Search Results (1,072)

Climate change disproportionately threatens low- and middle-income countries, yet integrated assessments combining socio-economic fragility with physical hazards remain limited. This study quantifies multi-dimensional climate vulnerability and derives optimized adaptation policies for six representative nations (Bangladesh, Colombia, Kenya, Morocco, Pakistan, Vietnam) by fusing socio-economic indicators with climate risk data (2000–2024). A computational framework integrating unsupervised learning, dimensionality reduction, and predictive modeling was employed. Principal Component Analysis synthesized eight indicators into a Compound Vulnerability Score (CVS), while K-Means and DBSCAN identified distinct vulnerability regimes. XGBoost quantified driver importance, and Graph Neural Networks captured systemic interconnections. XGBoost identified projected drought risk (31.2%), precipitation change (18.1%), and poverty headcount (14.3%) as primary drivers. Graph networks demonstrated significant risk amplification in African nations (Morocco SRS: 0.728–0.874; Kenya SRS: 0.504–0.641) versus damping in Asian countries. A Reinforcement Learning (RL) agent was trained using Deep Q-Networks with experience replay to optimize intervention portfolios under budget constraints. The RL policy achieved a 23% reduction in systemic risk compared to uniform allocation baselines, generating context-specific priorities: drought management for Morocco (score 50) and Pakistan (40); poverty alleviation for Kenya (40); coastal protection for Bangladesh (40); agricultural resilience for Vietnam (35); and institutional capacity building for Colombia (50). In conclusion, socio-economic fragility non-linearly amplifies climate hazards, with poverty and drought risk constituting critical vulnerability multipliers. The AI-driven framework demonstrates that targeted interventions in high-sensitivity systems maximize systemic risk reduction. This integrated approach provides a replicable, evidence-based foundation for strategic adaptation finance allocation in an increasingly uncertain climate future. Full article

The effects of progressive drought stress were examined in four economically important plant species belonging to the Lamiaceae family: catnip (Nepeta cataria L.), lavender (Lavandula angustifolia Mill.), holy basil (Ocimum tenuiflorum L.), and perilla mint (Perilla frutescens (L.) Britton). Plants were grown in a controlled pot experiment under three soil water capacity levels: 70% (control), 50% (moderate stress), and 30% (severe stress), and the drought stress lasted for 30 days. The study evaluated a comprehensive set of leaf micromorphological parameters, including the density and diameter of glandular trichomes, stomatal density and size, and the thickness of the lamina, mesophyll, epidermis, cuticle, and parenchymal layers. In addition, essential oil (EO) content, total flavonoid content (TFC), and elemental composition were analyzed. Drought responses were strongly species-specific. O. tenuiflorum, P. frutescens, and N. cataria showed high sensitivity characterized by reduced biomass and thinning of leaf tissues. These changes were accompanied by typical xeromorphic adaptations, such as increased stomatal and glandular trichome density, and reduced stomatal size. L. angustifolia exhibited pronounced cuticle thickening, suggesting an effective structural mechanism to minimize water loss. Secondary metabolism also responded differently among species. In some cases, drought shifted metabolic allocation toward flavonoid accumulation at the expense of essential oils, whereas in others, moderate stress promoted the co-accumulation of both compounds. These patterns indicate distinct adaptive strategies linking anatomical plasticity with metabolic regulation. Overall, moderate drought supported adaptive responses, while severe water limitation impaired growth and metabolic production. From a practical perspective, maintaining moderate soil water availability appears critical to optimize both plant performance and the accumulation of valuable secondary metabolites in Lamiaceae species. Full article

The dynamism of adventure tourism necessitates the precise identification of areas with suitable natural, infrastructural, and service capacities for hosting activities. The aim of this study is to assess the multi-scenario spatial suitability for the sustainable development of adventure tourism camps using a Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach. The datasets used included topographic, climatic, environmental, accessibility, natural and cultural attraction, and service infrastructure indicators. The relevant criteria were first standardized, and their weights were determined using the Analytic Hierarchy Process (AHP). Subsequently, the layers were integrated through a Weighted Linear Combination (WLC) model. Four scenarios were designed for sensitivity analysis: the first scenario with balanced weight distribution (S_bal), the second prioritizing accessibility (S_acc), the third focusing on natural attractions (S_att), and the fourth emphasizing services (S_serv). The results indicated that approximately 21% and 9% of Chaharmahal and Bakhtiari province have high and very high potential for adventure activities, respectively, which were selected as initial options for the multi-scenario analysis. In the balanced (S_bal) scenario, 31% and 13% of the area of these options fell into high and very high suitability classes, respectively. The Service-Based Scenario (S_serv) increased the share of high and very high suitability areas to 34% and 19%, while Accessibility-Based Scenario (S_acc) reduced these classes to 27% and 10%. In the Attraction-Based Scenario (S_att), the areas in the high and very high suitability classes were 30% and 12%, respectively. The findings demonstrate that altering the priority of components can significantly change the spatial pattern of suitability, and sustainable planning of adventure tourism activities should be conducted based on management objectives and regional capacities. The proposed framework is generalizable to other regions and can serve as a basis for decision-making in balanced development, optimal infrastructure allocation, and sustainable management of adventure tourism. Full article

Universities play a critical role in knowledge creation and technological innovation, serving as key drivers of regional development. However, existing research has paid limited attention to the mechanisms through which university innovation inputs translate into firm-level performance, particularly in the context of science and technology corridors in emerging economies. This study investigates how university innovation resources affect enterprise performance in the G60 Science and Technology Corridor within China’s Yangtze River Delta, one of the country’s most dynamic innovation regions. Using a panel dataset of 55 universities across nine cities from 2008 to 2017, we employ spatial analysis and fixed-effects panel regression models to examine the relationship between university innovation inputs and firm performance and further explore the mediating roles of local human capital and firm R&D investment. The results show that university innovation inputs significantly enhance enterprise performance, although excessive human resource inputs exhibit a negative effect on both short-term and long-term outcomes. Local human capital and firm R&D investment serve as key mediating mechanisms, with input and output resources influencing enterprise performance through distinct pathways. Heterogeneity analysis reveals that non-state-owned enterprises and small- and medium-sized enterprises derive greater long-term benefits from university resources. These findings contribute to the literature by clarifying the conceptual distinction between university innovation inputs and outputs, and by demonstrating the micro-level mechanisms—R&D investment and human capital—through which university-generated knowledge affects firm performance. The results also provide empirical evidence from an emerging economic context, extending the applicability of knowledge spillover and absorptive capacity theories. Policy implications include optimizing university human resource allocation, strengthening university–enterprise collaboration, and providing targeted support for non-state-owned enterprises and SMEs. Future research may extend the analysis to include institutional factors and university heterogeneity. Full article

Airport stand allocation research has traditionally focused on optimizing assignments within fixed infrastructure configurations, while strategic decisions regarding stand category composition remain underexplored. This study investigates how different proportional distributions of stand categories affect system-level performance under high traffic demand at international airports. A discrete-event simulation model implemented in MATLAB evaluates fifteen infrastructure configurations with varying distributions of small, medium, and large stands, classified according to the ICAO Annex 14. The model employed a first-come–first-served allocation logic to isolate infrastructure-driven effects from algorithmic decision-making. System throughput was measured through acceptance and rejection rates, disaggregated by aircraft stand category. Acceptance rates ranged from 33% to 92% across tested configurations, demonstrating pronounced sensitivity to stand composition. Balanced configurations consistently outperformed asymmetric alternatives. Insufficient stand availability in any single category led to concentrated rejection patterns and non-linear performance degradation; excess capacity in unconstrained categories could not compensate for shortfalls in constrained ones. Proportionality across stand categories is identified as a critical determinant of infrastructure robustness. The proposed simulation framework provides a computationally efficient tool for early-stage (pre-operational planning phase) infrastructure screening, supporting informed strategic capacity decisions prior to detailed operational optimization. Full article

As a crucial pathway for optimizing land factor allocation, farmland transfer plays a pivotal role in implementing the “storing grain in land and technology” strategy and safeguarding national grain security. Based on panel data from 30 provinces in China spanning 2009 to 2023, this study employs a two-way fixed effects model and a Spatial Durbin Model (SDM) to systematically examine the mechanisms, heterogeneity, and spatial spillover effects of farmland transfer on grain production capacity. The results indicate that: (1) Farmland transfer significantly enhances grain production capacity, and this conclusion remains robust after multiple robustness and endogeneity tests. (2) Farmland transfer boosts grain production capacity by promoting cultivated land connectivity and facilitating the substitution of machinery for labor; however, the accompanying non-grain tendency and land governance disputes exert inhibitory effects on capacity release. (3) Transfers to farming households and professional cooperatives, as well as the adoption of leasing and informal exchange arrangements, exhibit the strongest positive effects on production capacity, and the scale-efficiency gains of farmland transfer are particularly pronounced in major grain-consuming areas. (4) Improvements in a region’s farmland transfer level drive the enhancement of grain production capacity in neighboring regions through the diffusion of management experience and the sharing of social services. This study provides empirical evidence and policy insights to optimize farmland transfer mechanisms and safeguard food security. Full article

Agricultural economic resilience plays a pivotal role in the integrated development of agriculture and rural areas, and carries great significance for ensuring national food security and advancing sustainable agricultural development in the context of complex risks and challenges. Using panel data covering 41 cities in the Yangtze River Delta region from 2011 to 2023, this paper empirically investigates the impact mechanism of fiscal support for agriculture on agricultural economic resilience. The results demonstrate that fiscal support for agriculture in the Yangtze River Delta exerts a significant positive effect on agricultural economic resilience, especially with a pronounced promoting influence on resistance capacity. Mechanism analysis indicates that fiscal support for agriculture indirectly affects agricultural economic resilience through channels including agricultural industrial agglomeration and the urban–rural income gap. Accordingly, to strengthen agricultural economic resilience, it is necessary to optimize the allocation and expenditure structure of fiscal funds, adopt differentiated strategies with dynamic and timely adjustments, allocate funds to boost agricultural industrial agglomeration, enhance investment in human capital to narrow the urban–rural income gap, and facilitate sustainable agricultural development. Full article

Non-orthogonal multiple access (NOMA) has become one of the main enabling technologies for next-generation cellular networks. The ability to allocate multiple users on the same frequency resources simultaneously leads to improved spectral efficiency. This paper examines power allocation and user pairing for NOMA networks with an objective to enhance the sum spectral efficiency (sum capacity, bps/Hz) while guaranteeing the target rate of the far user. Two benchmark methods were used to evaluate the performance of the proposed scheme: (1) fixed power allocation, in which fixed power coefficients are allocated to the near and far users, and (2) random power allocation, where random coefficients are assigned to the users. However, these static methods fail to adapt to instantaneous channel conditions and may lead to reduced performance for the weak user and inefficient power utilization. To manage these limitations, a novel interference-limited power allocation (IL-PA) scheme is proposed. In the IL-PA, the power allocation coefficients are dynamically allocated to users according to an interference threshold. The proposed scheme guarantees that the interference induced by the near user does not exceed a predefined interference threshold; thus, the target rate of the far user is achieved. The proposed interference threshold is derived theoretically to enhance the overall system capacity and optimize the signal-to-interference-plus-noise ratio (SINR). Additionally, a user pairing scheme, which separates users into two groups according to their channel gains, is proposed to reduce complexity while preserving good performance. The simulation results show that the proposed power allocation and user pairing scheme outperforms the benchmark methods in terms of overall capacity. Full article

With the rapid evolution toward 6G networks, ensuring robust physical layer security (PLS) in highly dynamic and heterogeneous wireless environments has become a key challenge. Traditional security methods often struggle to adapt to time-varying channels, especially in the absence of perfect channel state information. Furthermore, the dynamic nature of node selection and power allocation in heterogeneous networks creates a complex hybrid action space operating across multiple timescales, significantly complicating the design of efficient and adaptive security strategies. To address this, this paper proposes a novel constrained hierarchical reinforcement learning (CHRL) framework for secure cooperative communications in next-generation wireless systems. The framework is designed to optimize secrecy performance within a hybrid action space comprising both discrete node selection and continuous power allocation, operating at different timescales. By hierarchically decoupling the joint optimization problem, the upper layer performs risk-aware node selection to maximize long-term secrecy capacity (SC) while guaranteeing a stable and secure link. At the lower layer, we develop a constrained MiniMax Multi-objective Deep Deterministic Policy Gradient (M3DDPG) algorithm that optimizes power allocation considering worst-case conditions. Lagrange multipliers are integrated to enforce a strictly positive SC constraint throughout transmission, effectively preventing security outages. Simulation results under time-varying Rayleigh fading channels demonstrate that the proposed CHRL framework outperforms existing HRL methods, achieving up to 17% improvement in SC while strictly maintaining security constraints. These results validate the effectiveness of the proposed approach for enhancing PLS in next-generation cooperative wireless networks. Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article

The acceleration of urbanization and the intensification of climate change have highlighted the vulnerability of the single-source water supply model. Multi-source water supply has become the core path to alleviate the contradiction between urban water supply and demand and enhance the stability of water supply. To scientifically control the water resource security level of multi-source water supply cities and predict the development trend, Jinan, a typical multi-source water supply city, was taken as an example. Based on the vitality–organizational capacity–resilience model framework, which focuses on the inherent resilience and health of the system itself and can simultaneously characterize the dual characteristics of the natural base and social disturbances of the water resource system, a water resource security evaluation system was constructed from three dimensions: system vitality, organizational capacity and resilience. The combined weights were determined by comprehensively applying the projection pursuit method and the CRITIC method. The gray clustering method was used to evaluate the water resource security status from 2015 to 2023. The results showed that the water resource security level in Jinan had shown a significant improvement trend from fluctuating and unstable to high-quality and stable. The driving mechanism had gradually shifted from relying on natural endowments and single water-saving control in the early stage to a highly coordinated “vitality–organizational capacity–resilience” model within the system. The evaluation system constructed in this paper could provide technical support for multi-source water supply cities to optimize water source allocation strategies and improve water resource emergency management mechanisms, and also offer a reference model for similar cities to conduct research on water resource security control. Full article

Personalized itinerary planning for large-scale passengers under resource constraints is a critical challenge in enhancing the operational efficiency and service quality of cruise tourism. Traditional clustering methods, which primarily rely on geometric similarity, often fail to address the intricate coupling between passenger preferences and finite venue capacities, lacking predictive capability for the ultimate planning quality. To overcome these limitations, this study proposes a novel bio-inspired data-driven hybrid optimization framework for the cruise itinerary planning task unit partition. The framework innovatively integrates a Genetic Balanced Clustering Algorithm (GBCA) for multi-objective passenger grouping, Kernel Principal Component Analysis (KPCA) for feature extraction from preference data, an improved Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) for hyperparameter optimization, and a Kernel Extreme Learning Machine (KELM) for data-driven prediction of itinerary planning quality. This synergy enables the framework to dynamically allocate venue capacities based on group preferences and optimize partitioning towards maximizing overall benefits, ensuring load balance and fairness. Extensive experiments on simulated cruise scenarios demonstrate that the proposed framework significantly outperforms conventional methods, improving segmentation quality by at least 40% while exhibiting superior convergence speed and stability. This work provides a scalable, intelligent solution for complex resource-constrained scheduling problems, showcasing the effective application of bio-inspired data-driven methodologies in engineering optimization. Full article

Driven by the global mandates of the United Nations Sustainable Development Goals (particularly SDG 13 and SDG 17) and the climate targets established at COP summits, China strives to achieve its carbon peaking target by 2030 but faces significant challenges due to substantial regional disparities in abatement capacities. This paper proposes a cost-based inter-provincial cooperation mechanism to optimize carbon emission reduction (CER) task allocation. Using a marginal abatement cost curve model, we simulate provincial CER tasks from 2020 to 2030 under various cooperation scenarios. The results indicate that: (1) Cooperation significantly reduces the national total abatement cost compared to independent implementation. Specifically, the cost-saving ratio can reach approximately 60–70% when the cooperation proportion is high (80%). (2) There is a trade-off between economic efficiency and regional peaking targets. While an 80% cooperation proportion is economically optimal for 2020–2028, and a 60% proportion for 2029–2030, a 40% cooperation proportion is ultimately recommended as the balanced optimal ratio to ensure that most provinces achieve their carbon peaks before 2030. (3) The mechanism effectively narrows the disparity in abatement costs across regions. By offering a scalable paradigm for inter-regional climate collaboration, this study provides a theoretical basis for designing differentiated cooperation strategies to fulfill COP commitments and advance the global SDGs. Full article

This paper proposes a joint optimization algorithm based on reinforcement learning to address the time slot and power allocation problem in underwater acoustic communication networks (UACNs). By maximizing the total capacity of successful transmissions as the optimization objective, two sub-objectives are formulated corresponding to time-slot scheduling and power allocation. The sub-objective corresponding to time-slot scheduling is addressed by constructing a Markov Decision Process (MDP) model based on Deep Q-Network (DQN) learning. In this model, the agent learns the time slot allocation policy with the goal of increasing the number of successfully transmitted links while reducing the collision. For the sub-objective corresponding to power allocation, another MDP model is developed, solved by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, in which each underwater transmission node acts as an independent agent. The MADDPG approach enables the system to improve channel capacity under energy limitation, which maximizes the total capacity of successfully transmitted links. In terms of model execution, the DQN adopts a centralized training and time slot allocation, while MADDPG uses a centralized training and distributed execution to select the transmission power by each node. Simulation results show that the proposed joint optimization algorithm demonstrates better performance in the number of successfully transmitted links and channel capacity compared to TDMA, Slotted ALOHA, and other algorithms. Full article

Water resources along mountain railway corridors are characterized by limited availability, high ecological sensitivity, and stringent water quality requirements. These factors render them susceptible to degradation and wastage during construction. To address these challenges, in this study, an integrated dynamic allocation model considering water quantity and quality along mountainous railway sections is developed. The model is established on the basis of a joint allocation framework that considers both water volume and quality parameters holistically. Constrained by sectoral water consumption quotas, the assimilative capacity of water functional zones, and graded water supply standards, the objectives are to maximize the comprehensive benefits of water resource utilization and promote water savings. A gray wolf optimizer (GWO) algorithm is employed to identify high-quality solutions. The model is applied to a case study of water resource allocation in a specific section of a mountain railway. The results indicate that in both the pre- and postoptimization scenarios, total water consumption and water quality are maintained within permissible limits. After optimization, the comprehensive water use efficiency increased by 12.39%, the daily costs decreased by 81.34 USD, and water savings increased by 23.19%. The optimized allocation strategy alleviates water scarcity along the railway corridor, enhances overall water resource efficiency, and provides a reference for addressing quality-induced water shortages in mountainous regions. Full article