Search Results (14,039)

Flash droughts, characterized by rapid onset and intensification, are occurring more frequently under global warming. Accurately identifying the frequency and hazard severity of flash droughts remains challenging, as they are influenced by multiple hydroclimatic drivers, including precipitation deficits, temperature increases, and soil moisture depletion. We developed a daily-scale Flash Drought Hazard Index (FDHI) by integrating the interactive effects of multiple driving factors, aiming to assess the spatiotemporal patterns of flash drought hazard in the Black Soil Region of Northeast China during the period 2000–2020. The FDHI employs the daily Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, Standardized Soil Temperature Index, and Standardized Runoff Index to characterize short-term anomalies in multiple hydrometeorological variables. Results showed that flash droughts occurred most frequently in the southern part of the Black Soil Region of Northeast China, particularly in the Songnen Plain and the Liaohe Plain, with annual frequencies of 5.98 and 5.80 events, respectively. Flash drought severity in the Liaohe Plain exhibited a significant increasing trend during the past decade. Moreover, the dominant driving factors varied substantially among regions. Flash droughts in the Liaohe Plain were mainly associated with precipitation deficits and enhanced evapotranspiration, whereas soil moisture depletion and temperature anomalies played a more important role in the Songnen Plain. These results reveal pronounced regional heterogeneity in flash drought mechanisms across the Black Soil Region of Northeast China and demonstrate the effectiveness of the proposed FDHI for daily-scale agricultural flash drought monitoring. The study provides scientific support for agricultural drought risk management and disaster mitigation under climate change. Full article

Actual evapotranspiration is a primary pathway for crop water consumption in irrigation districts, including the Shijin irrigation district, where understanding the impacts of irrigation is crucial for managing water resources in this large-scale, water-scarce region. However, existing studies on evapotranspiration driving mechanisms often overlook irrigation activities and lack an analysis of synergistic effects among different environmental factors, with such research remaining particularly limited for this area. This study investigates the synergistic impact mechanisms of multiple drivers on evapotranspiration. Using data from 2003 to 2017, a projection pursuit model was employed to quantitatively assess the contributions of meteorological factors, Leaf Area Index, and irrigation to evapotranspiration evolution. The results indicate a significant structural shift in evapotranspiration, and the reduction in soil evaporation plays an important role in driving the variation of total evapotranspiration. Among the various factors, Leaf Area Index and irrigation exhibited the highest contribution rates to evapotranspiration. Furthermore, irrigation primarily acts in synergy with crop growth to enhance evapotranspiration. This study provides critical scientific insights for evidence-based water resource management and policy optimization in the Shijin irrigation district. Full article

Stray-current corrosion from DC-electrified railways drives premature failure of buried metallic infrastructure (pipelines, foundations, tunnel reinforcement), causing resource waste, repair-driven carbon emissions and service disruptions that undermine the sustainability of urban transit corridors. Conventional impressed-current cathodic protection (ICCP) design relies on uniform-anode rules of thumb or closed commercial codes that cannot quantify the trade-off between protection uniformity, energy use and hardware cost. We present an open MATLAB framework that couples a custom 3D finite element method (FEM) solver with multi-objective particle swarm optimisation (MOPSO) and minimises three competing objectives simultaneously: total impressed current, RMS deviation from the protection target, and number of active anodes. A laboratory-calibrated coupling factor ($\mathrm{CF}=1.98$, consistent with the image-method prediction of 2 for a highly conductive pipe inclusion) absorbs the pipe–soil interface kinetics into a single direct FEM solve, and a pre-computed Green’s-function basis accelerates each MOPSO evaluation by more than two orders of magnitude. The solver is validated against an instrumented prototype with RMSE $=14.9$ mV across ten Cu/CuSO${}_4$ saturated reference electrode (CSE) measurements, and applied to a 500 m DC traction line. At an identical total current of 20.30 A across five anodes, the optimised design achieves an RMSE of 86.6 mV against the $-850$ mV NACE target, whereas a conventional uniform layout produces severe over-protection (RMSE $=1107$ mV)—a twelve-fold reduction. The framework is recommended as a transparent, reproducible engineering tool that simultaneously extends pipeline service life and reduces rectifier energy demand, supporting UN Sustainable Development Goals 9 and 11 for sustainable urban-rail infrastructure. Full article

Rural sustainable development is a core component of the global Sustainable Development Goals, and building digital villages and enhancing the resilience of rural economies are key pathways for underdeveloped regions to achieve rural sustainable development. The coordination and synergy between these two areas are central to rural revitalization. Taking 122 counties in Hunan Province as research units and using 2013–2023 spatial panel data, this study employs an improved coupling coordination model, spatial autocorrelation analysis and geographically weighted regression to explore their spatiotemporal evolution, clustering patterns and driving factors. The results show that both systems improved steadily: digital villages expanded from core areas, while economic resilience developed more balancedly. The coupling coordination evolved from near-disorder to a pattern characterized by regional equilibrium. The coupling coordination degree displayed significant positive spatial autocorrelation, forming an “High-High (H-H)” cluster in the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain and an “Low-Low (L-L)” cluster in western Hunan. Driving factors showed marked spatial heterogeneity. These findings provide empirical support for differentiated digital village policies in Hunan. Full article

Ecological interactions are complex and influenced by historical, ecological, and anthropogenic factors. In mutualistic networks, extrafloral nectaries (EFNs) drive ant–plant interactions, and network structure depends on the ecological flexibility and degree of generalization of the species involved. We evaluated whether plant and ant diversity and topological descriptors, at both network and plant-species levels, differ between networks with and without EFNs and between conservation levels of Neotropical savannas, considering total ants (arboreal and non-arboreal) and only arboreal ants. We sampled six remnants of Neotropical savannas (cerrado sensu stricto) in the Brazilian Cerrado, three preserved and three disturbed. In total, we analyzed 24 interaction networks, involving 45 plant species, 51 ant species, and 358 distinct interactions. Plants without EFNs were richer and more abundant, and nestedness was the only descriptor that varied, being higher in preserved areas (for total ants) and in networks with EFNs (for arboreal ants). In addition, EFN-bearing species showed higher degree, betweenness centrality and closeness centrality. EFN-mediated interactions play a stabilizing role in ant–plant networks, particularly in preserved areas, and maintaining EFN-bearing plant species may promote interaction redundancy and functional resilience in human-impacted savannas. Full article

Tourism drives economic growth but also intensifies environmental pressure at travel destinations, particularly by exacerbating local challenges in waste management. Rather than merely testing the theoretical validity of the norm activation model (NAM), this study utilizes its key constructs—specifically moral and accountability variables—as a strategic framework to examine the psychological drivers of waste reduction in the urban context of Vientiane, Laos. Data from 382 domestic tourists were analyzed using ordinary least squares regression. Ascription of responsibility (AR) (β = 0.219, p < 0.001) was the strongest predictor of intention, followed by personal norm (PN) (β = 0.173, p < 0.01) and actual waste management behavior (β = 0.160, p < 0.01). Notably, environmental knowledge and awareness of consequences—factors often emphasized in traditional environmental campaigns—had no significant influence. The findings demonstrate that, in addressing urban waste challenges in developing regions, fostering internalized moral sentiments (AR and PN) is far more effective than mere pro-environmental education. This study concludes that sustainable waste management may benefit from operationalized interventions that activate personal accountability rather than relying solely on general environmental awareness. Full article

Reducing carbon emissions while improving air quality is a central challenge for rapidly urbanizing regions. Focusing on 31 prefecture-level cities in the Middle Reaches of the Yangtze River Urban Agglomeration, this study examines carbon–pollution synergy (CPS), spatial dynamics, and the driving factors of CO₂ and representative air pollutants from 2013 to 2023. Spatial autocorrelation analysis, a revised four-factor Logarithmic Mean Divisia Index (LMDI) decomposition, and a factor-based CPS assessment were used to identify spatial clustering, compare driver heterogeneity, and evaluate coordination between CO₂ and primary pollutants. To improve methodological consistency, the LMDI decomposition and CPS assessment focus on the primary pollutants SO₂, CO, and NO₂, whereas PM_2.5 and O₃ are retained in the spatial analysis and discussion because they are strongly affected by secondary formation, atmospheric transport, and meteorological conditions. The results show that CO₂ and the selected pollutants exhibit significant but pollutant-specific spatial clustering. High CO₂ values remain concentrated in the core cities of Wuhan, Changsha, and Nanchang, PM_2.5 shows a persistent north–south gradient, and SO₂ hotspots shift from traditional industrial cores toward peripheral areas receiving industrial relocation. The revised LMDI results show that economic development is the most stable positive driver of CO₂ and the primary pollutants, whereas the energy-consumption factor generally suppresses emissions. The recalculated population-scale factor fluctuates around 1, indicating a comparatively limited and stage-dependent contribution once the other factors are controlled for. CPS analysis further indicates that coordinated reduction is most robust under the energy-consumption factor and, for conventional combustion-related pollutants, also under the energy-structure factor. Overall, the region has a clear basis for CPS governance, but effective implementation requires pollutant-specific and region-specific control strategies rather than a uniform co-mitigation pathway. Full article

To address the challenge of accurately reconstructing multi-depth targets using single-photon Light Detection and Ranging (LiDAR) under few-frame conditions in high-precision applications such as autonomous driving perception, remote sensing, and military reconnaissance, this paper proposes a computational imaging method named the Jensen–Shannon Divergence Weighted Pixel Fusion Constant False Alarm Rate (JSWPF-CFAR) approach. First, the proposed method utilizes the Jensen–Shannon (JS) divergence to characterize the statistical similarity between adjacent pixels, thereby constructing adaptive weights to achieve the effective fusion of echo signals. The key innovation lies in the formulation of a JS divergence-based weighting factor, which fully exploits the inherent spatial correlation within 3D target structures to optimize the pixel fusion process and enhance the signal statistics of target echoes. Subsequently, a CFAR detection model tailored for Geiger-mode Avalanche Photodiode (GM-APD) multi-depth echo signals is constructed to estimate the noise photon count within a local sliding window; this estimate is then used to calculate a photon counting threshold for identifying and extracting high-confidence target intervals. Finally, a peak-picking method is employed to perform the 3D reconstruction of multi-depth targets. Compared with existing techniques such as matched filtering and Reversible Jump Markov Chain Monte Carlo (RJMCMC), the proposed method exhibits superior reconstruction quality under few-frame and low Signal-to-Background Ratio (SBR) conditions. The experimental results demonstrate that the proposed method achieves an improvement in target restoration degree (RD) of at least 21.16% and a relative variance (Var) optimization of at least 62.90% over the matched filtering and RJMCMC baselines. These results indicate that the proposed approach effectively enhances the multi-depth estimation performance of single-photon LiDAR in complex scenes. Full article

Accurate carbon price forecasting is critical for trading decisions, risk management and policy formulation in carbon markets. However, mainstream decomposition-ensemble models suffer from two key drawbacks: point-wise modeling fails to capture long-term temporal dependencies, while independent modeling of decomposed trend and seasonal components leads to serious information loss. To address these limitations, this paper proposes a novel Dual-Path Interactive Attention Network (DPIANet) for carbon price time series forecasting, whose dual-parallel architecture consists of a Dual Interaction Attention (DIA) Block and a Decomposition–Subsequence Interaction Attention (DSIA) Block. First, DPIANet employs a patch-wise partitioning strategy to extract local temporal semantic information inaccessible to traditional point-wise segmentation. The DIA Block jointly captures temporal dependencies between different patches within the same sequence and inter-feature dependencies within the same time step. In parallel, the DSIA Block extracts interactive features between decomposed trend and seasonal subsequences, fusing these features with original subsequences to enhance representation and mitigate decomposition-induced information loss. A dual-layer feature selection method (PMI and XGBoost-SHAP) is adopted to identify key driving factors. Experiments on four representative Chinese regional carbon trading markets covering 2014-2020 show that DPIANet achieves superior prediction performance over state-of-the-art models in terms of MSE and MAE, with competitive robustness across different market characteristics, providing practical decision support for carbon market stakeholders. Full article

Ecological carrying capacity (ECC) is a vital indicator for regional sustainable development, reflecting an ecosystem’s support for human activities while maintaining core functions. Research on ECC has largely focused on static assessment, while exploration of dynamic prediction has been relatively limited. This study constructed a comprehensive evaluation system using the AHP-EW model with multidimensional indicators and developed a CAXO hybrid model for multi-scenario ECC projection of China. ECC patterns were classified into five levels, with SHAP and LIME adopted to interpret ECC changes. The results show that China’s ECC exhibits a “high in the southeast and low in the northwest” spatial pattern and has improved continuously from 2000 to 2020, with the proportion of Level V areas increasing from 10.86% to 14.61%. Significant regional disparities exist, with more favorable ECC conditions east of the Hu Huanyong Line and poorer conditions in the west. The CAXO model achieves reliable performance (OA = 90.01%, Kappa = 87.11%) and outperforms traditional models. SHAP analysis identifies NDVI (2.17) as the most critical driving factor, followed by soil moisture (0.53) and precipitation (0.52), while LIME reveals heterogeneous factor contributions across ECC levels. Northwestern China faces severe ecological constraints (Level I: 53.96%), whereas eastern China exhibits the optimal ECC status (Level V: 70.07%). Multi-scenario projections to 2050 show that Level V areas will reach 28.22% under SSP1-2.6, Level III will account for 27.70% under SSP2-4.5, and Level I will rise to 22.44% under SSP5-8.5. The proposed ECC framework and CAXO model provide scientific support for ecological security early warning and sustainable development policy-making. Full article

A common hypoxic scenario in tumors involves unresolved acute hypoxia that eventually leads to sustained (chronic) hypoxia. This shift drives a characteristic “HIF switch”, where the key hypoxia-responsive factors change from HIF-1α to HIF-2α over time, and importantly, this switch is closely linked to stemness regulation. However, the mechanisms underlying this switch and its impact on stemness regulation are not yet fully understood. Here, we developed a mechanistic network model integrating the HIF-1/HIF-2 signaling axis with the stemness regulators OCT4 and SOX2. We found the duration and intensity of hypoxia jointly shape the dynamics of HIF-1α and HIF-2α, ultimately regulating OCT4-mediated stemness. Under physioxia, HIF-2α–mTORC2 positive feedback supports the gradual accumulation of HIF-2α toward a modest steady level and low OCT4 expression, corresponding to a primed state. Under prolonged mild hypoxia, the concurrent induction of HIF-1α, albeit at low levels, and accelerated accumulation of HIF-2α elevate OCT4 to intermediate levels, promoting stem-like traits. Under moderate hypoxia, PHD-2-mediated negative feedback triggers pulsatile HIF-1α dynamics, driving a shift toward HIF-2α dominance. Ultimately, cooperative HIF-1α/HIF-2α signaling induces REDD1 and suppresses mTORC1-dependent protein synthesis, pushing OCT4 into a high-expression state associated with differentiation. This work presents a unified framework for understanding how the HIF signaling hierarchy coordinates metabolic and transcriptional programs to direct cell fate across varying hypoxic landscapes. Full article

Endoscopic spine surgery (ESS)—including full-endoscopic transforaminal and interlaminar techniques, and unilateral biportal endoscopy (UBE)—offers patients smaller incisions, preserved paraspinal muscle, and faster recovery. Because the working corridor is narrow, intraoperative fluoroscopy plays a larger role than in open or microscopic approaches, making radiation exposure worthy of attention for both patients and surgeons. This narrative review aims to be a practical resource for the endoscopic spine surgeon. We synthesize the available literature on typical radiation doses across the main ESS techniques, compare them with minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) and open alternatives, review the factors that drive exposure, and walk through the full menu of dose-optimization options—from simple measures such as collimation, pulsed fluoroscopy, and leaded eyewear, through navigation platforms, to robotic guidance. A consistent practical observation is that the simplest, least expensive interventions often deliver the largest dose reductions. Capital-intensive technologies add real value, particularly for endoscopic interbody fusion, and work best alongside rather than in place of these basics. With routine dosimetry and straightforward as-low-as-reasonably-achievable (ALARA) practices, surgeons can continue to build on the already favourable profile of ESS while keeping radiation exposure low. Conclusions are tempered by the largely retrospective and heterogeneous nature of the underlying evidence. Full article

Ecosystem service assessment provides a critical basis for optimizing regional ecological management and promoting sustainable development. From the water–land–carbon coupling perspective, this study established a technical framework for quantifying individual services, coupling a composite index, and analyzing multidimensional driving mechanisms. The InVEST model was applied to quantify three core ecosystem services: water yield, habitat quality, and carbon storage. A Composite Ecosystem Service Index (CESI) was constructed through normalization and weighted summation. Multidimensional driving factors were identified using the Optimal Parameter-Based Geographical Detector. Taking Ningxia during 2004–2024 as the study area, the results showed that the CESI exhibited a fluctuating upward trend with significant spatial heterogeneity, characterized by a south–high and north–low pattern. Land use transitions were dominated by bidirectional conversions between cropland and grassland, while impervious area expanded rapidly and barren land decreased overall. The spatial differentiation of CESI was jointly controlled by natural and anthropogenic factors, with land use type, precipitation, and digital elevation model showing the strongest explanatory power, and all two-factor interactions displaying pronounced enhancement effects. This study provides a reproducible framework for ecosystem service assessment in arid and semi-arid regions, supporting ecological restoration, land use optimization, and the coordinated development of ecology and economy under water–land–carbon synergy. Full article

As global resource depletion and environmental challenges continue to intensify, the circular economy has emerged as a critical strategy for firms pursuing sustainable development. This study integrates the perspectives of circular economy, the resource-based view (RBV), and stakeholder theory, and incorporates a system dynamics approach to construct a causal feedback model of circular economy systems. First, through a comprehensive literature review and systems thinking, this study develops a causal loop diagram (CLD) that captures the dynamic interactions among key elements, including firms, resources, design, products, consumers, recycling, and waste, thereby illustrating the underlying mechanisms of circular economy operations. Subsequently, the CLD is transformed into a structural equation model (SEM), and empirical analysis is conducted using 134 valid questionnaire responses. The results indicate that significant and positive causal relationships exist among the constructs. In particular, resource-based design advantage is identified as the core driving factor of the system, influencing waste reduction through circular recycling and resource circulation mechanisms. Moreover, the interaction between reinforcing feedback loops and balancing feedback loops forms a dynamic equilibrium within the circular economy system. The findings not only validate the theoretical framework of circular economy systems but also provide practical implications for firms in terms of resource allocation, product design, and recycling management, thereby facilitating resource circulation and sustainable development. Full article

Ovarian cancer (OC) remains one of the most lethal gynaecological malignancies, which is mainly due to late diagnosis, high frequency of metastasis, and the risk of developing resistance to systemic therapy. In recent years, exosomes—small extracellular vesicles (EVs) secreted by cancer cells and components of the tumour microenvironment (TME)—have been identified as potential mediators of OC progression. Exosomes participate in intercellular communication and enable the transfer of RNA, proteins, and lipids. These vesicles may modulate the immune response, promote angiogenesis, remodel the extracellular matrix, and drive epithelial–mesenchymal transitions. Exosomes also appear to play a role in the development of drug resistance via direct transfer of resistance factors or indirect modification of TME. In this review article, we summarise current knowledge on the biological role of exosomes in OC pathogenesis. We also discuss their possible diagnostic, prognostic, and therapeutic relevance. The properties and composition of exosomes make them promising noninvasive liquid biomarkers and convenient carriers for anticancer drugs. However, to fully exploit their potential, further large-scale preclinical and clinical studies are required, which should focus primarily on standardising research methods and assessing the safety and efficacy of exosome-based diagnostic and therapeutic methods. Full article