Search Results (745)

High reclaimed asphalt pavement (RAP) contents are often limited by insufficient restoration of field-aged SBS-modified asphalt and the lack of a comprehensive method for rejuvenator dosage selection. This study aimed to develop a multi-performance-based approach for determining the dosage of an epoxy-based reactive rejuvenator under high-RAP conditions. Rejuvenated binders with different dosages were evaluated using conventional tests, DSR, MSCR, BBR, and LAS tests. Continuous low-temperature grading temperature, dissipated energy ratio, and entropy-weight TOPSIS were used for comprehensive evaluation, while GPC was employed to characterize molecular-weight distribution. The rejuvenator improved low-temperature relaxation, fatigue resistance, energy dissipation, and workability, whereas excessive dosages reduced rutting resistance and elastic recovery. Entropy-weight TOPSIS ranked RA-6 highest, with a relative closeness coefficient of 0.66504, and this ranking was consistent with the overall trends obtained from individual performance tests, supporting the feasibility of the proposed evaluation method. GPC results showed systematic changes in molecular-weight distribution after rejuvenation. For the investigated material system, 6% is recommended among the tested dosages. The proposed framework provides a practical basis for dosage determination when material characteristics and performance requirements vary. Full article

Photovoltaic technology selection in Gulf Cooperation Council (GCC) desert environments is affected by coupled dust, thermal, ultraviolet (UV), humidity, and salinity stresses, which are not fully represented by static weighting and additive multi-criteria decision-making models. This study develops a GCC evidence-calibrated nonlinear decision-support framework that integrates published literature-derived GCC/desert-stress calibration, adaptive hybrid entropy–desert weighting, and bipolar fuzzy Einstein aggregation. The framework is applied to compare passivated emitter and rear cell (PERC), tunnel oxide passivated contact (TOPCon), and heterojunction technology (HJT) photovoltaic technologies using calibrated evidence from Qatar, the United Arab Emirates, Saudi Arabia, and Oman. The results show that dust tolerance receives the highest final hybrid weight (0.258), followed by thermal resistance (0.228), UV resistance (0.207), efficiency (0.173), and cost effectiveness (0.134). The nonlinear Einstein aggregation ranks HJT first (0.889), followed by TOPCon (0.861) and PERC (0.742). Benchmark comparison with TOPSIS, VIKOR, and PROMETHEE II shows high rank agreement, while Monte Carlo perturbation analysis indicates that HJT preserves the first rank in 93% of perturbation runs. The proposed framework links PV technology selection with published GCC desert-stress evidence and provides a reproducible basis for technology prioritization in harsh solar energy deployment environments. A stress-to-decision translation table is also provided to clarify how desert degradation mechanisms are converted into decision criteria and reusable selection guidance. Full article

Urban agglomerations concentrate human populations, economic activities, and land development, but gains in land-use efficiency do not necessarily progress synchronously with improvements in human-development capability. This study examines the coordination and mismatch between population high-quality development (PHQD)—referring specifically to the high-quality development of the human population—and land green use efficiency (LGUE) across 131 cities in six major Chinese urban agglomerations from 2011 to 2023. PHQD is measured using the entropy-weighted TOPSIS method. LGUE is measured using a non-oriented global super-efficiency slacks-based measure (SBM) model with undesirable outputs. People–land coordination is assessed using a modified coupling coordination degree (MCCD) model combined with a relative development index (RDI). Kernel density estimation, Dagum Gini decomposition, and Geodetector are applied to analyze distribution dynamics, disparities, and spatially stratified associations. MCCD rose from 0.311 to 0.419, although most cities remained at relatively low coordination levels. PHQD-lagging was the dominant mismatch type in 2023, involving 98 of 131 cities. Overall MCCD inequality declined from 0.138 to 0.103, while transvariation replaced between-group differences as the largest disparity component. Economic development had the strongest explanatory power, and all pairwise interaction q values exceeded the corresponding single-factor q values. Sustainable urban transformation therefore requires coordinated investment in human capital and public services alongside continued improvements in resource- and environmentally constrained land-use efficiency. Full article

Industry 5.0 reframes industrial transformation as a human-centric, sustainable and resilient socio-technical transition. Yet comparable country-level evidence on the capabilities that enable this transition remains limited, particularly for late-digitalizing economies. This study benchmarks Türkiye against the 27 European Union member states using a systems-informed multi-criteria decision-making framework. Seven indicators represent three complementary capability domains: enterprise digitalization (artificial intelligence, cloud computing, data analytics and enterprise resource planning), human capability (basic or above-basic digital skills) and innovation capacity (R&D expenditure and high-technology exports). CRITIC–MARCOS is used as the primary model, while the full 2 × 2 combination of CRITIC and Entropy weighting with MARCOS and TOPSIS ranking, equal-domain weighting and indicator-exclusion tests assess sensitivity. Türkiye ranks 28th under CRITIC–MARCOS and remains between 26th and 28th across the principal specifications. Pairwise rank correlations range from 0.932 to 0.981, supporting the stability of Türkiye’s placement in the lower-readiness group despite variation in its exact rank across methods. Türkiye is below the unweighted EU-27 country mean for all indicators, with its largest relative shortfall in high-technology exports. The findings diagnose a structural gap in the digital, human-capability and innovation enablers of Industry 5.0; they do not measure the complete Industry 5.0 construct, particularly its direct human-centric, sustainability and resilience outcomes. Full article

The operational reliability of shaft systems in hydropower units has become increasingly critical as these units are frequently engaged in grid regulation under new power systems. This paper presents a sensor-driven method for operational status assessment and trend prediction of Francis turbine generator unit shaft systems using vibration and swing signals. Time domain features are extracted from the sensor-acquired signals to construct a multi-dimensional quantitative index system for characterizing the operational state, and a combined Entropy Weight–Coefficient of Variation–TOPSIS model with dynamic health thresholds is established for adaptive condition assessment. To address the nonlinear and non-stationary characteristics inherent in such signals, a decomposition–prediction–reconstruction fusion framework is developed, incorporating Variational Mode Decomposition (VMD) for signal decomposition and noise reduction, iTransformer for capturing global multi-variable interactions, and Bidirectional Long Short-Term Memory (BiLSTM) for bidirectional temporal feature extraction. The hybrid model achieves a coefficient of determination R² of 0.9845 on complex vibration and swing signals, demonstrating its superior prediction capability. Based on the prediction results, health scores and dynamic thresholds are calculated to perform trend analysis and health early warning. A case study is conducted using real-world monitoring data from a 306 MW Francis turbine unit. The results demonstrate that the proposed method effectively characterizes the shaft system operational state, achieving a closed-loop integration from condition monitoring to fault diagnosis and predictive maintenance. The operational status assessment and trend prediction analyses are in good agreement with actual operating conditions, providing reliable technical support for the intelligent health management of hydropower units. Full article

The spatial mismatch between tourism resources and tourism vitality represents one of the key constraints on the high-quality development of regional tourism. Taking 77 county-level administrative units in Heilongjiang Province as the study area, this research utilizes panel data from 2019 to 2024 to evaluate tourism resource endowment and tourism vitality through the entropy-weighted TOPSIS method. A coupling coordination degree model, quadrant classification approach, and spatial autocorrelation analysis are employed to identify spatial mismatch patterns. Furthermore, a progressive analytical framework integrating XGBoost-based nonlinear modeling, SHAP interpretability analysis, and Geodetector-based spatial validation is constructed to identify the main explanatory factors and nonlinear associations related to these mismatch patterns. The results reveal that: (1) significant spatial mismatches exist between tourism resources and tourism vitality across counties in Heilongjiang Province. The overall coupling coordination degree remains at a low coordination level and exhibits a distinct core–periphery spatial structure; (2) four categories of mismatch units are identified, including high high matching, resource-leading mismatch, vitality-leading mismatch, and low low matching types. These categories display pronounced spatial clustering characteristics, with a sharp contrast between the high-value clusters in the Harbin metropolitan area and border regions and the low-value clusters in western Suihua; (3) the number of hotels shows the highest explanatory contribution in the model, showing a clear threshold-like pattern, while border ports demonstrate a sparse but important association pattern; (4) strong interaction relationships exist among explanatory variables, with nearly 30% of the model’s explanatory power originating from synergistic multi-factor interactions; and (5) Geodetector analysis further confirms the spatial explanatory power of the major explanatory factors and the significance of multivariate interaction effects. This study provides policy references for optimizing the allocation of tourism resources and enhancing tourism vitality in underdeveloped border regions. Full article

The Yellow River Basin is an important ecological security barrier and agricultural production area in China. Using panel data for nine sprovincial-level regions from 2010 to 2022, this study constructs a multidimensional development index of rural industrial integration and examines its association with regional environmental pollution. Regional pollution pressure is measured from total wastewater discharge, sulfur dioxide emissions, and general industrial solid-waste generation; the measure therefore captures broad regional pollution linked to agricultural and related industrial chains rather than agricultural non-point-source pollution alone. Two-way fixed-effects estimates show that higher integration scores are significantly associated with lower pollution levels. This association is statistically evident in the upper reaches, whereas the middle- and lower-reach estimates are not statistically significant and are interpreted as exploratory because each subsample contains only two provinces. Exploratory channel regressions suggest that agricultural technological progress, rural labor mobility, and agricultural industrial scale may help explain the observed association, but the regressions do not establish causal mediation. The findings indicate potential synergies between rural industrial integration and environmental governance, while also requiring caution regarding causal interpretation, composite-index boundaries, and small-sample regional comparisons. Full article

The integrated development of transportation, culture, and tourism is widely recognized as critical for regional sustainability, yet existing research predominantly uses two-dimensional frameworks and overlooks the unique constraints of highway-dominated pastoral borderlands. Taking Inner Mongolia (2023–2025) as a case study, this research develops a 24-indicator evaluation system and a multi-model analytical framework integrating entropy-weighted TOPSIS, coupling coordination, obstacle degree, and grey relational analysis. This study makes three contributions: (1) It constructs a highway-oriented three-dimensional evaluation framework suited for border regions spanning millions of square kilometres; (2) it distinguishes short-term static obstacles from long-term dynamic factors using an integrated multi-model chain; and (3) it proposes the theoretical concept of ‘Highway–Culture Spatial Matching’ for sparsely populated borderlands. The results reveal a significant spatial gradient, with high coordination in the Hohhot–Baotou–Ordos core and severe lagging in western pastoral areas. The spatial match between highway network accessibility and nomadic cultural heritage sites, rather than resource abundance alone, is the decisive factor determining system coupling. The obstacle degree model pinpoints highway mileage and road network density as primary short-term bottlenecks, while grey relational analysis confirms that the scale of the cultural tourism industry and intangible heritage activation are key long-term drivers. This work provides a quantifiable, evidence-based reference for formulating tiered, sustainable development policies in similar northern frontier ethnic regions, consistent with the UN 2030 Agenda for Sustainable Development. Full article

The existing research has rarely integrated within-province associations, interprovincial spatial linkages, and multidimensional heterogeneity when examining how digitalization is related to increasing household consumption. Using a balanced panel of 30 provincial-level regions in China for 2011–2023, compiled from national and provincial statistical yearbooks (CSMAR) and the Peking University Digital Financial Inclusion Index, this study constructs a 0–1 digital economy development index with entropy-weighted TOPSIS. Two-way fixed effects estimate the average within-province relationship; global and local Moran’s I and a spatial Durbin model evaluate spatial dependence and decompose direct, indirect, and total effects. Panel quantile regressions and alternative spatial weight matrices serve as robustness checks, whereas instrumental variables and double/debiased machine learning provide supplementary identification evidence. Digital economy development is positively associated with consumption upgrading in the baseline model. Under economic distance weights, the direct, indirect, and total effects are all significantly positive, although their structure differs across consumption categories, urban and rural groups, regions, and temporal stages. The findings support combining digital infrastructure with service capacity, skills, consumer protection, and interprovincial governance while avoiding uniform policy prescriptions across regions. Full article

Fused deposition modeling (FDM) of polylactic acid (PLA) is plagued by dimensional inaccuracies—thermal shrinkage, warpage, and geometric distortion—that restrict its application in high-precision manufacturing. Blending thermoplastic polyurethane (TPU) with PLA enhances toughness, yet the coupled effects of blend ratio and printing parameters on dimensional accuracy remain unclear. This study establishes a multi-objective optimization framework integrating single-factor experiments, orthogonal design, ANOVA, and entropy weight–TOPSIS. Single-factor experiments combined with TOPSIS first identify the optimal PLA/TPU blend ratio, and orthogonal experiments are subsequently conducted on this optimal ratio to determine the best parameter combination. The 70:30 PLA/TPU blend delivers the optimal comprehensive performance (TOPSIS closeness: 0.680), attributed to favorable phase compatibility and robust interfacial adhesion as verified by XRD and SEM. Range analysis and ANOVA on the orthogonal results identify infill rate and layer height as the dominant factors governing dimensional accuracy (p < 0.05). Under the optimized parameter set (60 mm/s, 210 °C, 90% infill, 0.1 mm layer), the total dimensional deviation reaches only 0.256 mm, substantially lower than single-objective counterparts (0.321–0.418 mm). This work offers a validated strategy for precision control in FDM through synergistic optimization of blend formulation and processing parameters. Full article

To address the strong coupling among engine geometry, propulsion performance, aerodynamic response, flight trajectory, and manufacturing cost, this study establishes an integrated propulsion–aerodynamics–trajectory–cost design framework for high-speed, long-range rocket-boosted vehicles. Six chamber and nozzle geometric parameters are selected as design variables. An engine performance model is first used to calculate propulsion responses, including thrust, chamber pressure, and specific impulse. A mass and configuration update model then transfers the effects of engine parameter variations to the overall vehicle characteristics, while aerodynamic data and a two-dimensional point-mass trajectory model are introduced to obtain mission-level indicators, including maximum velocity, maximum altitude, and range. An existing manufacturing cost decomposition model for solid rocket motors is extended by coupling the cost response with component masses, geometric dimensions, and parameter-update relationships, thereby enabling the simultaneous evaluation of mission performance and manufacturing cost within the integrated computational chain. Kriging surrogate models are constructed for rapid prediction of the coupled system responses, entropy-weighted TOPSIS is used to screen feasible candidates, and SQP is employed for continuous constrained refinement. Compared with the baseline design, the comprehensive evaluation index increases from 0.4861 to 0.6110. The maximum velocity, maximum altitude, and range increase by 8.29%, 26.81%, and 21.24%, respectively, while the manufacturing cost increases by only 0.48%. The evaluation index is also 15.68% higher than that of the engine-level optimized design. These results demonstrate that the integrated consideration of propulsion, aerodynamics, trajectory, and manufacturing cost improves mission-level performance–cost trade-offs and provides a system-level design approach for mission-oriented and cost-aware solid rocket motor development. Full article

Improving the flood adaptability of urban waterfront spaces is an essential entry point for enhancing regional stormwater regulation capacity, scientifically preventing flood disasters, and stabilizing urban water security. Existing flood adaptability assessments mostly rely on single weighting methods and individual evaluation models, inevitably causing systematic bias and low result robustness. Against this limitation, this study integrates remote sensing intelligent interpretation, spatiotemporal landscape pattern analysis, and multi-criteria decision theory to construct a comprehensive flood adaptability evaluation system under the pressure–state–response (PSR) framework. Innovatively, a game-theoretic combined weighting scheme integrating the entropy weight method, CRITIC method, and standard deviation method is proposed, and three complementary models including TOPSIS, VIKOR, and EDAS are coupled for cross-verification evaluation, which effectively improves the objectivity and robustness of spatial flood adaptability quantification. Taking Anqing City as a typical case, this study adopts Sentinel-2 time-series remote sensing images from 2016 to 2023 and applies an optimized random forest algorithm to automatically classify land cover. Five underlying surface types, including water bodies, vegetation, farmland, built-up areas, and bare land, are accurately extracted with an overall classification accuracy of around 90% for most years. Core landscape metrics such as Shannon’s diversity index and patch density are selected to systematically analyze the spatiotemporal differentiation characteristics of waterfront landscape patterns during the study period. The results indicate the obvious spatial heterogeneity of flood adaptability in Anqing City. Yingjiang District and Yuexi County present high comprehensive flood adaptability, while Wangjiang County and Huaining County show relatively low performance. Urban areas gain strong flood resistance from complete disaster prevention infrastructures and economic resilience; mountainous areas possess natural advantages in flood retention and drainage due to high vegetation coverage and topographic relief; by contrast, plain districts are severely restricted by low-lying terrain and insufficient drainage systems, resulting in prominent flood vulnerability. The proposed method is helpful for providing reliable scientific support for waterfront landscape optimization, zoned flood disaster management, and resilient water space planning in riverine cities. Full article

This study aims to evaluate the inclusive green growth (IGG) performance of resource-based cities and to diagnose the structural bottlenecks that constrain their transition. Using a city-level panel of 37 resource-based cities in the Yellow River Basin, China, from 2011 to 2024 (518 city-year observations) drawn from official statistical yearbooks, government documents, and remote-sensing products, we construct a multidimensional IGG index covering economic growth, social equity, and environmental protection, measured through entropy-weighted TOPSIS; kernel density estimation, global and local spatial autocorrelation analysis, a gravity-based potential-interaction network, and an obstacle-degree model are then applied to examine temporal evolution, spatial differentiation, and constraint structure. The results show that (1) the basin-wide IGG index rose from 0.2199 to 0.2845 (+29.4%), with a visible adjustment around 2015 and a stronger acceleration after 2020; (2) environmental protection improved fastest (0.7027 in 2024), while economic growth remained the weakest dimension (0.2408); (3) spatial dependence strengthened significantly from 2017 onward, with high-high clusters concentrated in lower-reach Shandong cities; and (4) the economic-growth dimension constitutes the dominant obstacle (54.43%), led by export-capacity and social-insurance shortfalls. A robustness check with an alternative combined weighting scheme confirms these patterns. The findings indicate that transition policy should shift from pollution control toward capability building, with differentiated pathways by city type and river reach. Full article

Blending hydrogen into existing natural-gas pipeline networks provides a practicable route toward future low-carbon applications. A Venturi-type mixer is a classical high-efficiency static gas-mixing device, and clarifying the effects of its structural parameters is important for efficient transport and downstream combustion stability. In this study, numerical simulations were performed in ANSYS Fluent 2024 R1. The contraction angle, throat length, and diffuser angle were selected as representative structural variables. First, the independent effects of these variables on the mixing process were examined through single-factor simulations. Then, three key levels of the three structural parameters were selected to establish a Box–Behnken experimental matrix for response-surface modeling. Based on the numerical results, entropy weighting and a genetic algorithm were used for multi-objective optimization, and the final solution was verified using the TOPSIS method. The results show that the optimized Venturi-type mixing device with optimized parameters of a contraction angle of 20.7°, a throat length of 60 mm, and a diffuser angle of 5° can reduce flow energy loss while maintaining high mixing uniformity. The diffuser angle is the dominant geometric parameter affecting both energy loss and mixing behavior. Compared with the reference central-point structure design, the overall TOPSIS score of the optimized structure increased from 0.41 to 0.82; the pressure loss decreased from 258.94 Pa to 206 Pa, corresponding to a reduction of approximately 20%; and the final-section mixing uniformity decreased only slightly, from 97.85% to 97.43%. Full article

Synergistic pollution reduction and carbon mitigation (SPRCM) is a critical pathway for advancing global climate governance and promoting green and low-carbon transitions. It also represents an important practice for strengthening ecological civilization and achieving regional sustainable development in China. Based on panel data from 31 Chinese provinces during 2010–2024, this study employs an improved entropy-weighted TOPSIS method to measure SPRCM levels and integrates a modified gravity model, social network analysis, and the CatBoost-SHAP explainable machine learning approach to examine its spatiotemporal evolution, spatial network structure, and nonlinear association characteristics. The main findings are as follows. First, China’s overall SPRCM level shows a gradual upward trend, while significant regional disparities remain. Second, the interprovincial spatial association network has gradually evolved toward a polycentric structure and exhibits clear hierarchical characteristics. Although network connectivity has continuously improved, linkages between core and peripheral provinces remain relatively weak. Third, individual network characteristics display distinct regional differentiation. Eastern coastal provinces consistently occupy core network positions, whereas northeastern and some western provinces remain relatively peripheral. Fourth, the block model analysis identifies four functional groups within the network: net beneficiaries, net spillovers, brokers, and two-way spillovers. These blocks play differentiated roles in spatial association and network interactions. Fifth, the CatBoost-SHAP analysis indicates that road density, freight volume, and human capital are the most important variables in explaining model predictions, with their combined mean absolute SHAP importance accounting for 54.59%. Moreover, these factors exhibit significant nonlinear association patterns with predicted SPRCM levels. Full article