Search Results (3,040)

Achieving sustainable development in emerging cultural and tourism communities requires not only economic efficiency, but also the long-term revaluation and adaptive integration of cultural and tourism resources. A key challenge lies in integrating diverse and interdependent resources in ways that enhance cultural value, satisfy heterogeneous visitor demands, and maintain resilience under uncertainty. As many emerging cultural tourism communities rely on newly constructed, place-based cultural scenes rather than historically rooted heritage, conventional resource evaluation approaches often fail to capture the cultural and social dimensions essential for sustainability. To address this gap, this study proposes a sustainability-oriented resource integration framework for emerging cultural tourism communities. Drawing on scene theory and customer value theory, a quantitative evaluation system is developed to measure tourists’ perceived spatial quality while explicitly incorporating community interaction and social influence. Based on this evaluation, a multi-objective optimization model is constructed to balance perceived spatial quality, system dynamic adaptability, and tourism suppliers’ cost expectation fulfillment. The model is solved using an ant colony aggregation-inspired dynamic allocation algorithm and validated through a case study in China. The results show that integrating spatial quality and community influence into resource selection enhances cultural sustainability and system resilience, while avoiding short-term, efficiency-driven development. This study provides a decision-support approach for responsible, community-oriented local development. Full article

At present, large-scale hazardous chemical logistics warehouses are characterized by complex structural layouts, diverse stored materials, and high operational risks, which pose significant challenges to fire emergency response. The awareness of hazardous material inventory, orderliness, and timeliness of on-site reconnaissance directly determine the efficiency and safety of firefighting and rescue operations. In response to these challenges, this study, based on 77 fire cases involving hazardous chemical logistics warehouses, proposes an evaluation framework that integrates a TOWA–TOWGA hybrid operator with complex network analysis. Accordingly, a fire scene core reconnaissance task identification model is developed. The new model is capable of identifying key reconnaissance tasks while capturing the dynamic evolutionary patterns of fire development across three distinct stages. The research findings demonstrate that identifying the fire’s spread direction, locating accessible water sources, and pinpointing the fire’s ignition point constitute the core tasks throughout the entire fire emergency response cycle. The priority ranking of these core tasks exhibits distinct temporal variability as the fire evolves dynamically. This model enables the accurate identification of key reconnaissance tasks and critical operational pathways, thereby providing robust theoretical support and a solid practical foundation for fire rescue teams to optimize resource allocation strategies and formulate science-based reconnaissance protocols. Full article

Multi-unmanned aerial vehicle (UAV) systems are crucial for establishing resilient communication networks in disaster-stricken areas, but their limited energy and dynamic characteristics pose significant challenges for sustained and reliable service provision. Optimizing resource allocation in this situation is a complex sequential decision-making problem, which is naturally suitable for multi-agent reinforcement learning (MARL). However, the most advanced MARL methods (e.g., multi-agent proximal policy optimization (MAPPO)) often encounter difficulties in the “loosely coupled” multi-UAV environment due to their overly centralized evaluation mechanism, resulting in unclear credit assignment and inhibiting personalized optimization. To overcome this, we propose a novel hierarchical framework supported by MAPPO with decoupled critics (MAPPO-DC). Our framework employs an efficient clustering algorithm for user association in the upper layer, while MAPPO-DC is used in the lower layer to enable each UAV to learn customized trajectories and power control strategies. MAPPO-DC achieves a complex balance between global coordination and personalized exploration by redesigning the update rules of the critic network, allowing for precise and personalized credit assignment in a loosely coupled environment. In addition, we designed a composite reward function to guide the learning process towards the goal of proportional fairness. The simulation results show that our proposed MAPPO-DC outperforms existing baselines, including independent proximal policy optimization (IPPO) and standard MAPPO, in terms of communication performance and sample efficiency, validating the effectiveness of our tailored MARL architecture for the task. Through model robustness experiments, we have verified that our proposed MAPPO-DC still has certain advantages in strongly coupled environments. Full article

The transition to green hydrogen is critical for achieving sustainable energy systems and climate goals. This study presents MODERHydrogen-H₂, a comprehensive framework for assessing solar- and wind-based green hydrogen production, fossil fuel substitution, and greenhouse gas (GHG) reduction. The method integrates Geographic Information Systems (GIS) to optimize renewable energy resource allocation while adhering to sustainability criteria. Applied to four solar sites (2000 MW) in Colombia’s Magdalena–Cauca Basin and three wind projects (1700 MW) in the Caribbean Basin, the model estimates an annual production of 211,074 tons of green hydrogen by 2030. This output could displace 37,221 terajoules of fossil fuels, contributing 2.5% to the national energy matrix and reducing CO₂ emissions by 10.09 million tons. MODERHydrogen-H₂ demonstrates scalability and adaptability, offering a decision-support tool for global energy transition strategies. Its implementation supports affordable, reliable, and low-carbon energy systems, aligning with Sustainable Development Goals (SDGs) targets. The model offers a single platform from which to simulate renewable energy potential in a sustainable manner within a given geographical area, develop scenarios for modifying the energy matrix of a country or region, simulate rational and efficient water supply and demand for energy uses, including aspects of climate change, calculate green hydrogen production in a sustainable manner, and finally calculate greenhouse gas emissions. Full article

Manufacturing industries are increasingly applying sustainable closed-loop supply chains (CLSCs) to meet economic, environmental, and societal goals. The increasing complexity and interdependence associated with the sustainability CLSCs make them highly vulnerable to disruption risks that threaten continuity and sustainability. However, prior studies fall short of guiding how disruption risks in sustainable CLSCs can be systematically prioritized under uncertainty in a stable and decision-relevant manner. To fill this literature void, this study develops a hybrid of the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) method and the genetic algorithm (GA) technique to prioritize disruption risks under uncertainty. Triangular fuzzy numbers are used to capture the imprecision of 13 experts from industry and academia, whereas the GA technique used aimed to improve stability and reduce the variability commonly observed in conventional fuzzy multi-criteria decision-making methods. The method is validated through a real-world case study, identifying supplier disruption risk, route disruption risk, and industrial accidents as the most critical risks. Moreover, sensitivity analysis is conducted to validate the robustness of GA-based Fuzzy-TOPSIS, demonstrating its superior stability and reliability compared to the classical Fuzzy-TOPSIS method in uncertain environments. The novelty of this study lies in embedding a GA-driven approach within the fuzzy-TOPSIS structure to explicitly address ranking instability under uncertainty in sustainable CLSCs. The study provides significant theoretical contributions by enhancing multi-attribute decision-making regarding disruption risk in sustainable CLSC literature, as well as practical insights for decision-makers to efficiently allocate resources by focusing mitigation investments on consistently high-priority risks instead of low-priority ones. Full article

Digital technologies such as big data are reshaping resource allocation, raising interest in whether and how heterogeneous science and technology innovation (STI) policies can help unlock urban carbon lock-in. Using panel data for 286 prefecture-level cities in China from 2009 to 2023, this paper examines the relationship between heterogeneous STI policy intensity—classified as supply-side, demand-side, complementary-factor, and institutional-reform policies—and urban carbon unlocking efficiency. We develop a mechanism-based framework and empirically assess (i) the moderating roles of digital infrastructure, science and technology finance, and government green attention, and (ii) spatial spillover effects using spatial econometric models. The results show that all four policy types show a significant positive association with local carbon unlocking efficiency, with institutional-reform policies exhibiting the strongest association. When the four types are included jointly, only supply-side and demand-side policies retain statistically significant direct associations. Heterogeneity analyses indicate that demand-side, complementary-factor, and institutional-reform policies are more strongly associated with efficiency gains in low-pollution cities, whereas supply-side and demand-side policies have a stronger association in high energy-consuming cities. Mechanism analysis reveals that regional digital infrastructure exerts a selective moderating effect on the relationship between heterogeneous sci-tech innovation policies and urban carbon emission reduction efficiency. It positively reinforces the effectiveness of supply-side, demand-side, and institutional reform-oriented policies, while its interaction with complementary policies is statistically insignificant. Technology finance and government green policies function as a “resource catalyst” and an “institutional guarantee” respectively, significantly enhancing the correlation between heterogeneous sci-tech innovation policies and urban carbon emission reduction efficiency. Finally, carbon unlocking efficiency displays significant spatial dependence: the intensity of supply-side and institutional-reform policies is positively associated with carbon unlocking efficiency in neighboring cities, while complementary-factor policies exhibit a negative spatial association. Overall, the findings provide empirical evidence to inform the design and coordination of heterogeneous STI policy portfolios aimed at improving urban carbon unlocking efficiency. Full article

In logistics systems, the organization of information retrieval plays a key role in human interaction with technical systems to ensure decision-making speed, route optimization, planning, and resource allocation. At the same time, the efficiency of the logistics system when simultaneously processing large volumes of data and constantly updating it is determined by the speed of processing user requests and the accuracy of the responses provided by the system. Within the retrieval-augmented generation architecture, a hybrid information retrieval method has been proposed, based on the combined use of a vector-graph data representation structure and large language model. Experiments showed that the hybrid method achieved best accuracy rates of 0.24–0.25 (among all considered methods) with enhanced scalability capabilities (when the number of nodes increases fourfold, the time increases only twofold—from 0.09 s to 0.20 s) due to the limitation of the graph traversal area when implementing the graph component of the hybrid search. An optimal range of 30–50 nodes to be traversed was also identified, balancing precision and query processing speed. The findings are of practical value to logistics system developers and supply chain managers aiming to implement high-precision, natural language-based information retrieval in dynamic operational environments. Full article

Railway passenger flow forecasting plays a critical role in operational efficiency and resource allocation for transportation systems. However, existing deep learning approaches suffer significant performance degradation when facing rare but high-impact events, primarily due to sample scarcity and their inability to distinguish between routine patterns and disruption regimes. To address these challenges, this study introduces EA-ARIMA-Informer, an adaptive forecasting framework that integrates entropy-augmented ARIMA with Informer through an entropy-guided regime-switching mechanism. The passenger flow series is characterized through a multi-dimensional entropy space comprising four complementary measures: Sample Entropy quantifies local regularity and predictability, Permutation Entropy captures the complexity of ordinal dynamics, Transfer Entropy measures causal information flow from external events (holidays, weather) to passenger demand, and the Conditional Entropy Growth Factor (CEGF)—a novel metric introduced herein—detects regime transitions by tracking the rate of uncertainty change between consecutive time windows. These entropy indicators serve dual roles as feature inputs for representation learning and as state identifiers for segmenting the time series into stable and fluctuating regimes with distinct predictability properties. An adaptive dual-path architecture is then designed accordingly: EA-ARIMA handles low-entropy stable regimes where linear seasonality dominates, while EA-Informer processes high-entropy fluctuating regimes requiring nonlinear residual modeling, with CEGF-guided gating dynamically controlling component weights. Unlike conventional black-box gating mechanisms, this entropy-based switching provides physically interpretable signals that explain when and why different model components dominate the forecast. The framework is validated on a large-scale dataset covering nearly 300 Chinese cities over three years (2017–2019), encompassing normal operations, holiday peaks, and extreme weather disruptions. Experimental results demonstrate that EA-ARIMA-Informer achieves a MAPE of 4.39% for large-scale cities and 7.82% for data-scarce small cities (Tier-3), substantially outperforming standalone ARIMA, XGBoost, and Informer, which yield 15.95%, 13.75%, and 12.87%, respectively, for Tier-3 cities. Ablation studies confirm that both entropy-based feature augmentation and CEGF-guided regime switching contribute significantly to these performance gains, establishing a new paradigm for interpretable and adaptive forecasting in complex transportation systems. Full article

In the context of global decarbonization and digital transformation, this study investigates whether and how the adoption of artificial intelligence (AI) promotes corporate energy transition, as measured by firms’ total energy consumption, energy intensity, and carbon emission intensity. Drawing on the theories of general-purpose technology (GPT), the resource-based view (RBV), and dynamic capabilities, the paper conceptualizes AI as a production-embedded technological capability that enhances intelligent automation, energy monitoring, and resource coordination within firms. Using panel data on Chinese A-share listed firms from 2012 to 2024, and capturing AI adoption through observable changes in firms’ production-related capital intensity, the analysis employs firm- and year-fixed effects, instrumental variables, and a dynamic event-study design to address endogeneity and temporal dynamics. The results show that AI adoption reduces firms’ energy consumption by approximately 2.0%, energy intensity by 1.8%, and carbon emission intensity by 2.3% within two to three years after adoption. Mechanism tests indicate that green innovation, operational efficiency, and resource allocation efficiency mediate this effect. Heterogeneity analyses reveal more substantial effects among non-state, large-scale, and technology-intensive firms operating in highly marketized regions. The findings broaden understanding of AI as a strategic sustainability technology and provide actionable implications for policymakers to align digital and energy governance to achieve carbon neutrality goals. Full article

Probabilistic streamflow models play a pivotal role in quantifying hydrological uncertainty and form the backbone of modern risk management strategies for flood and drought forecasting, water allocation planning, and the design of resilient infrastructure. Unlike deterministic approaches that yield single-point estimates, these models provide a spectrum of possible outcomes, enabling a more realistic assessment of extreme events and supporting informed, sustainable water resource decisions. By explicitly accounting for natural variability and uncertainty, probabilistic models promote transparent, robust, and equitable risk evaluations, helping decision-makers balance economic costs, societal benefits, and environmental protection for long-term sustainability. In this study, we introduce the bounded half-logistic distribution (BHLD), a novel heavy-tailed probability model constructed using the T–Y method for distribution generation, where T denotes a transformer distribution and Y represents a baseline generator. Although the BHLD is conceptually related to the Pareto and log-logistic families, it offers several distinctive advantages for streamflow modeling, including a flexible hazard rate that can be unimodal or monotonically decreasing, a finite lower bound, and closed-form expressions for key risk measures such as Value at Risk (VaR) and Tail Value at Risk (TVaR). The proposed distribution is defined on a lower-bounded domain, allowing it to realistically capture physical constraints inherent in flood processes, while a log-logistic-based tail structure provides the flexibility needed to model extreme hydrological events. Moreover, the BHLD is analytically characterized through a governing differential equation and further examined via its characteristic function and the maximum entropy principle, ensuring stable and efficient parameter estimation. It integrates a half-logistic generator with a log-logistic baseline, yielding a power-law tail decay governed by the parameter $\beta$, which is particularly effective for representing extreme flows. Fundamental properties, including the hazard rate function, moments, and entropy measures, are derived in closed form, and model parameters are estimated using the maximum likelihood method. Applied to four real streamflow data sets, the BHLD demonstrates superior performance over nine competing distributions in goodness-of-fit analyses, with notable improvements in tail representation. The model facilitates accurate computation of hydrological risk metrics such as VaR, TVaR, and tail variance, uncovering pronounced temporal variations in flood risk and establishing the BHLD as a powerful and reliable tool for streamflow modeling under changing environmental conditions. Full article

Efficient resource allocation during fruit expansion and ripening is critical for enhancing mango (Mangifera indica L.) productivity and fruit quality. A study was conducted to quantify the effects of foliar-applied GA₃ at concentrations of 0 (control), 50 (GA50), 100 (GA100) and 200 (GA200) mg L⁻¹, applied at 15, 25 and 35 days after full bloom, on fruit physiochemical attributes during the fruit expansion and ripening phases. In addition, metabolic profiling and pathway analysis were conducted after fruit ripening. Compared with the control, GA₃ application at 50, 100, and 200 mg L⁻¹ increased fruit length by 8, 12, and 14%, and fruit diameter by 5, 11, and 14%, respectively. The mean single-fruit weight was increased by 5–11% at physiological maturity. During the fruit expansion phase, GA₃ treatment decreased starch and total acidity by up to 11% and 29%, respectively, while increasing the soluble sugar content by 21%. Furthermore, enhanced antioxidant enzyme activities (SOD, POD, and CAT), accompanied by a reduction in malondialdehyde (MDA) contents in leaves, were observed. At the ripening stage, GA₃-treated fruits exhibited lower weight loss, higher firmness, more uniform color development, and reduced disease incidence, although vitamin C content and total soluble solids declined. PCA analysis identified GA100 as the optimal treatment. Metabolomics analysis revealed 287 differentially regulated metabolites between GA100 and the control. Sweet, fruity, and floral compounds were upregulated, whereas terpenoids and aldehydes were downregulated. KEGG pathway analysis indicated that GA100 modulated key resource-related metabolic pathways, including nitrogen, carbon and energy metabolism, thereby promoting efficient resource allocation toward fruit growth, quality, and aroma development. Overall, preharvest foliar application of GA₃, particularly at a concentration of 100 mg L⁻¹ (GA100), markedly improved mango fruit growth and quality but tended to simplify the aroma profiles by favoring ester production over complex terpenoid-derived notes. Full article

Artificial intelligence (AI) has become an integral driver of digital transformation in the banking sector, fundamentally influencing operational efficiency, resource allocation, and profitability. This study investigates how AI adoption affects the profitability of Chinese commercial banks and through which mechanisms these effects occur, within the context of the country’s broader financial digitalization process. Using panel data for 17 A-share listed banks in China from 2009 to 2022, we employ a multi-period difference-in-differences (DID) framework—whose validity rests on the parallel trend assumption, empirically verified through an event-study specification—and combine it with propensity score matching (PSM) and placebo simulations to ensure credible causal identification. The results indicate that AI adoption significantly improves bank profitability. Mechanism analyses suggest that AI enhances profitability through two overarching channels—operational efficiency and resource allocation—manifested in (i) higher cost elasticity of income, (ii) improved deposit–loan turnover adaptability via more efficient liquidity and funding-cycle management, and (iii) optimized cross-business capital allocation efficiency through better risk–return matching in diversified operations. The effects are stronger for banks with higher digital investment intensity and tighter customer stickiness–liability cost coupling, and vary systematically across ownership types, bank sizes, and policy cycles. Overall, the findings provide policy-relevant evidence on how AI-driven digital transformation can enhance bank performance and risk management in modern financial systems. This study contributes by constructing a disclosure-based AI adoption measure from bank annual reports and exploiting staggered adoption with a multi-period DID design to provide causal evidence from China’s listed banking sector. Full article

Under intensifying climate change and anthropogenic pressures, extreme low-flow events increasingly jeopardize water security in the Yellow River water supply region. This study develops the Inter-basin Multi-source Water Joint Allocation and Interconnected Routes Regulation System (IMWA-IRRS) to optimize spatiotemporal allocation of multi-source water and simulate topological relationships in complex water networks. The model integrates system dynamics simulation with multi-objective optimization, validated through multi-criteria calibration using three performance indicators: correlation coefficient (R), Nash-Sutcliffe Efficiency (E_ns), and percent bias (PBIAS). Application results demonstrated exceptional predictive performance in the study area: Monthly runoff simulations at four hydrological stations yielded R > 0.98 and E_ns > 0.98 between simulated and observed data during both calibration and validation periods, with |PBIAS| < 10%; human-impacted runoff simulations at four hydrological stations achieved R > 0.8 between simulated and observed values, accompanied by PBIAS within ±10%; sectoral water consumption across the Yellow River Basin exhibited PBIAS < 5%, while source-specific water supply simulations maintained PBIAS generally within 10%. Comparative analysis revealed the IMWA-IRRS model achieves simulation performance comparable to the WEAP model for natural runoff, human-impacted runoff, water consumption, and water supply dynamics in the Yellow River Basin. The 2035 water allocation scheme for Yellow River water supply region projects total water supply of 59.691 billion m³ with an unmet water demand of 3.462 billion m³ under 75% low-flow conditions and 58.746 billion m³ with 4.407 billion m³ unmet demand under 95% low-flow conditions. Limited coverage of the South-to-North Water Diversion Project’s Middle and Eastern Routes constrains water supply security, necessitating future expansion of their service areas to leverage inter-route complementarity while implementing demand-side management strategies. Collectively, the IMWA-IRRS model provides a robust decision-support tool for refined water resources management in complex inter-basin diversion systems. Full article

To address the reduction in energy efficiency caused by severe signal attenuation during long-distance transmission in cognitive radio networks, this paper constructs an IRS-assisted and energy-constrained relay cognitive radio resource allocation model operating in the underlay mode. By introducing controllable reflective paths, the model enhances link quality and improves energy utilization efficiency. Our objective is to maximize the energy efficiency of secondary users while satisfying the interference constraints imposed on the primary user. To effectively solve the highly non-convex and high-dimensional optimization problem, we propose a Chaotic Spider Wasp Optimization algorithm. The algorithm employs chaotic mapping to initialize the population and enhance population diversity, and incorporates a dynamic trade-off factor to achieve an adaptive balance between hunting and nesting behaviors, thereby improving global search capability and avoiding premature convergence. In addition, the Jain fairness index is introduced to enforce fairness in the power allocation among secondary users. Simulation results demonstrate that the proposed model and optimization method significantly improve system energy efficiency and the stability of communication quality. Full article

Long-term continuous cropping in cotton fields of Southern Xinjiang has limited crop productivity. To investigate how subsoiling depth regulates ecosystem-level water partitioning and thereby enhances water productivity mechanisms, a two-year field experiment was conducted in a mulched drip irrigation cotton field in Southern Xinjiang. Using a non-subsoiled field in the current season (CT) as the control, three subsoiling depths were established: subsoiling at 30 cm (ST1), 40 cm (ST2), and 50 cm (ST3). Changes in evapotranspiration partitioning and water use efficiency were analyzed. The results showed that subsoiling enhanced the utilization of deep soil water. Compared with CT, the ST2 and ST3 treatments significantly reduced soil water storage in the 0–60 cm layer during the flower opening to boll-setting stages, while soil water consumption increased by 26.4 mm and 28.8 mm, respectively. We demonstrate that subsoiling depth exerts a predominant control on the partitioning of evapotranspiration. Increasing subsoiling depth systematically shifted water loss from non-productive soil evaporation to productive plant transpiration, with the ST2 and ST3 treatments decreasing seasonal soil evaporation by 24.1% and 25.1%, respectively, and increasing plant transpiration by 21.9% and 22.8%, and lowering the Es/ET (where Es is soil evaporation and ET is evapotranspiration) ratio by 22.1% and 27.1%. However, this maximal physiological water-saving did not yield the optimal agronomic return. We established a non-linear relationship in which the ST2 treatment uniquely achieved the maximum seed cotton yield, WUE (water use efficiency), and IWUE (irrigation water use efficiency) (increasing by up to 34.4%, 17.2%, and 23.4%, respectively). This optimal depth better balances water resource allocation and reproductive growth. We conclude that under sandy loam soil conditions in typical mulched drip-irrigated cotton areas of Southern Xinjiang, implementing an optimal subsoiling depth (40 cm) can engineer a more resilient soil–plant–water continuum, providing a feasible pathway toward enhancing water and crop production sustainability. Full article