075 hybrid metal matrix composites with 10 wt.% TiO₂ and 3 wt.% graphite (HMMCs) are needed to improve performance and sustainability. This study focuses on optimizing the milling process for Al7075 HMMCs using the desirability approach and advanced multi-criteria decision-making (MCDM) methodologies, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Combined Distance-based Assessment (CODAS). Surface roughness (SR), cutting force (CF), carbon emissions (CE), and energy consumption (EC) were systematically evaluated and ranked using the L₁₈ Taguchi Orthogonal Array. Minimum Quantity Lubrication (MQL) and cryogenic CO₂ cooling techniques were used to achieve a superior surface finish and reduce friction at the tool-workpiece interface, thereby minimizing scratches and thermal damage. Desirability evaluation results showed the optimal machining conditions for milling of Al7075 (HMMCs) occurred at a cutting speed (V_c) of 200 m/min, a feed rate (f) of 0.02 mm/rev, and a depth of cut (a_p) of 0.3 mm, proving the potential of integrating MCDM tools with effective cooling strategies. The desirability method favored a balanced compromise, while entropy-weighted TOPSIS/CODAS emphasized energy and carbon-related responses. Improvements of 6% in cutting force, 7% in surface roughness, and a 7% reduction in energy consumption, along with 8% lower carbon emissions, were achieved, demonstrating the effectiveness of hybrid cooling strategies in promoting eco-friendly and resource-efficient processes. Full article

As a core driver of high-quality urban–rural integration, digital inclusive finance plays a key role in the process of Chinese-style modernization. After measuring the level of high-quality urban–rural integration development using the TOPSIS entropy method, this study employs fixed-effects models and mediation models to empirically examine how digital inclusive finance influences high-quality urban–rural integration development over the period from 2012 to 2022. The main findings are as follows: (1) Digital inclusive finance has a significantly positive promoting effect on high-quality urban–rural integration. (2) The enabling effect of digital inclusive finance exhibits significant regional heterogeneity, following a gradient pattern of “strongest in the Eastern region, followed by the Central region, and weakest in the Western region.” (3) In terms of dimensional effects, the breadth of coverage contributes the most, followed by the depth of use, while the degree of digitalization has the smallest impact. (4) The mediation mechanism indicates that factor mobility indirectly promotes high-quality urban–rural integration. Based on the above findings, this paper proposes policy recommendations to foster high-quality urban–rural integration development in China. Full article

To support refined water quality evaluation and short-term early warning in multi-section river systems, this study developed three percentile-based composite indices: the Water Quality Index (WQI), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Entropy Weight Method (EWM). Monthly multi-parameter monitoring data from 2019 to 2025 were used, covering ten river sections (P1–P5, M1–M5). The three indices were compared in terms of statistical distribution, methodological consistency, and anomaly response. An integrated assessment–prediction framework was further established. Within this framework, a one-step prediction scheme was applied to evaluate four models: Long Short-Term Memory networks (LSTM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The results show that WQI scores were generally high and fluctuated within a narrow range. A clear “ceiling effect” was observed in the moderate-to-high grade intervals. WQI also showed weak consistency with TOPSIS and EWM (r ≈ 0.29–0.32). In contrast, TOPSIS and EWM were more sensitive to water quality fluctuations and extreme risks, and were moderately correlated with each other (r ≈ 0.53). Using TOPSIS < 50 as the threshold, 49 severe anomalous events were identified. These events were mainly clustered in February–April 2020, April–July 2023, and June–September 2025, with sections P4, M1, and M2 acting as high-incidence sites. In several typical events, WQI values remained high, indicating that reliance on WQI alone may delay early warning. Prediction results further reveal that the choice of index strongly affects sequence predictability. Taking XGBoost as the reference, the median validation R² followed a stable gradient: WQI (0.807) > TOPSIS (0.723) > EWM (0.594). XGBoost yielded positive R² values across all indices and sections. It also achieved the most robust overall performance and the strongest cross-site, cross-index generalization capability. Full article

Smart museums increasingly rely on digital media, interactive installations, artificial intelligence, augmented reality, and virtual reality to support cultural communication and visitor engagement. However, existing studies have mainly examined specific technologies, usability, or visitor satisfaction, while a systematic and quantitative framework for comparing interactive experience across different smart museums remains limited. To address this gap, this study proposes a hybrid multi-criteria decision-making framework for evaluating smart museum interactive experience. Based on the Strategic Experiential Modules, an evaluation system consisting of five dimensions—Sense, Feel, Think, Act, and Relate—and sixteen indicators was constructed. The Analytic Hierarchy Process was used to determine subjective weights from expert judgments, the entropy method was applied to capture the data-driven dispersion characteristics of expert evaluation data, and a game-theoretic combination weighting strategy was used to integrate the two weighting results. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare five representative smart museum cases. The results show that Zhejiang Provincial Museum achieved the highest relative closeness value (C_i = 0.9891), followed by Shanghai Museum (C_i = 0.8457) and Hunan Museum (C_i = 0.5326). Robustness analysis further showed that the ranking order remained consistent under entropy weights, AHP weights, average weights, and game-theoretic combined weights. The Friedman test indicated no significant difference in the relative closeness coefficients across weighting schemes (χ² = 1.200, p = 0.753). These findings indicate that the proposed framework can effectively identify relative strengths and weaknesses in smart museum interactive experience and provide a replicable decision-support tool for experience-oriented museum design and optimization. Full article