Search Results (34,414)

Accurately quantifying the sensitivity of alpine vegetation to climate change is a key prerequisite for formulating regional climate change adaptation policies. The sensitivity of the fragile alpine grasslands on the Tibetan Plateau to climate change has received widespread attention. However, the spatiotemporal dynamics and driving mechanisms of this sensitivity are still unclear under continuous warming and wetting. This study, based on MODIS_NDVI and meteorological data from 2000 to 2023, constructed a dynamic Vegetation Sensitivity Index (VSI) framework and integrated Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models with Shapley Additive exPlanations (SHAP) attribution analysis to reveal the spatiotemporal evolution characteristics and driving mechanisms of vegetation sensitivity on the Tibetan Plateau. The results show that (1) the VSI of alpine grasslands exhibited a spatial pattern of higher values in the southwest and lower values in the northeast, with an overall upward trend. Specifically, 56.31% of the region showed an increase in the VSI, with the upward trend being more pronounced in the northern plateau. (2) The dominant role of different climate factors varied regionally; vegetation sensitivity to precipitation increased in the northern plateau, and temperature sensitivity decreased in the central plateau, while sensitivity to solar radiation significantly increased in the central plateau. (3) SHAP attribution analysis indicated that elevation was the core factor driving VSI differentiation, showing a higher sensitivity at higher elevations, with lower growth rates. These findings reveal the dynamic evolution of vegetation sensitivity under the warming and wetting climate trend and its elevation-regulated mechanism, providing important scientific insights for regional ecological adaptation management. Full article

Understanding the dynamic characteristics of droplets in the orientated flow channels of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for their effective heat and water management and bipolar plate design. Therefore, the transient transport dynamics of liquid water within orientated gas flow channels (OGFCs) of PEMFCs are investigated, and a two-phase model based on the volume of fluid (VOF) method is established in the current study. Moreover, the impacts of the size of droplets and the geometrical parameters of baffles on the removal dynamics of liquid water are examined. The results show that baffles effectively promote droplet breakup and accelerate their detachment from the Gas Diffusion Layer (GDL) surface by increasing flow instability and local shear forces. The morphology of water is altered by the high velocity of gaseous flow, which can break up into several smaller droplets and distribute them on the surface of GDL by the gas flow. The shape of the liquid water film changes from a regular cuboid to a big droplet due to the surface tension of the liquid water droplets and the hydrophobicity of the GDL surfaces. Increasing the baffle height can reduce the time needed for the removal of droplets. With the increase in L1* from 0.25 to 0.75, the drainage time decreases slightly; however, for L1* increasing from 0.75 to 1.25, the drainage time remains almost the same. The impacts of different leeward lengths, L2*, on the water coverage ratio and pressure drop are minor. Full article

The technological attributes of urban digital infrastructure (UDI) are transforming labor skill structures in the market, thereby altering changes in skill premiums. This study investigates the relationship between UDI and skill premiums by developing a theoretical model that incorporates both digital and material capital. Using data from the China Labor Force Dynamic Survey and urban statistics, we examine the underlying mechanisms. The findings indicate that UDI exhibits skill-biased technological attributes, thereby increasing the skill premium. UDI development raises the demand for high-skilled labor across both skill-intensive and non-skill-intensive industries, altering the labor skill structure and consequently elevating the skill premium. This effect stems from the complementarity between UDI-related digital capital and high-skilled labor. Compared to material capital, deepening digital capital enables high-skilled labor to contribute more significantly to output. Full article

Plant immunity research is being reshaped by integrative multi-omics approaches that connect transcriptomic, proteomic, and interactomic data to build systems-level views of plant–pathogen interactions. This review outlines the scope and methodological landscape of these approaches, with particular emphasis on how transcriptomic and proteomic insights converge through network-based analyses to elucidate defense regulation. Transcriptomics captures infection-induced transcriptional reprogramming, while proteomics reveals protein abundance changes, post-translational modifications, and signaling dynamics essential for immune activation. Network-driven computational frameworks including iOmicsPASS, WGCNA, and DIABLO enable the identification of regulatory modules, hub genes, and concordant or discordant molecular patterns that structure plant defense responses. Interactomic techniques such as yeast two-hybrid screening and affinity purification–mass spectrometry further map host–pathogen protein–protein interactions, highlighting key immune nodes such as receptor-like kinases, R proteins, and effector-targeted complexes. Recent advances in machine learning and gene regulatory network modeling enhance the predictive interpretation of transcription–translation relationships, especially under combined or fluctuating stress conditions. By synthesizing these developments, this review clarifies how integrative multi-omics and network-based frameworks deepen understanding of the architecture and coordination of plant immune networks and support the identification of molecular targets for engineering durable pathogen resistance. Full article

Thermosensitive nanogels undergo a volume phase transition in response to temperature changes, making them promising candidates for applications, such as water pollutant remediation and drug delivery. In this study, we investigated the thermosensitive volume phase transition of a neutral poly(N-isopropylacrylamide) (PNIPAM) nanogel using coarse-grained dissipative particle dynamics (DPD) simulations conducted using ESPResSo software with varying bead volumes. Langevin dynamics simulations were employed to compare the results. In DPD simulations, water is explicitly treated, whereas in Langevin dynamics, it is treated implicitly, and hydrophobic interactions are represented by an attractive potential between monomer beads. Our results, including the radius of gyration and various radial distribution functions, revealed a clear volume phase transition as the temperature varied, transitioning from an expanded state to a collapsed state. Notably, the volume phase transition observed in Langevin simulations is attributed to the attractive potential between the PNIPAM monomers, whereas in the DPD simulations, it arises from implicit hydrophobic interactions, obviating the need for an additional attractive potential between the monomer beads. This implicit hydrophobic effect originates from the temperature dependence of the Flory–Huggins interaction parameter. Full article

Kazakhstan, located in Central Asia, is experiencing faster warming than the global trend, making it an important region regarding the study of how climate change is affecting climatic zones. This research aims to identify projected shifts in Köppen–Geiger climate zones under high-emission Shared Socioeconomic Pathway (SSP) 5-8.5 climate scenarios. The Köppen–Geiger climate classification system is a practical tool that effectively captures climate types based on just two variables: temperature and precipitation. Monthly temperature and precipitation data from Climatic Research Unit (CRU,) ERA5-Land, and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles from 1951 to 2100 were used to generate climatic zone maps. CMIP6 models were evaluated against meteorological station data and ERA5-Land, with bias metrics used to identify the best-performing models for temperature and precipitation in Kazakhstan. Based on these results, two inter-model datasets were developed and used to generate Köppen–Geiger climate maps for high-emission scenarios for the 2061–2100 time period. This research resulted in two key outcomes. First, to facilitate this analysis, a Google Earth Engine (GEE) application was developed as an open accessible tool for dynamic visualization of Köppen–Geiger climate maps. Second, projected maps based on CMIP6 SSP5-8.5 scenario projections indicate that southern Kazakhstan may shift to BSh (Hot Semi-Arid) and Csa (Mediterranean) climates, and the southwest region of the country is projected to shift to a BWh (Hot Desert) climate. These projected Köppen–Geiger climate maps contributed to climate adaptation efforts by identifying regions at risk of desertification and aridification. This study provides a comprehensive analysis of climate zone transformations in Kazakhstan and offers a practical scalable geovisualization tool for monitoring climate change impacts. This allows users easy access to climate-related information and insights into data processing procedures. Full article

Traditional vehicle routing problems focus primarily on cost minimization. This paper addresses the unbalanced transportation problem, aiming to minimize both costs and carbon emissions. We propose a Cost–Carbon Emissions Adaptive Genetic Algorithm (CSC-AGA) based on the Cost–Carbon Synergy (CSC) mechanism, which quantifies the marginal cost of carbon emission reduction by comparing intergenerational changes in cost and emissions. This mechanism enables dynamic adjustment of penalty coefficients during the evolutionary process. The algorithm adapts penalty coefficients and search parameters to optimize both objectives within a single framework. Experimental results demonstrate that the proposed algorithm outperforms traditional approaches in both cost control and emission reduction, while also approximating or surpassing the approximate Pareto front of existing multi-objective methods with better computational efficiency. The Generalized Unbalanced Transportation Problem (G-UTP) is an NP-hard optimization problem, inheriting the complexity of classical transportation problems while also balancing economic and environmental objectives. Full article

This paper investigates how Toronto’s Portuguese-Azorean community has shaped the city’s multicultural and psychological landscape, focusing particularly on intergenerational experiences of trauma among immigrant youth. Framed within North America’s broader migration dynamics, the study explores the creation and transformation of the ethnic enclave “Little Portugal” as both a space of cultural resilience and chronic urban stress. It introduces the concept of chronic urban trauma to describe the persistent psychosocial impact of displacement, assimilation pressures, and gentrification on young Portuguese-Azorean Canadians. While first-generation immigrants constructed cohesive ethnic infrastructures grounded in work, faith, and language, younger generations face cultural dissonance, linguistic loss, and identity fragmentation that manifest as emotional distress and social alienation. These experiences illustrate how structural urban change can perpetuate transgenerational trauma within immigrant families. By integrating perspectives from urban geography, trauma studies, and migration theory, this theoretical work underscores the need for trauma-informed educational and social policies that promote inclusion, belonging, and mental well-being among immigrant youth. Ultimately, the study positions “Little Portugal” as a microcosm of how multicultural cities negotiate the intersections of ethnicity, urban transformation, and psychological resilience. Full article

Long underground expressways have emerged as an alternative to surface highways in densely urbanized areas; however, their enclosed geometry, extended length, and steep longitudinal gradients introduce traffic-flow dynamics distinct from those of surface roads. This study investigates the combined and interaction effects of traffic volume, heavy-vehicle ratio, longitudinal gradient, lane number, and lane-changing policy on traffic performance in long underground expressways using microscopic traffic simulation. A hypothetical 20 km underground expressway network was evaluated under 72 systematically designed scenarios. Weighted average speed and throughput were analyzed using nonparametric statistics, generalized linear models with interaction terms, and machine learning-based sensitivity analysis. While traffic volume and heavy-vehicle ratio were confirmed as dominant determinants of performance, a key contribution of this study is the identification of the density-dependent role of lane-changing policies. Under moderate traffic density, permissive lane-changing improves efficiency by enabling vehicles to bypass localized disturbances caused by heavy vehicles and longitudinal gradients, thereby enhancing capacity utilization. In contrast, under high-density conditions, permissive lane-changing amplifies lane-change conflicts and shockwave propagation within the confined underground environment, accelerating traffic instability and performance breakdown. These adverse effects are further intensified by steep uphill gradients. The findings demonstrate that lane-changing policies on long underground expressways should be designed in a context-sensitive manner, balancing efficiency and stability across traffic states. Full article

The rapid integration of unmanned aerial vehicles (UAVs) into next-generation wireless systems demands seamless and reliable handover (HO) mechanisms to ensure continuous connectivity. However, frequent topology changes, high mobility, and dynamic channel variations make traditional HO schemes inadequate for UAV-assisted 6G networks. This paper presents a comprehensive review of existing HO optimization studies, emphasizing artificial intelligence (AI) and machine learning (ML) approaches as enablers of intelligent mobility management. The surveyed works are categorized into three main scenarios: non-UAV HOs, UAVs acting as aerial base stations, and UAVs operating as user equipment, each examined under traditional rule-based and AI/ML-based paradigms. Comparative insights reveal that while conventional methods remain effective for static or low-mobility environments, AI- and ML-driven approaches significantly enhance adaptability, prediction accuracy, and overall network robustness. Emerging techniques such as deep reinforcement learning and federated learning (FL) demonstrate strong potential for proactive, scalable, and energy-efficient HO decisions in future 6G ecosystems. The paper concludes by outlining key open issues and identifying future directions toward hybrid, distributed, and context-aware learning frameworks for resilient UAV-enabled HO management. Full article

The construction of reservoirs has undeniably provided numerous conveniences and benefits to human societies. However, it has also markedly altered downstream flow regimes, leading to essential fish habitat loss that directly undermines the ecosystem services provided by fish populations, thereby jeopardizing the long-term sustainability of fishery resources. Existing assessments of spawning suitability largely concentrate on static characteristics of available spawning grounds, while the dynamics of habitat suitability migration and contraction in response to changing environmental flows remain poorly understood. To address this gap, we classified hydrological years into wet, flat, and dry categories to reflect the varying environmental flow requirements during the fish-spawning period. Using the Mike21 hydraulic model together with a spatial suitability analysis for spawning habitats, we quantified spawning ground suitability from both temporal and spatial perspectives. Taking the four major Chinese carps (FMCC) and the Dongta spawning ground in the Pearl River as a case study, our findings reveal that the proportion of highly suitable habitats closely tracks the environmental-flow trajectories. Throughout the FMCC spawning period, the spatial pattern of high suitability zones undergoes a marked migration in response to flow variations across wet, flat, and dry years, consistently shifting upstream. Specifically, as discharge rises from low-flow to high-flow events, the most suitable areas move from downstream deep-pool sections toward upstream shallow riffle zones, which is crucial for the sustainable development of fishery resources. Full article

Understanding how avian assemblages respond to seasonal dynamics within urban land-cover structure is crucial for biodiversity conservation in rapidly urbanizing environments. Here, we investigated seasonal variation in avian dietary and foraging location guilds in central Taizhou City, China. Field surveys were conducted using the line transect method from April to November 2024. We assessed seasonal changes in community composition and the relationships between bird guilds and land cover types using multi-response permutation procedure (MRPP), non-metric multidimensional scaling (NMDS), and fourth-corner analysis. Bird community composition exhibited significant seasonal variations (MRPP, p < 0.05), with NMDS ordination showing a clear seasonal separation. Foraging location guilds exhibited more pronounced seasonal fluctuations in individual abundance than the dietary guilds. The Shannon diversity index for dietary guilds peaked in spring, followed by summer and autumn, whereas foraging location guilds exhibited higher diversity in summer and autumn. Fourth-corner analysis identified significant associations between guilds and land cover types, with foraging location guilds demonstrating stronger and more consistent responses to habitat structure than dietary guilds. Together, these results indicate that in urban landscapes, the spatial arrangement of habitats may shape avian foraging behavior more strongly than food availability alone, highlighting the need to integrate both structural and resource-based habitat features into urban planning and conservation. Full article

Climate change and altered hydrological regimes are restructuring wetland habitats globally, triggering cascading effects on colonial waterbirds. This study investigates how environmental drivers, including thermal anomalies, water-level fluctuations, and aqueous surface extent, influence the distribution and size of waterbird colonies (Ardeidae, Threskiornithidae, and Phalacrocoracidae) in the Danube Delta Biosphere Reserve. We integrated colony census data (2016–2023) with remote-sensing-derived habitat metrics, in situ meteorological and hydrological measurements to model colony abundance dynamics. Our results indicate that elevated early spring temperatures and water level variability are the primary determinants of numerical population dynamics. Spatial analysis revealed a heterogeneous response to hydrological stress: while the westernmost colony exhibited high site fidelity due to its proximity to persistent aquatic surfaces, the central colonies suffered severe declines or local extirpation during extreme drought periods (2020–2022). A discernible eastward shift in bird assemblages was observed toward zones with superior hydrological connectivity and proximity to anthropogenic hubs, suggesting an adaptive spatial response that was consistent with behavioral flexibility. We propose an adaptive management framework prioritizing sustainable solutions for maintaining minimum lacustrine water levels to preserve critical foraging zones. This integrative framework highlights the pivotal role of remote sensing in transitioning from reactive monitoring to predictive conservation of deltaic ecosystems. Full article

Antimicrobial resistance (AMR) in hospital wastewater is a recognized public health concern, yet the dynamics of its emergence remain poorly understood. This study aimed to characterize the quantitative and qualitative changes in the microbial community of a newly built internal medicine intensive care hospital wing following the start of patient treatment. Wastewater samples were collected regularly from eight relevant sites, including seven patient-associated locations within the intensive care ward and the central sanitary sewer where all effluent converged. Culture-based analyses targeted the “ESCAPE-SO” bacterial and fungal groups (“Enterococci”, “Staphylococci”, “Candida”, “Acinetobacter”, “Pseudomonas”, “Enterobacteriaceae”, “Stenotrophomonas”, “Others”). Comparisons were made between a 12-month pre-operation period (only flushing every 72 h to prevent contamination of the drinking water system) and the first 12 months of patient treatment. The results showed a significant increase in mean bacterial concentrations from 53 [0–349] CFU/mL before patient treatment to 8423 [3054–79,490] CFU/mL during patient treatment (p = 0.0224) with a particular focus on Pseudomonas spp. as the dominant genus. Resistance against all four main antibiotic classes of the WHO AWaRe essential “watch” list (carbapenems, third-generation cephalosporins, broad-spectrum penicillin and ciprofloxacin) emerged within the first twelve months and depended on the amount of prescribed antibiotics and the number of patients treated. These findings indicate that hospital activity drives rapid development of antimicrobial resistance in wastewater microbial communities, highlighting the critical role of clinical antibiotic use in shaping environmental resistomes. This study provides quantitative evidence that resistance can emerge within months of hospital operation, emphasizing the need for early monitoring and targeted interventions to mitigate the spread of AMR from hospital effluents into broader environmental systems. Full article

Landslides remain a critical geohazard in mountainous regions, where intensified extreme rainfall and rapid land-use changes exacerbate slope instability, challenging the reliability of traditional single-sensor susceptibility assessments. To overcome the limitations of data heterogeneity and noise, this study presents a decision-level fusion strategy integrating Permanent Scatterer InSAR (PS-InSAR) deformation dynamics with multi-source optical remote sensing indicators via a Wavelet Transform (WT) enhanced Multi-source Additive Model Based on Bayesian Analysis (MAMBA). San Martín del Castañar (Spain), a region characterized by rugged terrain and active deformation, served as the study area. We utilized Sentinel-1A C-band datasets (January 2020–February 2025) as the primary source for continuous monitoring, complemented by L-band ALOS-2 observations to ensure coherence in vegetated zones, yielding 24,102 high-quality persistent scatterers. The WT-based multi-scale enhancement improved the signal-to-noise ratio by 23.5% and increased deformation anomaly detection by 18.7% across 24,102 validated persistent scatterers. Bayesian fusion within MAMBA produced high-resolution susceptibility maps, indicating that very-high and high susceptibility zones occupy 24.0% of the study area while capturing 84.5% of the inventoried landslides. Quantitative validation against 1247 landslide events (2020–2025) achieved an AUC of 0.912, an overall accuracy of 87.3%, and a recall of 84.5%, outperforming Random Forest, Logistic Regression, and Frequency Ratio models by 6.8%, 10.8%, and 14.3%, respectively (p < 0.001). Statistical analysis further demonstrates a strong geo-ecological coupling, with landslide susceptibility significantly correlated with ecological vulnerability (r = 0.72, p < 0.01), while SHapley Additive exPlanations identify land-use type, rainfall, and slope as the dominant controlling factors. Full article