Search Results (2,648)

This study compares the Stackelberg game model and the entropy weight method for allocating intercity water rights in Heilongjiang Province (2014–2021). The entropy method objectively determines indicator weights, while the Stackelberg framework simulates leader–follower interactions between the water authority and users to balance efficiency and satisfaction. Under the same total water rights cap, the Stackelberg scheme achieves a comprehensive benefit of CNY 14,966 billion, 4% higher than the entropy method (CNY 14,436 billion). The results and comprehensive benefits of the two schemes are close to each other in the cities of Qiqihaer, Daqing, Hegang, etc., but the allocation method of the game theory is more in line with the practical needs and can meet the water demand of each region, and the entropy right method is more useful for the cities of Jiamusi, Jixi, and Heihe, while for other cities the water rights allocation appeared to be unreasonable. While the entropy approach is transparent and data-driven, it lacks dynamic feedback and may under- or over-allocate in rapidly changing contexts. The Stackelberg model adapts to varying demands, better aligning allocations with actual needs. We discuss parameter justification, sensitivity, governance assumptions, and potential extensions, including hybrid modeling, climate change integration, stakeholder participation, and real-time monitoring. The findings provide methodological insights for adaptive and equitable water allocation in regions with strong regulatory capacity. Full article

Pale lager dominates global beer markets. However, rising living standards and changing consumer expectations have reshaped sensory preferences, highlighting the importance of understanding consumers’ true sensory priorities. In this study, a twenty-eight-item questionnaire, refined through multiple rounds of optimization, was distributed across China and yielded 1837 valid responses. Spearman correlation analysis and partial least-squares regressions showed that educational background and spending willingness exerted the strongest independent effects on sensory priorities. A hybrid analytic hierarchy process–entropy weight method–Delphi procedure was then applied to quantify sensory attribute importance. Results indicated that drinking sensation (30.92%) emerged as the leading driver of pale lager choice, followed by taste (26.60%), aroma (24.77%), and appearance (17.71%), confirming a flavor-led and experience-oriented preference structure. Weighting patterns differed across drinking-frequency cohorts: consumers moved from reliance on overall mouthfeel, through heightened sensitivity to negative attributes, to an eventual focus on subtle hedonic details. Based on these findings, a new sensory evaluation scale was developed and validated against consumer preference rankings, showing significantly stronger alignment with consumer preferences (ρ = 0.800; τ = 0.667) than the traditional scale. The findings supply actionable metrics and decision tools for breweries, supporting applications in product development, quality monitoring, and targeted marketing. Full article

In multi-criteria decision-making (MCDM) environments characterized by uncertainty and data irregularities, the reliability of weighting methods becomes critical for ensuring robust and accurate decisions. This study introduces a novel hybrid objective weighting method—IQRBOW-E (Interquartile Range-Based Objective Weighting with Entropy)—which dynamically combines the statistical robustness of the IQRBOW method with the information sensitivity of Entropy through a tunable parameter $\beta$. The method allows decision-makers to flexibly control the trade-off between robustness and information contribution, enhancing the adaptability of decision support systems. A comprehensive experimental design involving ten simulation scenarios was implemented, in which the number of criteria, alternatives, and outlier ratios were varied. The IQRBOW-E method was integrated into the VIKOR framework and evaluated through average $Q$ values, stability ratios, SRD scores, and the Friedman test. The results indicate that the proposed hybrid approach achieves superior decision stability and performance, particularly in data environments with increasing outlier contamination. Optimal $\beta$ values were shown to shift systematically depending on data conditions, highlighting the model’s sensitivity and adaptability. This study not only advances the methodological landscape of MCDM by introducing a parameterized hybrid weighting model but also contributes a robust and generalizable weighting infrastructure for modern decision-making under uncertainty. Full article

Industrial revitalization is the foundation and top priority of rural revitalization, and artificial intelligence (AI) serves as a core driver of industrial revitalization. This study analyzes how AI empowers the rural industrial revitalization, and it measures the comprehensive development level of AI and rural industrial revitalization using the entropy-weighted TOPSIS method. Utilizing data on prefecture-level cities in Hebei Province from 2003 to 2023, this research empirically investigates the impact of AI on rural industrial revitalization through a two-way fixed-effects model and a mediation effect model. The findings reveal that AI development significantly promotes rural industrial revitalization, a conclusion that holds after robustness tests. Mechanism analysis indicates that AI facilitates rural industrial revitalization by promoting agricultural technological innovation and driving industrial structural upgrading. Heterogeneity analysis shows that the empowering effect of AI on rural industrial revitalization is more pronounced in areas lagging in technological innovation and in the Functional Expansion Zone of Central and Southern Hebei. Full article

This paper analyzed the current development status of the economy, transportation, and ecology in Xi’an and constructed a coupling coordination evaluation index system. Employing the entropy weight method to determine indicator weights, it adopted the coupling coordination degree model and the obstacle degree model to examine the coupling coordination development level of the three subsystems and identified key obstacle factors at the criterion layer and the indicator layer. The results showed the following: (1) From a temporal perspective, the coordination degree among the three subsystems in Xi’an increased significantly from 0.217 in 2013 to 0.712 in 2023. Its coupling coordination development level showed a steady evolutionary trend, evolving from “Moderately Imbalanced” to “Moderately Coordinated;” (2) The comprehensive ranking of the obstacle degrees of the three system layers was as follows: from 2013 to 2017, economy > transportation > ecology; in 2018, ecology > economy > transportation; in 2019, ecology > transportation > economy; from 2020 to 2023, transportation > ecology > economy; (3) The top five in the ranking of obstacle degree calculation results at the indicator layer were as follows in sequence: investment in environmental pollution control, built forestry areas, road passenger turnover, highway density, and length of highways; (4) The key obstacle factors at the indicator layer exhibited an evolutionary trend of “economic dominance → transportation dominance → interweaving of ecology and transportation.” Ultimately, it proposed corresponding implementation paths, aiming to promote the collaborative development of economic growth model innovation, green transformation of transportation, and ecological protection. Full article

Amid an increasingly complex and uncertain global landscape, geopolitical tensions and frequent trade frictions have emerged as critical external risks threatening the economic stability and sustainable development of Chinese cities. Enhancing cities’ economic resilience has become a key challenge in advancing China’s high-quality development agenda. As a major national strategic initiative, the Belt and Road Initiative (BRI) is expected to offer new development opportunities and pathways for risk mitigation, particularly for cities situated along its domestic routes. This paper examines whether and how the BRI affects the economic resilience of these cities and further explores the moderating role of local governance capacity in policy implementation. To this end, an empirical strategy combining the entropy weighting method and the difference-in-differences (DID) approach is employed to systematically assess the impact of the BRI on urban economic resilience at the city level. The key findings are as follows: (1) The findings show that the BRI has an enhancing effect on the economic resilience of cities along the routes, but governance is very weak, and urban resilience improves by 0.0045 units on average. Our findings imply that, while the BRI appears to be on the correct path, enhanced governance is necessary to implement city-specific planning approaches effectively. (2) The results of the moderating effect indicate that local governance capacity significantly amplifies the impact of the BRI on urban economic resilience, underscoring the critical role of institutional strength in the policy transmission process. (3) The heterogeneity analysis reveals significant regional disparities in policy effectiveness: while the BRI significantly improves economic resilience in eastern and central cities, it exerts a suppressive effect in western regions. This divergence is closely associated with variations in local governance capacity. In contrast, cities with stronger governance capabilities are more likely to experience positive outcomes, as confirmed by the significant moderating effect of local governance capacity. This study contributes to the growing literature on the spatial implications of national development strategies by empirically examining how the BRI reshapes urban economic resilience across regions. It offers important policy insights for enhancing the spatial governance of cities, particularly in aligning strategic infrastructure investment with differentiated local capacities. The findings also provide a valuable reference for land-use planning and regional development policies aimed at building resilient urban systems under conditions of global uncertainty. Full article

Multi-criteria decision analysis (MCDA) in furniture design is challenged by increasing product complexity and component proliferation. This study introduces a novel framework that integrates entropy reduction—achieved through dimensional standardization and modularity—as a core factor in the MCDA methodologies. The framework addresses both individual furniture evaluation and product family optimization through systematic complexity reduction. The research employed a two-phase methodology. First, a comparative analysis evaluated two furniture variants (laminated particleboard versus oak wood) using the Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The divergent rankings produced by these methods revealed inherent evaluation ambiguities stemming from their distinct mathematical foundations, highlighting the need for additional decision criteria. Building on these findings, the study further examined ten furniture variants, identifying the potential to transform their individual components into universal components, applicable across various furniture variants (or configurations) in a furniture line. The proposed dimensional modifications enhance modularity and interoperability within product lines, simplifying design processes, production, warehousing logistics, product servicing, and liquidation at end of lifetime. The integration of entropy reduction as a quantifiable criterion within MCDA represents a significant methodological advancement. By prioritizing dimensional standardization and modularity, the framework reduces component variety while maintaining design flexibility. This approach offers furniture manufacturers a systematic method for balancing product diversity with operational efficiency, addressing a critical gap in current design evaluation practices. Full article

Establishing scientifically sound and equitable compensation standards is crucial for effective ecological compensation. This study focuses on the quantitative assessment of ecological compensation standards in the water-source areas of the South-to-North Water Diversion Project. Based on the dual perspective of cost and benefit, we embed a three-dimensional dynamic adjustment coefficient—water volume, water quality, and payment capacity—and fully considered spillover effects. Using the AHP-Entropy Method, the allocation ratio of the water-receiving area was scientifically divided, achieving differentiated distribution and dynamic adaptation of the compensation mechanism. The compensation allocation ratio for water-receiving areas was determined, ensuring differentiated distribution and dynamic adaptability in the compensation mechanism. The results show the following: (1) In 2023, the ecological compensation amount for Yangzhou, based on the cost method and the equivalent factor method, ranges from CNY 1.21 billion to 2.53 billion. The amount of compensation after the dynamic game between both parties can avoid the waste of resources caused by over-compensation, and at the same time make up for the shortcomings of under-compensation due to the current water price. (2) Ecological compensation is measured only from the single perspective of the water-source area, without considering the differences in the receiving area. This paper uses the AHP-entropy value method to combine and assign weights, and calculates the apportionment ratio of the main water-receiving areas of Shandong Province in the east line of the South-to-North Water Diversion: for the Jiaodong line, these are Qingdao 20.97% and Jinan 14.53%, and for the North Shandong line, they are Dongying 23.98%, Dezhou 13.68%, Liaocheng 9.47%, and Binzhou 17.37%. (3) The dynamic correction coefficient and game model can effectively balance the cost of protecting the water-source area and the receiving area’s ability to pay, and combination with the empowerment method enhances the regional difference in suitability. Full article

The digital economy is a vital driver of industrial transformation and green development in China. This study aims to investigate how formal and informal environmental regulations affected the growth of the digital economy across Chinese prefecture-level cities between 2010 and 2020. Utilizing panel data from official statistical yearbooks and local bulletins, the research employs entropy weighting and ratio analysis to measure regulatory intensity and effectiveness. Spatial econometric models are applied to assess the direct and spillover effects of environmental regulation on digital economy development. Results indicate a nationwide strengthening of both formal and informal regulations, with formal mechanisms exerting a more pronounced influence on digital growth. Regional disparities are evident, with cities under stricter environmental oversight showing faster digital advancement. Spatial spillover effects exist but diminish with distance, likely due to institutional fragmentation. These findings underscore the need for integrated multi-level regulatory approaches, promoting synergy between formal and informal regulations and embedding environmental goals within land-use planning and digital infrastructure investment. The study offers new insights into the interplay between environmental governance and urban digital transformation, providing lessons relevant to China and other developing countries pursuing sustainable transitions. Full article

Ensuring dynamic risk management for intelligent connected vehicles (ICVs) in complex urban environments is critical as autonomous driving technology advances. This study presents three key contributions: (1) a comprehensive risk indicator system, constructed using entropy-based weighting, extracts 13-dimensional data on abnormal behaviors (e.g., speed, acceleration, position) to enhance safety and efficiency; (2) a multidimensional risk quantification method, simulated under single-vehicle and platooning modes on a CARLA-SUMO co-simulation platform, achieved >98% accuracy; (3) a cloud takeover strategy for high-level autonomous vehicles, directly linking risk assessment to real-time control. Analysis of 56,117 risk data points shows a 32% reduction in safety risks during simulations. These contributions provide methodological innovations and substantial data support for ICV field testing. Full article

Domain adaptation is a key approach to ensure that artificial intelligence models maintain reliable performance when facing distributional shifts between training (source) and testing (target) domains. However, existing methods often struggle to simultaneously preserve domain-invariant representations and discriminative class structures, particularly in the presence of complex covariate shifts and noisy pseudo-labels in the target domain. In this work, we introduce Conditional Rényi $\alpha$-Entropy Domain Adaptation, named CREDA, a novel deep learning framework for domain adaptation that integrates kernel-based conditional alignment with a differentiable, matrix-based formulation of Rényi’s quadratic entropy. The proposed method comprises three main components: (i) a deep feature extractor that learns domain-invariant representations from labeled source and unlabeled target data; (ii) an entropy-weighted approach that down-weights low-confidence pseudo-labels, enhancing stability in uncertain regions; and (iii) a class-conditional alignment loss, formulated as a Rényi-based entropy kernel estimator, that enforces semantic consistency in the latent space. We validate CREDA on standard benchmark datasets for image classification, including Digits, ImageCLEF-DA, and Office-31, showing competitive performance against both classical and deep learning-based approaches. Furthermore, we employ nonlinear dimensionality reduction and class activation maps visualizations to provide interpretability, revealing meaningful alignment in feature space and offering insights into the relevance of individual samples and attributes. Experimental results confirm that CREDA improves cross-domain generalization while promoting accuracy, robustness, and interpretability. Full article

This study develops a multi-dimensional sustainable tourism optimization framework for Juneau, Alaska, integrating economic, social, and environmental dimensions to balance tourism-driven prosperity with ecological and socio-cultural integrity. Utilizing a hybrid Analytic Hierarchy Process and entropy weighting method, the model assigns robust indicator weights. Optimized via the NSGA-II algorithm, it identifies an optimal tourist threshold, achieved through a strategic tax adjustment. This policy not only sustains economic revenue at USD 325 million but also funds a critical feedback loop: revenue reinvestment into environmental conservation and social infrastructure, which stabilizes cost indices and enhances community well-being. The model’s projections show this approach significantly mitigates environmental degradation, notably glacier retreat, and alleviates social pressures such as infrastructure overload and resident dissatisfaction. A key contribution of this research is the framework’s adaptability, which was validated through its application to Barcelona, Spain. There, the framework was recalibrated with social indicators tailored to address urban overtourism, achieving substantial reductions in housing and congestion costs alongside environmental improvements, while economic recovery was maintained. Sensitivity analyses confirm the model’s stability, though data limitations and subjective weighting suggest future enhancements via real-time analytics and dynamic modeling. Key policy recommendations include dynamic tourist caps, diversified attractions, and community engagement platforms, offering scalable solutions for global tourism destinations. This framework advances sustainable tourism by providing a blueprint to decouple economic growth from ecological and social harm, ensuring the longevity of natural and cultural assets amidst climate challenges. Full article

With the development of the power system, the ability to present a comprehensive and reasonable evaluation of its coordination level has become important for the collaborative optimization of source-grid-load-storage. By identifying uncertain risk factors fully, the present work develops a multi-level coordination-level evaluation of source-grid-load-storage based on AHP-entropy weighting. Building on previous studies, the present work reflects interactive characteristics of the collaborative optimization of source-grid-load-storage. Meanwhile, to determine the indicator weighting more reasonably, AHP-entropy weighting is adopted; this method combines the advantages of subjective AHP weighting and objective entropy weighting. Firstly, the multi-level coordination-level evaluation of source-grid-load-storage is introduced and includes both direct factors and indirect factors. Next, based on AHP-entropy weighting, the indicator weighting of the multi-level coordination-level evaluation is determined. Lastly, a case study is conducted that involves evaluating the coordination levels of the power systems of three regions. Additionally, the effectiveness of the multi-level coordination-level evaluation of source-grid-load-storage is validated. Full article

With the increasing content richness of social media platforms, Multimodal Named Entity Recognition (MNER) faces the dual challenges of heterogeneous feature fusion and accurate entity recognition. Aiming at the key problems of inconsistent distribution of textual and visual information, insufficient feature alignment and noise interference fusion, this paper proposes a multimodal named entity recognition model based on dual-stream Transformer: CASF-MNER, which designs cross-modal cross-attention based on visual and textual features, constructs a bidirectional interaction mechanism between single-layer features, forms a higher-order semantic correlation modeling, and realizes the cross relevance alignment of modal features; construct a dynamic perception mechanism of multimodal feature saliency features based on multiscale pooling method, construct an entropy weighting strategy of global feature distribution information to adaptively suppress noise redundancy and enhance key feature expression; establish a deep semantic fusion method based on hybrid isomorphic model, design a progressive cross-modal interaction structure, and combine with contrastive learning to realize global fusion of the deep semantic space and representational consistency optimization. The experimental results show that CASF-MNER achieves excellent performance on both Twitter-2015 and Twitter-2017 public datasets, which verifies the effectiveness and advancement of the method proposed in this paper. Full article

The bond market serves dual roles in fiscal and financial spheres, playing a crucial role in coordinating monetary policy. This paper investigates the impact of quantitative and price-based monetary policies on the liquidity level of China’s bond market. A comprehensive index measuring the liquidity of the local bond market is constructed using a combination weighting method that integrates the entropy method and the coefficient of variation. Employing the time-varying stochastic volatility structure vector autoregression (TVP-SV-SVAR) model on data spanning from 2013 to 2021, this study empirically compares the impulse response of local bond market liquidity to monetary policy shocks. The findings reveal that both types of monetary policy operations exhibit asymmetric, nonlinear, and time-varying impacts on bond market liquidity. Quantitative monetary instruments induce deeper impulse responses, with longer-lasting effects. These conclusions offer insights for monetary policy reforms and bond market development in China. Full article